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Premium Career Track - Chief Data Officer (CDO)

This program consists of courses that will help you acquire skills to aim for CDO position in a reputed organization. Learn data strategy & analytics.
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This Premium Career Track - Chief Data Officer (CDO) program by Uplatz includes the following courses:

1. Talend

2. SAP Data Services (BODS)

3. SQL Programming with MySQL Database

4. Business Intelligence Specialization

5. Power BI

6. Tableau

7. SAS BI

8. Data Visualization in Python

9. Data Visualization in R

10. Build your Career in Data Science

11. Data Science with Python

12. Data Science with R

13. Machine Learning (basic to advanced)

14. Deep Learning Foundation

15. Generative AI Specialization

16. CISSP (Cybersecurity)

17. MS Excel

18. Google Sheets

19. Cloud Computing Basics

20. SAP MDG (Master Data Governance)

 

The Chief Data Officer (CDO) is a senior executive responsible for managing and leveraging an organization's data assets to drive strategic decisions, business growth, and operational efficiency. The role of the CDO has become increasingly important with the rise of data-driven decision-making and the recognition of data as a valuable corporate asset.

 

What does a CDO do?

A Chief Data Officer supports good data operations or DataOps. DataOps is an emerging concept that takes a process-oriented, automated, and collaborative approach to design, implementing, and managing data workflows and a distributed data architecture.

A CDO oversees the collection, management, and storage of data across an organization. They are responsible for analyzing and deriving insights from data to inform business strategy and value. Chief data officers form part of a company's executive team.

 

Roles and Responsibilities of a CDO

• Overseeing data management, data analytics, and data governance

• Ensuring data quality

• Spearheading data and information strategy

• Create a data management system that facilitates the secure collection and processing of data.

• Establish a culture within your organization that normalizes sharing this data and making informed decisions on how to improve future business outcomes.

• Implement proper data analytics to identify and, hopefully, reduce pain points at all stages of the business process, increasing not only profit, but also trust in the eyes of stakeholders and clients.

Course/Topic 1 - Talend - all lectures

  • Lecture 1 - Talend Introduction

    • 15:06

  • Lecture 2 - Architecture and Installation - part 1

    • 49:38

  • Lecture 3 - Architecture and Installation - part 2

    • 54:24

  • Lecture 4 - Architecture and Installation - part 3

    • 47:31

  • Lecture 5 - File - Java - Filter Components

    • 53:39

  • Lecture 6 - tAggregateRow - tReplicate - tRunJob Components - part 1

    • 53:40

  • Lecture 7 - tAggregateRow - tReplicate - tRunJob Components - part 2

    • 06:17

  • Lecture 8 - Join Components - part 1

    • 38:00

  • Lecture 9 - Join Components - part 2

    • 19:34

  • Lecture 10 - Sort Components

    • 29:26

  • Lecture 11 - Looping Components

    • 24:19

  • Lecture 12 - Context - part 1

    • 37:48

  • Lecture 13 - Context - part 2

    • 33:37

  • Lecture 14 - Slowly Changing Dimensions (SCD)

    • 44:55

  • Lecture 15 - tMap Components - part 1

    • 31:15

  • Lecture 16 - tMap Components - part 2

    • 37:47

  • Lecture 17 - tMap Components - part 3

    • 33:42

  • Lecture 18 - tMap Components - part 4

    • 13:43

  • Lecture 19 - Talend Error Handling

    • 56:03

  • Lecture 20 - Audit Control Jobs

    • 47:55

  • Lecture 21 - How to use tJAVA components with scenario

    • 54:12

  • Lecture 22 - Talend Big Data Hadoop Introduction and Installation

    • 31:57

  • Lecture 23 - Talend HIVE Components - part 1

    • 47:34

  • Lecture 24 - Talend HIVE Components - part 2

    • 24:42

  • Lecture 25 - Talend HDFS Components

    • 57:50

  • Lecture 26 - Talend TAC

    • 30:31

Course/Topic 2 - SAP Data Services (BODS) - all lectures

  • SAP BO Data Services consists of a UI development interface, metadata repository, data connectivity to source and target system and management console for scheduling of jobs. This introductory tutorial gives a brief overview of the features of SAP BODS and how to use it in a systematic manner.

    • 1:41:26

  • In this beginner's SAP BODS tutorial, you will learn, History of SAP BODS, SAP Data Services Advantages and the disadvantages of SAP BODS.

    • 1:37:53

  • SAP BODS is an ETL tool for extracting data from disparate systems, transform data into meaningful information, and load data in a data warehouse. It is designed to deliver enterprise-class solutions for data integration, data quality, data processing and data profiling.

    • 1:44:34

  • Data Services Designer is a developer tool, which is used to create objects consisting of data mapping, transformation, and logic. It is GUI based and works as a designer for Data Services.

    • 1:39:01

  • SAP BO Data Services (BODS) is an ETL tool used for data integration, data quality, data profiling and data processing. It allows you to integrate, transform trusted data-to-data warehouse system for analytical reporting.

    • 1:45:08

  • This tutorial will help all those students who want to create their own local repository, configure a job server, start basic job development and execute the job to extract data from source systems and load the data to target systems after performing transformations, look-ups and validations.

    • 1:38:08

  • This tutorial will help all those readers who want to create their own local repository, configure a job server, start basic job development and execute the job to extract data from source systems and load the data to target systems after performing transformations, look-ups and validations.

    • 1:29:13

  • Learn SAP Business Objects Data Services from basic concepts to advanced concepts starting from introduction, architecture, data services, file formats, data loading, etc.

    • 1:49:55

  • SAP BODS (Business Object Data Services) is an SAP DWH (Data Warehouse) product, where DWH is an enterprise level centralized reporting system. Data services is an end-to-end data integration, Data management, Test analysis software.

    • 1:54:16

  • Before you start this SAP BODS tutorial, you should have a basic knowledge of SAP system, RDBMS, Data warehouse and Business Intelligence (BI).

    • 1:52:00

  • SAP Bods training tutorials as per syllabus wise so beginners can easily learn SAP Business Object Data Services (Bods) step by step with real time project scenarios.

    • 1:37:31

  • SAP BODS combines industry data quality into one platform. BODS provides a single environment for development, run-time, management, security, and data connectivity.

    • 1:27:31

  • SAP BODS is an ETL tool that delivers a single enterprise-class solution for data integration, data quality, and data profiling that permits you to integrate, transform, improve, and provide trusted data that supports important business processes and enables sound decisions.

    • 1:37:14

  • It provides a GUI that allows us to efficiently produce a job that mine data from various sources, convert that data to meet the business requirements of an organization, and load data into a single place.

    • 1:43:07

  • SAP BO Data Services (BODS) is an ETL tool used for data integration, data quality, data profiling and data processing. It allows you to integrate, transform trusted data-to-data warehouse system for analytical reporting.

    • 1:29:54

Course/Topic 3 - SQL Programming with MySQL Database - all lectures

  • In this video get an in-depth introduction to the terminology, concepts, and skills you need to understand database objects, administration, security, and management tools. Plus, explore T-SQL scripts, database queries, and data types

    • 30:09

  • In this video learn basic of SQL Programming and overview the SQL basic commands and how we use these commands in SQL Programming. This SQL tutorial will teach you basics on how to use SQL in MySQL, SQL Server, MS Access, Oracle, Sybase, Informix, Postgres, and other database systems.

    • 42:45

  • In this video we talk about DDL (DATA DEFINATION LANGUAGE) and also cover all the basic techniques of DDL.In this video we will learn about the SQL commands – DDL, DML, DCL; SQL Constraints – Keys, Not Null, Check , Default, and also MYSQL Hands-on and basic Querying

    • 33:59

  • In this video session we learn SQL commands and how to use these commands like select command, insert command, delete command etc. In this video we will learn about hands-on experience on the terminal, creating database, Tables and manipulating data.

    • 38:49

  • In this video we learn about SQL Basic and Aggregate Functions and also cover different functions of SQL. This tutorial teaches us about clauses and the update command. We will also cover making records, updating and modifying rows.

    • 37:02

  • In this session we talk about SQL Regular Expression and we also cover all techniques of SQL Regular Expression.This tutorial teaches us about clauses and the update command. We will also cover making records, updating and modifying rows and EML commands.

    • 36:07

  • In this video we learn about SQL Comparison Clauses and how we use Comparison Clauses in SQL. This tutorial covers Comparison Operators by relating values by a mathematical symbol which is used to compare two values. Learn about comparison operators result - TRUE, FALSE, or UNKNOWN

    • 28:30

  • In this session we learn about SQL String and also cover all types of string in SQL and how we can use SQL Strings. In this video we will learn about the basic string functions such as concat_ws, file format, and insert function, L-case, u case, and lower case. We will also learn about basic functions such as upper functions.

    • 30:31

  • In this session we cover advance level string function and also cover all different commands we use in SQL String Function. This video is a sequel for string functions tutorial. In this tutorial we will learn few most useful string functions such as spaces and null issue as well, L-Pad command.

    • 38:20

  • In this SQL String function part 3 we learn select Repeat function and Select Replace function and also cover different between Select Repeat function and Select Replace function. This tutorial is another sequel to string functions, however, these functions are used less and not used that frequently. We will further learn here about the repeat function, absolute function, ceiling, and floor and down functions.

    • 23:20

  • In this session we learn about SQL Numeric Functions and how we use Numeric functions in SQL. In this video, we will be covering numerical functions. Learn about the basic date functions and also about truncate functions.

    • 36:50

  • In this video session we learn about SQL Numeric Function and also cover the basic functionality of SQL Numeric Function. SQL Data Functions. In this video we will learn about few more Date functions. We will further look into the day function option as well. This tutorial covers basic querying over a single table.

    • 46:38

  • : In this video we talk about SQL Joins Introduction and Demonstration and basic join’s function and how to make table using joins. In this tutorial learn about joints in SQL. This tutorial teaches us how to connect two different tables with joints. We will also cover the topic of querying two or more tables and about subquery .

    • 36:52

  • In this lecture last session we talk about MySQL Workbench and procedures and Views and MySQL Workbench functionality. In this tutorial learn about SQL in automating things. This tutorial covers stroll procedure, functions and views which are helpful for automation purposes in SQL.

    • 21:22

Course/Topic 4 - Business Intelligence Specialization - all lectures

  • In this lecture session we learn about basic introduction to business intelligence and also talk about factors of business intelligence in brief.

    • 1:20:10

  • In this tutorial we learn about business Intelligence, as a technological concept, which began shortly after the 1988 international conference, The Multiway Data Analysis Consortium, held in Rome. The conclusions reached at this conference jump-started efforts for simplifying BI analysis, while making it more user-friendly.

    • 2:42:42

  • In this lecture session we learn about The general process of business intelligence is as follows: Gathering data and organizing it through reporting. Turning it into meaningful information through analysis. Making actionable decisions aimed at fulfilling a strategic goal.

    • 1:03:39

  • In this lecture session we learn that Data Warehousing may be defined as a collection of corporate information and data derived from operational systems and external data sources and also talk about features of data warehousing.

    • 1:45:30

  • In this lecture session we learn about Business intelligence (BI) combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations to make more data-driven decisions.

    • 1:36:03

  • In this tutorial we learn about Apache HTTP Server is a free and open-source web server that delivers web content through the internet. It is commonly referred to as Apache and after development, it quickly became the most popular HTTP client on the web.

    • 1:10:35

  • In this lecture session we learn about NoSQL is an approach to database management that can accommodate a wide variety of data models, including key-value, document, columnar and graph formats. A NoSQL database generally means that it is non-relational, distributed, flexible and scalable.

    • 1:48:46

  • In this lecture session we learn about basic introduction of data analytics and also talk about features and functions of data analytics.

    • 1:07:03

  • In this lecture session we learn about Embedded BI (business intelligence) is the integration of self-service BI tools into commonly used business applications. BI tools support an enhanced user experience with visualization, real-time analytics and interactive reporting.

    • 1:03:54

  • In this lecture session we learn about Designing a collection strategy is one way to ensure that your accounts receivable stays under control and you continue to collect your cash. Without one, there is disorganization, disconnections, miscommunications and just simply chaos in the accounts receivable department.

    • 59:47

  • In this tutorial we learn about Survival analysis is a branch of statistics for analyzing the expected duration of time until one event occurs, such as death in biological organisms and failure in mechanical systems.

    • 1:08:06

  • In this lecture session we learn about Geospatial analysis is the gathering, display, and manipulation of imagery, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates or implicitly, in terms of a street address, postal code, or forest stand identifier as they are applied to geographic models.

    • 1:23:43

  • In this lecture session we learn about Data mining is used to explore increasingly large databases and to improve market segmentation. By analyzing the relationships between parameters such as customer age, gender, tastes, etc., it is possible to guess their behavior in order to direct personalized loyalty campaigns.

    • 1:47:09

  • In this lecture session we learn about It is the simplest unsupervised learning algorithm that solves clustering problems. K-means algorithm partitions n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster.

    • 1:08:09

  • In this lecture session we learn about Density-based spatial clustering of applications with noise (DBSCAN) is a well-known data clustering algorithm that is commonly used in data mining and machine learning.

    • 1:32:29

  • In this lecture session we learn about Regression analysis is a reliable method of identifying which variables have impact on a topic of interest. The process of performing a regression allows you to confidently determine which factors matter most, which factors can be ignored, and how these factors influence each other.

    • 43:19

  • In this lecture session we learn about machine learning techniques in business intelligence and also talk about features and function of machine learning techniques in brief.

    • 1:12:20

  • In this lecture session we learn about machine learning is the necessary piece for truly self-service BI tools. BI tools with machine learning implementations not only enable deeper insights into data, but they also empower business people to take analysis into their own hands.

    • 2:34:07

  • In this lecture session we learn about Predictive analytics combining several data analysis techniques, such as machine learning, data mining, and statistics.

    • 1:47:14

  • In this lecture session we learn about Crowdsourced data collection is a participatory method of building a dataset with the help of a large group of people. This page provides a brief overview of crowdsourced data collection in development and highlights points to consider when crowdsourcing data.

    • 1:56:04

  • In this lecture session we learn about basic introduction to business analysis and also talk about features and functions of business analysis.

    • 1:46:49

  • In this tutorial we learn about Data models define how the logical structure of a database is modeled. Data Models are fundamental entities to introduce abstraction in a DBMS.

    • 1:01:32

  • In this lecture session we learn about deep dive into data warehousing and also talk about some key features of deep dive data warehousing.

    • 2:22:58

  • In this lecture session we learn about how we reduce the development time to market and also talk about common patterns in deep dive into data warehousing and also talk about features of patterns.

    • 53:34

  • In this lecture session we learn about basic introduction to data warehousing and also talk about features of data warehousing.

    • 1:33:00

  • In this lecture session we learn about Feature extraction for machine learning and deep learning. Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set. It yields better results than applying machine learning directly to the raw data.

    • 1:57:04

  • In this lecture session we learn about Integration Platform as a Service (iPaaS) is a suite of cloud services enabling development, execution and governance of integration flows connecting any combination of on premises and cloud-based processes, services, applications and data within individual or across multiple organizations.

    • 1:00:53

  • In this lecture session we learn about Business intelligence concepts refer to the usage of digital computing technologies in the form of data warehouses, analytics and visualization with the aim of identifying and analyzing essential business-based data to generate new, actionable corporate insights.

    • 1:48:15

  • In this lecture session we learn about Contextual analysis is the systematic analysis—identification, sorting, organization, interpretation, consolidation, and communication—of the contextual user work activity data gathered in contextual inquiry, for the purpose of understanding the work context for a new system to be designed.

    • 1:17:25

  • In this lecture session we learn about Organizational intelligence is the capacity of an organization to create knowledge and use it to strategically adapt to its environment or marketplace. It is similar to I.Q., but framed at an organizational level. While organizations in the past have been viewed as compilations.

    • 1:36:55

  • In this lecture session we learn about the Information Systems Business Analysis program (ISBA) will help you develop critical skills in areas such as: application software, business data analysis and modeling, customer engagement management, business processes, enterprise resource planning, and communications.

    • 1:17:17

  • In this lecture session we learn about Operational intelligence (OI) is an approach to data analysis that enables decisions and actions in business operations to be based on real-time data as it's generated or collected by companies.

    • 1:01:04

  • In this lecture session we learn about Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions.

    • 1:17:07

  • In this lecture session we learn about Operational intelligence (OI) is an approach that gives you real-time information about what's happening in your business. It can help you make quick decisions that make your operations better. Cut through the noise of software delivery and break silos with powerful dashboards and reports.

    • 1:00:43

Course/Topic 5 - Power BI - all lectures

  • Learn how you can leverage Power BI to easily build reports and dashboards with interactive visualizations and see how other organizations have used this solution to drive business results with actionable insights.

    • 20:51

  • In this session, with Power BI Desktop, you can build advanced queries, models, and reports that visualize data. You can also build data models, create reports, and share your work by publishing to the Power BI service.

    • 01:57

  • This is the first part of Basic Dashboard in Power BI. In this video you will learn how to create a basic dashboard with simple data points.

    • 23:35

  • In this Video, we will show you how can you install Power PI desktop in PC.

    • 18:05

  • The third part in a series of Microsoft Power BI tutorials for beginners. This tutorial cover Filter’s pane and the Slicers.

    • 30:14

  • In this Part 4, video shows the time slicer feature of Power BI Desktop. Also running some simple statistics using the matrix visualization.

    • 30:09

  • In this Part 5 session you will learn about how to create a simple R script in Power BI desktop using the grid Extra package for displaying data and the dplyr package for data munging.

    • 21:32

  • In this Microsoft Power BI video, you will learn how to represent the data in a Map using Power BI. For this purpose, a data that contains the columns such as a State, Province, Country, City, ZIP Code/Postal Code, etc. must be present in the database

    • 33:52

  • In this video you can explore, what is Star Schema, why it is important in Power BI, Among the most basic design skills in designing a data warehouse solution is the star schema design.

    • 09:10

  • In this Power BI Tutorial, you will look at how to use Power Query in Power BI Desktop to merge different queries and join kind. This Microsoft Power BI tutorial for beginners is aimed at new Power BI users.

    • 07:49

  • In this video we will go through the basics of data modelling in Power BI, to get you started fast and easy.

    • 01:52

  • In this video, learn how to use relationship’s view, what other views exist in Power BI Desktop and why it's important to use them.

    • 10:39

  • This video explains the importance of cross filter direction in Microsoft Power BI. It discusses how the single or bi-directional filter affects the data in the report.

    • 12:44

  • In this video you will see details about m language and dax language.

    • 16:22

  • In this video you will learn how to create two interactive Power BI dashboards, plus a decomposition tree using the free Power BI tools.

    • 26:35

  • In this video, we will show you how you can use a parameter, within a Power BI report, to dynamically change the data in a report.

    • 15:46

Course/Topic 6 - Tableau - all lectures

  • In this video lecture we learn basic about Tableau. Tableau is a business intelligent tool for visually analysing the data.

    • 10:01

  • In this video we talk about Tableau Desktop Basics and also cover all the Basic topics of Tableau Desktop.

    • 02:32

  • In this video we learn how to install Tableau business intelligent tool into your desktop and process of Tableau Desktop Installation.

    • 06:28

  • In this video we about Tableau Desktop Workspace Navigation and cover all the importance of Tableau Desktop Workspace Navigation.

    • 10:40

  • In this session we talk about Tableau Design Flow and also cover all the different types of Tableau Design Flows.

    • 07:08

  • In this video we learn about Connections to Multiple Data Sources and cover all techniques of data sourcing.

    • 18:04

  • In this video we talk about Hands-on - Tableau Data Connection and also cover different between live and exact Tableau Data Connection.

    • 21:51

  • In this session we learn basic about Tableau Filters and different types of filters we can use in Tableau business tool.

    • 31:46

  • Data can be organized and simplifies by using various techniques in Tableau. In this session we also cover types of filters and condition of filters in Tableau.

    • 16:06

  • In this session we learn about Tableau Operators. Types of Tableau Operators and how to use these Tableau Operators.

    • 18:30

  • In this video we talk about Bins - Groups - Sets – Parameters and also cover all the parameters we use in Tableau.

    • 15:00

  • In this session we learn about Hands on - Tableau Sets and cover all different sets in Hands on - Tableau Sets..

    • 13:35

  • In this session we talk about Basic Tableau Charts and learn about different types of charts.

    • 23:26

  • In this video we talk about Hands on - Basic Tableau Charts how to make pie chart and importance of charts in Tableau business tool.

    • 21:24

  • In this lecture we learn the Tableau Advanced Topics like Advance graphs, LODS and its usage and extensions etc.

    • 03:03

  • In this video we talk about Tableau Extensions and cover all different types of extensions in a single video.

    • 21:20

  • In this Lecture section we talk and overview the Tableau Dashboards and explore the Dashboards of Tableau.

    • 26:30

  • In this lecture session we talk about the Tableau Story. In Tableau story is a sequence of visualization that work together to convey the information.

    • 20:27

  • In this video we talk about Tableau LODs extension and importance of LODs extension in Tableau business tool.

    • 18:55

  • In this lecture session we talk about Tableau Actions and also cover all Actions filters.

    • 26:53

Course/Topic 7 - SAS Business Intelligence - all lectures

  • This tutorial teaches you the integrated platform for delivering enterprise intelligence. This platform, which we call the SAS Enterprise Intelligence Platform, optimally integrates individual technology components within your existing IT infrastructure into a single, unified system.

    • 1:26:02

  • This session teaches the change management feature enables a team of SAS Data Integration Studio users to work simultaneously with a set of related metadata and avoid overwriting each other's changes. With change management, most users are restricted from adding or updating the metadata in a change-managed folder in the Folders tree.

    • 1:32:54

  • This teaches you the Data marts which are small slices of data warehouse. This module is a collection of tips on how to run your data mart implementation project Planning a Data Warehouse, Exercises

    • 1:26:33

  • This Help you to Learn how to build a data mart during SAS BI training, starting from reviewing a case study. Review of the Case Study, Define the Source Data, what are the Target Tables in SAS BI, Load the Target Tables, Exercises

    • 1:21:55

  • In this session, you will learn the On-Line Analytical Processing (or OLAP) has long been part of the data storage and exploitation strategy for SAS professional. Take an overview on OLAP in this module of SAS BI Training. What Is OLAP, Building an OLAP Cube in SAS BI, Solutions to Exercises

    • 1:11:53

  • This tutorial is designed to give you a good idea about SCD, its dimensions, load transformation and Lookup transformation. Defining Slowly Changing Dimensions in SAS BI How to use SAS BI SCD Type 2 Loader Transformation Using the Fact Table Lookup Transformation

    • 55:41

  • This session teaches you how to schedule data integration studio jobs during SAS BI training. Scheduling SAS Data Integration Studio Jobs

    • 1:11:38

  • In this session you will understand about the online analytical processing concepts, building an OLAP cube with SAS OLAP Cube Studio, building an information map from a SAS OLAP cube

    • 1:21:09

  • This video teaches you about the introduction to SAS Visual BI and exploring the SAS integration with JMP

    • 1:17:56

  • This tutorial helps you to Reviewing the platform for SAS Business Analytics and reviewing the course environment

    • 54:13

  • This video teaches you about the SAS Stored Process concepts, creating a stored process from a SAS Enterprise Guide project creating a stored process from a SAS program, creating stored process parameters, creating a stored process to provide a dynamic data source

    • 57:18

Course/Topic 8 - Data Visualization in Python - all lectures

  • In this first video tutorial on Data Visualization in Python course, you will get a brief introduction and overview on what is data visualization, its importance, benefits and the top python libraries for Data Visualization like Matplotlib, Plotly and Seaborn.

    • 25:25

  • In this first part of the video on Matplotlib, you will learn both the theoretical and the practical knowledge on Matplotlib, which is one of the most popular and top python libraries for Data Visualization. You will get a complete introduction to Matplotlib, the installation of Matplotlib with pip, the basic plotting with Matplotlib and the Plotting of two or more lines in the same plot.

    • 30:44

  • In this second part of the Matplotlib video tutorial, you will learn how to add labels and titles like plt.xlabel and plt.ylabel along with understanding how to create lists and insert functions onto it. All this can be seen explained it detail by the instructor by taking examples for it.

    • 21:14

  • In this tutorial, you will learn about 2 important python libraries namely; Numpy and Pandas. Along with the theoretical concepts, you will also get practical implementation on various topics related to these two such as what is Numpy and what is its use, the installation of Numpy along with example, what is pandas and its key features, with the installation of Python Pandas and finally the Data Structure with examples of Pandas.

    • 54:10

  • In this second part of the Numpy and Pandas tutorial, you will learn the complete overview of Pandas like its history, its key features, the installation process of Pandas, Pandas Data Structure and within it the Data Frame and syntax to create Data Frame. All this will be explained in detail by the instructor.

    • 41:10

  • In this third part of the video tutorial on Numpy and Pandas, you will learn about creating Data Frame from Dictionary. Also, you will understand how to read CSV Files with Pandas using practical examples by the Instructor.

    • 30:19

  • In this tutorial, you will learn about the different Data Visualization Tools such as Bar Chart, Histogram and the Pie Chart. You will get a complete understanding of what is these tools, why and how to use these 3 tools, the syntax for creating Bar Chart, Histogram and the Pie Chart and different programs for creating these data visualization tools. In the first part of the video, you will learn about the Bar Chart and in the subsequent videos, you will learn about the Histogram and the Pie Chart.

    • 49:49

  • In this second part of the Data Visualization Tools video, you will learn about the complete overview of Histogram like what is Histogram, how to create Histogram and many others with the help of practical examples by the instructor.

    • 37:01

  • In this third and final part of the Data Visualization Tools video, you will learn about the Pie Chart-what is Pie Chart, how to create the Pie Chart and how to create the syntax for Pie Chart? All these questions will be explained in detail by the instructor by taking practical examples. Further, you will understand the concept of Autoptic parameter in Pie Chart.

    • 47:31

  • In this first part of the video tutorial on more data visualization tools, you will learn about some additional data visualization tools apart from Bar Chart, Histogram and Pie Chart such as Scatter Plot, Area Plot, STACKED Area Plot and the Box Plot. The first part of this tutorial consists of mainly the Scatter Plot, the theoretical concepts associated with it such as what is Scatter Plot, the syntax for creating Scatter Plot and creating Scatter Plot with examples.

    • 35:33

  • In this second part of the video tutorial, you will learn and understand what is Area Plot, creating Area Plot with Function and Syntax and creating Area Plot with examples. All these will be seen explained in detail by the instructor. Further, you will also learn and understand the concept associated with the STACKED Area Plot.

    • 40:32

  • In this final part of the video tutorial, you will learn about the Box Plot; which is also known as Whisker Plot, how to create Box Plot, its syntax and arguments used like Data & Notch, the parameters used in Box Plot such as vert, patch artist and widths. These will be seen explained in detail by the instructor.

    • 36:46

  • In this first video tutorial on Advanced Data Visualization Tools, you will learn about the Waffle Chart – its definition, complete overview, the syntax and programs to create Waffle Chart and the step-by-step procedure to create the Waffle Chart. All these will be seen explained in detail by the instructor.

    • 33:42

  • In this second part of the video tutorial on Advanced Data Visualization Tools, you will learn about the Word Cloud-its definition, the reason why Word Cloud is used, what are the modules needed in generating the Word Cloud in Python, how to install Word Cloud and how to create Word Cloud with the help of some examples.

    • 58:41

  • In this tutorial, you will learn and understand about the concept of Heat Map and how one can create the Heat Map along with the help of the parameter camps. This will be seen explained in detail by the instructor.

    • 37:45

  • In this first part of the video tutorial on Specialized Data Visualization Tools, you will learn about the Bubble Chart; its definition and how to create bubble charts with the help of different examples.

    • 35:55

  • In this video, you will learn about the Contour Plots; which is also sometimes referred to as Level Plots. Along with understanding the whole theoretical concept of Contour Plots, you will also learn how to create Contour Plots with practical examples as will be seen explaining by the instructor in details.

    • 32:04

  • In this third part of the video on Specialized Data Visualization Tools, you will learn about the Quiver Plot and how to create the Quiver Plot by taking different examples. This will be seen explained in complete details by the instructor.

    • 40:49

  • In this video on Specialized Data Visualization Tools, you will learn about 3D plotting in Matplotlib and also the 3D Line Plot used in Data Visualization with the help of different practical examples and how to create it. This will be seen explained in detailed by the instructor throughout the tutorial.

    • 41:41

  • In this tutorial, you will learn about the 3D Scatter Plot and how to create a 3D Scatter Plot. The instructor will be seen explaining this in complete details with the help of different examples.

    • 27:58

  • In this tutorial, you will learn and understand the 3D Contour Plot, what is the function used in creating the 3D Contour Plot and how it can be created; which will be explained in detail by the instructor with the help of examples.

    • 30:18

  • In this last part of the video tutorial on Specialized Data Visualization Tools, you will learn about the 3D Wireframe Plot and the 3D Surface Plot, along with creating the same with the help of different examples, seen explained in detail by the instructor.

    • 43:06

  • In this tutorial, you will learn about Seaborn, which is another very important Python library. Through this video, you will get an introduction to Seaborn, along with some important features of it, functionalities of Seaborn, Installation of Seaborn, the different categories of plot in Seaborn and some basic type of plots one can create using Seaborn like Distribution Plot.

    • 50:52

  • In this second part of the video on Seaborn Library, you will learn and understand some basic plots using Seaborn Library like the Line Plot. Here, the instructor will be seen explaining in detail the Seaborn Line Plot and with a detailed example of how to create Seaborn Line Plot with random data.

    • 24:38

  • This is a continuation video of creating the Line Plot with some more examples using the Seaborn library. Along with this, you will also learn about the Lmplot and the function used for creating the Lmport. This can be seen explained in detailed by the instructor with practical examples.

    • 23:34

  • In this tutorial, you will learn about Data Visualization using Seaborn library. Under this, you will learn the Strip Plot, how to create the strip plot and the program used to create the Strip Plot. This will be shown by the Instructor with detailed examples like Strip plot using inbuilt data-set given in Seaborn and others.

    • 31:37

  • In this video, you will learn about the Swarm Plot; its definition, complete overview and how you can create the Swarm Plot. This can be seen explained in detail by the instructor with examples like visualization of “fmri” dataset using swarm plot().

    • 30:05

  • In this tutorial, you will learn a complete overview on Plotting Bivariate Distribution along with the concepts of Hexbin Plot, Kernel Density Estimation (KDE) and the Reg Plots. You will understand many of the in-depth concepts on these, with detailed explanation by the instructor with examples.

    • 43:18

  • In this tutorial, you will learn about the Pair Plot Function in Visualizing Pairwise Relationship under Seaborn library. You will understand the complete overview of Pair Plot Function, the syntax for using it, the parameters used like hue, palette, kind and diag kind. This will be seen explained in detail by the instructor with the help of examples.

    • 34:31

  • In this tutorial, you will learn about the Box Plot, Violin Plots and the Point Plots – their definitions and how to create them which will be seen explained in detail by the instructor throughout the video.

    • 45:53

Course/Topic 9 - Data Visualization in R - all lectures

  • In this introductory tutorial on Data Visualization in R Programming, you will learn about what is data visualization, the type of graph or chart one should select for data visualization, what is the importance and benefits of data visualization and finally what are the applications of data visualization.

    • 28:24

  • In this video, you will learn how to work on the Histogram, which falls under different Chart types used in Data Visualization in R Programming; along with working on the bar chart, box plot and heat map. You will be seeing a detailed explanation by the instructor on the complete workaround of these by taking different examples.

    • 42:00

  • In this video, you will learn what is density plot and how you can create the density plot by taking different examples for it. You will also learn about the different applications being used in the density plot under Data Visualization with R Programming.

    • 17:28

  • In this tutorial, you will learn about Data Visualization with GGPLOT2 Package where inside it you will learn the overview of GGPLOT2, iteratively building plots, univariate distributions and bar plot, annotation with GGPLOT2, axis manipulation and the density plot. You will get a complete understanding of the theoretical concept along with the implementation of each of these.

    • 41:07

  • In this second part of the video tutorial, you will learn about Plotting with GGPLOT2 and building your plots iteratively, along with the importance of the ‘+’ symbol and its use in the GGPLOT2 work process. You will be seeing a detailed explanation from the instructor by taking different examples.

    • 27:40

  • In this video you will learn about the complete theoretical and practical implementation of Univariate Distribution and Bar Plot, which can be seen explained in complete details by the instructor throughout the tutorial.

    • 30:42

  • In this tutorial, you will learn about annotation with ggplot2, along with geom text () and adding labels with geom label () with complete explanation on this by the instructor with the help of different examples.

    • 50:28

  • In this tutorial, you will learn about Axis Manipulation with ggplot2, its complete overview and in-depth concepts along with the different functions used during the process. You will be seeing explaining the topic in complete details by the instructor by taking examples and working in R studio.

    • 34:13

  • In this section, you will learn about Text Mining and Word Cloud, along with the Radar Chart, Waffle Chart, Area Chart and the Correlogram. In this first part of the video, you will learn about the Text Mining and Word Cloud, the different reasons behind using Word Cloud for text data, who is using Word Clouds and the various steps involved in creating word clouds.

    • 31:43

  • In this video, you will learn how to execute data using redline function. Also, you will understand the usage of corpus function and content transformer function. Further, you will understand about the text stemming, Term Document Matrix function and the Max word’s function.

    • 31:49

  • In this tutorial, you will learn about the Radar Chart, the function used in the Radar Chart which is gg Radar (), scales, mapping and the use label. Along with this, you will also learn how to create Radar Chart in R studio. Moreover, you will learn about the Waffle Chart in R and how to create vector data in Waffle Chart with the help of different examples.

    • 34:18

  • In this last part of the session, you will learn about the Area Chart, its in-depth concepts and how to work on it. This will be seen explained in detail by the instructor. Moreover, you will also learn about the Correlogram in R, the correlation matrix, Mt cars and the work around on different visualization methods been used.

    • 39:54

  • This is a project tutorial titled Visualizing COVID-19 where you will see the different scenarios being explained by the tutor on visualizing COVID-19 data and how it can be done through Data Visualization in R process. In this first part, you will understand the complete overview of the project, its description and the different tasks associated with it being done by the ggplot.

    • 34:59

  • In this second part of the project video, you will learn about the “Annotate” process and the number of COVID cases being reported in China with the help of Data Visualization. You will be seeing the task performed on the dataset being provided by the WHO along with understanding the tribble function and how it will help during the entire work process.

    • 37:38

  • In this last part of the session, you will understand the work around of the task being done with the help of plot. You will see a detailed explanation by the instructor seeking help of few examples to explain the complete process of plotting in respect to the COVID-19 project being implemented.

    • 34:50

Course/Topic 10 - Build your Career in Data Science - all lectures

  • In this lecture about how to learn data science with a step by step explanation. This video will talk about the career path in data science specially from a fresher’s point of view. Further we will talk about the skills required to become a successful data scientist and also about the compensation of a data scientist. Lastly we will see the platforms and data science tools used to learn data science. This video also clarifies whether a person would need a degree for a successful career in data science.

    • 25:08

  • This video talk majorly about the data science career path. There are different career options in data science. You will know how attending a one-week duration bootcamps on data science will help you more over a 3 years’ degree. The courses are intended to make the student job ready and be equipped with the skills necessary for becoming a Data Scientist.

    • 18:41

  • In this session we learn about the importance and introduction for Data Science. In this video we will also talk about the different technologies in data science and strong foundations of R programming language. We will also see the jobs in data science and the types of jobs in data science. Furthermore, we will see prerequisites for data science and difference between BI and Data Science.

    • 54:03

  • In this lecture about how to learn data science with a step by step explanation. This video will talk about the career path in data science specially from a fresher’s point of view. Further we will talk about the skills required to become a successful data scientist and also about the compensation of a data scientist. Lastly we will see the platforms and data science tools used to learn data science. This video also clarifies whether a person would need a degree for a successful career in data science.

    • 43:49

Course/Topic 11 - Data Science with Python - all lectures

  • In this video tutorial we will get introduced to Data Science and the integration of Python in Data Science. Furthermore, we will look into the importance of Data Science and its demand and the application of Data Science.

    • 1:01:14

  • In this video we will learn, all the concepts of Python programming related to Data Science. We will also learn about the Introduction to Python Programing, what is Python Programming and its History, Features and Application of Python along with its setup. Further we will see how to get started with the first python program.

    • 59:19

  • This video talks about the Variable and Data Types in Python Programming. In this session we will learn What is variable, the declaration of variable and variable assignment. Further we will see the data types in python, checking data types and data type conversions.

    • 27:05

  • This tutorial will help you to understand Data Types in python in depth. This video talks about the data types such as numbers, sequence type, Boolean, set and dictionary.

    • 55:27

  • This tutorial talks about the Identifier, keyword, reading input and output formatting in Data Science. We will learn about what is an identifier and keywords. Further we will learn about reading input and taking multiple inputs from a user, Output formatting and Python end parameter.

    • 49:19

  • This tutorial talks about taking multiple inputs from user and output formatting using format method, string method and module operator.

    • 44:09

  • This tutorial talks about the Operators and type of operators. In this session we will learn about the types of operators such as arithmetic, Relational and Assignment Operators.

    • 27:52

  • This tutorial talks further about the part 2 of operators and its types. In this session we will learn about the types of operators such as Logical, Membership, Identity and Bitwise Operators.

    • 31:22

  • In this video you will learn about the process of decision making in Data Science. Furthermore, this tutorial talks about different types of decision-making statements and its application in Data Science.

    • 45:23

  • In this video tutorial we will learn about the Loops in Python programing. We will cover further the different types of Loops in Python, starting with: For Loop.

    • 32:47

  • In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: While loop and nested loop.

    • 39:43

  • In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: break, continue and pass loops

    • 23:13

  • In this video tutorial we will start explaining about the lists in Python Programming. This tutorial talks about accessing values in the list and updating the list in Data Science.

    • 46:54

  • In this video tutorial we will look into the further parts about the lists in Python Programming. Deleting list elements, basic list operations, built in functions and methods and the features which are covered in this session.

    • 40:30

  • This tutorial will cover the basics on Tuples and Dictionary function in Data Science. We will learn about accessing and deleting tuple elements. Further we will also cover the basic tuples operations and the built in tuple functions and its methods. At the end we will see the differences in list and tuple.

    • 53:32

  • This tutorial will cover the advanced topics on Tuples and Dictionary function in Data Science. Further in this session we will learn about the Python Dictionary, how to access, update and delete dictionary elements. Lastly we will cover built in functions and methods.

    • 51:22

  • In this session we will learn about the functions and modules used in Data science. After watching this video, you will be able to understand what is a function, the definition of function and calling a function.

    • 44:01

  • In this session we will learn about the further functions and modules used in Data science. After watching this video, you will be able to understand the ways to write a function, Types of functions, Anonymous Functions and Recursive functions.

    • 43:16

  • In this session we will learn about the advanced functions and modules used in Data science. After watching this video, you will be able to understand what is a module, creating a module, import statement and locating modules.

    • 48:21

  • This tutorial talks about the features of working with files. In this video we will learn about opening and closing file, the open function, the file object Attributes, the close method, reading and writing files.

    • 1:05:09

  • This tutorial talks about the advanced features of working with files. In this video we will learn about file positions, renaming and deleting files.

    • 26:50

  • In this session we will learn about the regular expression. After this video you will be able to understand what is a regular expression, meta characters, match function, search function, Re- match vs research, split function and sub function.

    • 1:02:45

  • This video introduces you to the Data Science Libraries. In this video you will learn about the Data science libraries: libraries for data processing, modelling and data visualization.

    • 45:35

  • In this session we will teach about the components of python ecosystem in Data Science. This video talks about the Components of Python Ecosystem using package Python distribution Anaconda and jupyter notebook.

    • 54:24

  • This tutorial talks about the basics of analyzing data using numpy and pandas. The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data. We will further see what is Numpy and why we use numpy.

    • 56:58

  • This tutorial talks about the later part of analyzing data using numpy and pandas. In this tutorial we will learn how to install numpy.

    • 43:37

  • This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn what is Pandas and the key features of Pandas. We will also learn about the Python Pandas environment setup.

    • 37:21

  • This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn about Pandas data structure with example.

    • 1:12:53

  • This the last session on Analysing Data using Numpy and Pandas. In this session we will learn data analysis using Pandas

    • 28:31

  • In this video tutorial we will learn about the Data Visualization using Matpotlib. This video talks about what is data visualisation, introduction to matplotlib and installation of matplotlib.

    • 37:45

  • In this session we will see the part 2 of Data Visualization with Matplotlib. This video talks about the types of data visualization charts and line chart scatter plot

    • 43:41

  • This tutorial covers part 3 of Data Visualization with Matplotlib. This session covers the types of data visualisation charts: bar chart histogram, area plot pie chart and box plot contour plot.

    • 1:09:26

  • This session talks about the Three-Dimensional Plotting with Matplotlib . In this we will learn about plot 3D scatter, plot 3D contour and plot 3D surface plot.

    • 1:03:43

  • In this tutorial we will cover basics of Data Visualisation with Seaborn. Further we will cover Introduction to seaborn, seaborn functionalities, how to install seaborn and the different categories of plot in seaborn

    • 41:53

  • In this tutorial we will cover the advanced topics of Data Visualisation with Seaborn. In this video we will see about exploring seaborn plots.

    • 59:16

  • Introduction to Statistical Analysis is taught in this video. We will learn what is statistical analysis and introduction to math and statistics for data science. Further we will learn about the terminologies in statistics for data science and categories in statistics, its correlation and lastly mean median and mode quartile.

    • 1:15:05

  • This video course talks about the basics of Data Science methodology. We will learn how to reach from problem to approach.

    • 47:33

  • In this session we will see Data Science Methodology from requirements to collection and from understanding to preparation.

    • 44:12

  • In this session we will learn advanced Data Science Methodology from modelling to evaluation and from deployment to feedback.

    • 39:25

  • This video tutorial talks about the - Introduction to Machine Learning and its Types. In this session we will learn what is machine learning and the need for machine learning. Further we will see the application of machine learning and different types of machine learning. We will also cover topics such as supervised learning, unsupervised learning and reinforcement learning.

    • 56:17

  • This video tutorial talks about the basics of regression analysis. We will cover in this video linear regression and implementing linear regression.

    • 1:11:51

  • This video tutorial talks about the further topics of regression analysis. In this video we will learn about multiple linear regression and implementing multiple linear regression.

    • 54:25

  • This video tutorial talks about the advanced topics of regression analysis. In this video we will learn about polynomial regression and implementing polynomial regression.

    • 38:18

  • In this session we will learn about the classification in Data science. We will see what is classification, classification algorithms and Logistic regression. Also we will learn about implementing Logistic regression.

    • 1:08:43

  • In this session we will learn about the further topics of classification in Data science, such as decision tree and implementing decision tree.

    • 38:36

  • In this session we will learn about the advanced topics of classification in Data science, such as support vendor machine and implementing support vector machine.

    • 25:37

  • This tutorial will teach you about what is clustering and clustering algorithms. Further we will learn what K means clustering and how does K means clustering work and also about implementing K means clustering.

    • 53:10

  • In this session we will see the further topics of clustering, such as hierarchical clustering, agglomerative hierarchical clustering, how does agglomerative hierarchical clustering Work and divisive hierarchical clustering.

    • 28:55

  • This video tutorial talks about the advanced topics of clustering, such as implementation of agglomerative hierarchical clustering.

    • 33:58

  • This video will help you to understand basics of Association rule learning. In this session we will learn about the Apriori algorithm and the working of Apriori algorithm.

    • 53:30

  • This video will help you to understand advanced topics of Association rule learning such as implementation of Apriori algorithm.

    • 58:45

  • This is a session on the practical part of Data Science application. In this example we will see problem statement, data set, exploratory data analysis.

    • 56:40

  • This is a session on the practical part of Data Science application.

    • 42:39

  • This is a session on the practical part of Data Science application. In this we will see the implementation of the project.

    • 50:54

  • This is a session on the practical part of Data Science application

    • 38:18

  • This is a session on the practical part of Data Science application

    • 1:02:31

Course/Topic 12 - Data Science with R - all lectures

  • In this lecture session we learn about introduction of data science and also talk about features of data science in R.

    • 54:03

  • In this lecture session we learn about data collection and management and also talk about features of data collection and management in data science with R.

    • 29:32

  • In this lecture session we learn about model deployment and maintenance and also talk about functions of model deployment and maintenance in data science with R.

    • 12:47

  • In this lecture session we learn about setting expectations and also talk about factors of setting expectations in brief.

    • 10:18

  • In this lecture session we learn about loading data into R and also talk about features of loading data into R and also talk about the importance of loading data into R.

    • 42:20

  • In this lecture session we learn about exploring data in data science and machine learning and also talk about features of exploring data in data science and machine learning.

    • 08:01

  • In this lecture session we learn about features of exploring data using R and also talk about factors of exploring data using R.

    • 45:36

  • In this lecture session we learn about benefits of data cleaning and also talk about features of benefits of data cleaning.

    • 22:44

  • In this lecture session we learn about cross validation in R and also talk about features of validation in data science with R.

    • 17:32

  • In these lecture sessions we learn about data transformation in data science with R and also talk about features of data transformation in brief.

    • 1:35:26

  • In this lecture session we learn about modeling methods in data science with R and also talk about the importance of modeling methods.

    • 20:13

  • In this lecture session we learn about solving classification problems and also talk about features of solving classification problems in brief.

    • 11:55

  • In this lecture session we learn about working without known targets in data science with r and also talk about features of working without known targets.

    • 19:58

  • In this lecture session we learn about evaluating models in data science with R and also talk about features of evaluating models in brief.

    • 28:11

  • In this lecture session we learn about confusion matrix in indian accounting standards and also talk about features of confusion matrix.

    • 34:03

  • In this lecture session we learn about introduction to linear regression and also talk about features of linear regression in indian accounting standards.

    • 1:25:24

  • In this lecture session we learn about linear regression in R and also talk about features and functions of linear regression in brief.

    • 26:51

  • In this lecture session we learn about linear regression in R in data science with r and also talk about features of linear regression in R language.

    • 41:22

  • In this lecture session we learn about simple and multiple regression in data science with r and also talk about the basic difference between simple and multiple regression in brief.

    • 56:54

  • In this lecture session we learn about linear and logistic regression in data science with r language and also talk about functions of linear and logistics regressions.

    • 29:09

  • In this lecture session we learn about support vector machines (SVM) in R and also talk about features of support vector machines in data science with R language.

    • 45:18

  • In this lecture session we learn about factors of support vectors machines in data science with R and also talk about features of support vectors machines.

    • 1:30:55

  • In this lecture session we learn about unsupervised methods in data science with R and also talk about functions of unsupervised methods in data science.

    • 24:44

  • In this lecture session we learn about clustering in data science with R language and also talk about features of clustering in data science.

    • 50:44

  • In this lecture session we learn about K-means algorithms in R and also talk about all types of algorithms in data science with R language.

    • 1:09:44

  • In this lecture session we learn about hierarchical clustering in data science with R language and also talk about features of hierarchical clustering.

    • 33:41

  • In this lecture session we learn about libraries in data science with R and also talk about libraries of hierarchical clustering in brief.

    • 23:15

  • In this lecture session we learn about the dendrogram of diana and also talk about all types of clustering in data science with R.

    • 41:05

  • In this lecture session we learn about market basket analysis in data science with R and also talk about features of market basket analysis in data science with R.

    • 05:08

  • In this lecture session we learn about MBA and association rule mining in data science with r language.

    • 23:52

  • In this lecture session we learn about implementing MBA in data science with R and also talk about implementing MBA.

    • 09:18

  • In this lecture session we learn about association rule learning in data science with R and also talk about features of association rule learning.

    • 24:02

  • In this lecture session we learn about decision tree algorithms in data science with R and also talk about features of tree algorithms.

    • 36:29

  • In this lecture session we learn about exploring advanced methods in tree algorithms in data science with R and also talk about features of exploring advanced methods.

    • 48:16

  • In this lecture session we learn about using kernel methods and also talk about features of using kernel methods in data science with R.

    • 47:43

  • In this lecture session we learn about documentation and deployment and also talk about features of documentation and deployment in data science with R.

    • 30:09

Course/Topic 13 - Machine Learning (basic to advanced) - all lectures

  • In this session we will learn about introduction to Machine Learning. We will start by learning about the basics of Linear Algebra required to learn Machine Language. Further we will learn about Linear equations represented by Matrices and Vectors.

    • 32:51

  • In this module we will learn about the computational roots of matrices. We will learn how to multiply matrix with scalar and vector. We will learn about addition and subtraction of matrices.

    • 27:08

  • In this module we will learn about Num-Pie Linear Algebra to work on Python. It further includes the understanding of the use of functions - #dot, #vdot, #inner, #matmul, #determinant, #solve, #inv. Basic examples of the #dot, #vdot functions will be discussed.

    • 13:55

  • In this module we will learn about how the #inner function work in a two-dimension array. We will also learn its usage in #dot and #vdot. We will see explanation of the functions solving examples.

    • 13:39

  • In this module we will learn about using #matmul function. We will learn about normal product and stack of arrays. We will also learn how to check the dimensions of the array and how to make it compatible.

    • 11:20

  • In this module we will learn about the #determinant function. The basics of the #determinant function will be explained. Examples will be solved with explanations to understand it.

    • 06:46

  • In this module we will learn what a Determinant is. We will also learn about how to find a Determinant. We will further learn how to find the Determinant of a 2*2 and 3*3 matrix learn about the basics of #inv function.

    • 08:40

  • In this module we will learn about the #inv function. We will learn about how to find the inverse of a matrix. We will also learn how to find the Identity matrix for the inverse.

    • 16:32

  • In this module we will discuss about the inverse of a matrix. We will understand what an Inverse is. We will further learn how the Inverse of a matrix is found.

    • 16:44

  • In this module we will learn about the difference of the dot( ) and the inner( ). We will see examples of dot( ) and inner( ), We will also learn about the dissimilarities between the dot( ) and inner( ) with the help of examples.

    • 27:37

  • In this module we will learn about numpy matrix. We will learn the different ways of creating a matrix. We will also learn about a vector as a matrix and its multiplication with matrix.

    • 16:27

  • In this module we will learn about the #numpy.vdot( ) function. This module is a continuation of the previous module. We will also learn about the #numpy,inner( ) function.

    • 16:24

  • In this module we will understand the different concepts like Rank, Determinant, Trace, etc, of an array. Then we will learn how to find the item value of a matrix. We will also learn about the matrix and vector products.

    • 25:27

  • In this module we will learn about the matrix and vector products. We will learn about how it works on imaginary and complex numbers. We will also get an understanding of matmul( ), inner( ), outer( ), linalg.multi_dot( ), tensordot( ), einsom( ), einsum_path( ),linalg.matrix_power( ).

    • 25:02

  • In this module we will learn about the basics of #inverse of a matrix. We will understand what an Inverse is. We will also see examples of inverse of a matrix and learn how to calculate it.

    • 16:44

  • In this module we will learn about the basics of Python. We will also learn about the Packages needed by the machine language. We will further learn the basics of numpy, scipy, pandas, skikit-learn, etc. needed machine learning and data science.

    • 16:18

  • In this module we will understand about SciPy. We will also learn about SkiKit-learn and Theano. We will further learn about TensorFlow, Keras, PyTorch, Pandas, Matplotlib.

    • 19:19

  • In this module we will see examples of the topics discussed in the previous module. We will also start the basics of Python. We will also solve some basic problems.

    • 18:50

  • In this module we will continue the basic problems of Python. We will also understand about Operators. We will also see the different operators and its applications.

    • 21:36

  • In this module we will continue learning the different Operators. We will also learn about Advanced Data types. We will learn and understand the different data types and about Sets.

    • 18:39

  • In this module we will learn about list. We will see the different functions of list. We will also learn about Jupyter notebook.

    • 17:46

  • In this module we will learn about #condition statements in Python in brief. We will also learn about the applications of #condition statements We will solve some examples to understand the #condition statements.

    • 25:44

  • In this module we will learn about the Loop in Pyhton. We will also learn about the different kinds of loops. We will see examples of For loop, and break keyword.

    • 16:48

  • In this module we will continue with the #for loop. We will also learn about the continue keyword. We will solve examples for the usage of the keywords.

    • 20:48

  • In this module we will learn about Functions in Python. We will solve examples using different functions. We will understand how functions work.

    • 16:53

  • In this module we will learn about arguments in functions. We will also solve examples to understand the usage of arguments in functions. We will also learn about #call by reference in Python.

    • 18:40

  • In this module we will learn about strings. We will also learn about types of arguments for functions in python. We will also see the usage of the different types of arguments.

    • 10:36

  • In this module we will learn about default arguments. We will also learn about variable arguments. We will solve examples to understand it better.

    • 06:37

  • In this module we will learn about the remaining arguments. We will understand about default and variable arguments better. We will also learn about keyword arguments.

    • 32:18

  • In this module we will learn about built-in functions. We will also learn about the different built-in functions in python. We will solve examples to understand the functions better.

    • 37:15

  • In this module we will continue the previous functions. We will also learn about other built-in functions. We will also learn about bubble sort in python.

    • 09:44

  • In this module we will learn about the scope of variable in function. We will also learn about the different variables and its usage. We will solve examples using the different variables to understand it better.

    • 13:15

  • In this module we will learn about the math module in python. We will learn about the different inbuilt functions that deal with math functions. We will solve problems using the different math functions.

    • 12:52

  • In this module we will continue with the previous lecture. We will also learn about the different arguments in functions. We will also learn about call by reference in python.

    • 18:40

  • In this module we will continue with the previous lecture. We will also start mathplotlib in python. We will learn the different types of mathplotlib by using jupyter.

    • 1:04:09

  • In this module we will learn about loan calculator using tkinter. We will also learn how to use the loan calculator. We will solve an example to understand its usage.

    • 33:36

  • In this module we will continue with the previous lecture. We will learn how to compute payments using functions. We will also learn about the function getmonthlypayment.

    • 36:44

  • In this module we will learn about numpy function. We will also learn about mathematical and logical operations using numpy. We will also be explained about different numpy arrays.

    • 17:05

  • In this module we will continue with the previous lecture. We will learn about different numpy attributes. We will solve examples using the different attributes and slicing an array.

    • 24:43

  • In this module we will learn about advanced slicing of an array. We will use jupyter to do array slicing. We will understand detail how array slicing works.

    • 29:55

  • In this module we will learn about using jupyter notebook online. We will also learn about ranges. We will learn about creating arrays from ranges. We will also learn about linear space.

    • 28:43

  • In this module we will learn about the average function. We will also learn about the different averages. We will solve examples to understand the function.

    • 21:25

  • In this module we will learn about generating random strings and passwords. We will also learn about generating a string of lower and upper case letters. We will solve examples using the different strings.

    • 21:25

  • In this module we will learn about generating strings. We will also learn about upper case letters and only printing specific letter. We will also learn about alpha numeric letters.

    • 22:46

  • In this module we will learn about the unique function. We will continue using arrays. We will solve example using unique functions in arrays.

    • 16:52

  • In this module we will learn about array manipulation function, We will learn about the delete function in numpy. We will solve examples for better understanding.

    • 10:24

  • In this module we will learn about the insert function in numpy. We will also learn about flattened array. We will solve examples.

    • 10:22

  • In this module we will learn about examples with two dimension arrays. We will also learn about the ravel function. We will also learn about the rollaxis function, swapaxes function.

    • 14:43

  • In this module we will learn about statistical functions. We will also learn about min and max values. We will solve examples using the functions.

    • 06:22

  • In this module we will learn about functions for rounding. We will also learn about round off function, floor function and ceil function. We will solve examples using the functions.

    • 14:16

  • In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples.

    • 25:20

  • In this module we will learn about numpy nonzero function. We will also learn about the where function. We will solve examples using the different functions.

    • 14:23

  • In this module we will learn about matrix library. We will also learn about the different matlib functions We will solve different examples using the matlib function. vvvv

    • 18:25

  • In this module we will learn about the basic operations that can be done on numpy arrays. We will also learn about arithmetic operations and functions. We will do examples with arithmetic operations.

    • 14:40

  • In this module we will learn about numpy filter array. We will do programs on numpy filter array. We will solve examples using the filter array.

    • 16:35

  • In this module we will learn about array manipulation functions. We will see how the array manipulation functions work. We will learn about the different manipulation functions.

    • 29:18

  • In this module we will learn about broadcasting function in numpy. We will also learn about reshape in numpy. We will also learn about removing function in numpy.

    • 24:23

  • In this module we will learn about indexing. We will also learn about slicing. We will solve examples to understand the concept.

    • 15:39

  • In this module we will learn about numpy append function. We will also learn about resize function. We will solve examples using the functions.

    • 25:20

  • In this module we will learn about conversion of numpy dtypes to native python types. We will also learn to create 4*4 matrix in which 0 and 1 are staggered with zero on the main diagonal. We will also learn to create 10*10 matrix elements on the borders will be equal to 1 and inside 0.

    • 22:11

  • In this module we will learn how to use a python program to find the maximum and minimum value of a flattened array. We will also see the function called flat and flatten to make the array flattened. We will learn about function import numpy as np and array-np.arrange( )

    • 19:44

  • In this module we will learn how to generate a random string of a given length. Tutor will address the issues faced in generating random strings. Further in the video, we will discuss the various ways in which generation of a random staring can be performed.

    • 21:46

  • In this video we will be covering on creating a simple project. We will see the practical on how tutor creates a simple project. We will also see some examples on how to create a simple project. The video talks about how to get common items between 2 python numpy arrays.

    • 21:23

  • In this video we will talk about another function in python programming called the split function. The function split divides the arrays into sub arrays. The split() method splits a string into an array of substrings. The split() method returns the new array. The split() method does not change the original string. If (" ") is used as separator, the string is split between words.

    • 11:53

  • This video is a sequel of explanation of spilt function. We will discuss the three types of split functions – 1. Normal split, 2. Horizontal split and 3. Vertical Split. Further we will discuss the roles of split function and what do they do.

    • 12:18

  • In this video we will learn about the numpy filter array. We will further see what is filtering of array. Getting some elements out of an existing array and creating a new array out of them is called filtering of array, using a bullion index list.

    • 16:35

  • In this video we will learn about an important topic in Python, i.e Python file handling. We will see what is a file and the type of executable files. Further we will see what is output and how to view the output. Different access modes that can be opened with the file.

    • 14:08

  • In this video we will see an example on how to open and file in view mode, by giving the name of the file. File statement in Python is used in exception handling to make the code cleaner and much more readable. It simplifies the management of common resources like file streams. ... with open ( 'file_path' , 'w' ) as file : file .

    • 23:38

  • This video is a continuation of file system tutorial. Here we will see to use the append mode and what is append mode. Python has a built-in open() function to open a file. This function returns a file object, also called a handle, as it is used to read or modify the file accordingly. We can specify the mode while opening a file. In mode, we specify whether we want to read r , write w or append a to the file.

    • 28:56

  • In this module we will start a new topic known as random module which is a very important part in numpy. Further we will discuss the functionalities of random module to generate random numbers.

    • 14:48

  • In this module we will see how to generate the arrays on float and hot generate a single floating value from 0 to 1. Further we will see taking array as a parameter and randomly return one of the values.

    • 19:02

  • In this module we will learn the random module in continuations. The random is the module present in the numpy library. This module contains simple random generation methods.

    • 22:41

  • In this module how random module contains functions used to generate random numbers. We will also see some permutations and distribution functions.

    • 22:41

  • In this module we will see the choice functions and the different variants of choice function. Further we will see how to randomly select multiple choices from the list. Random.sample or random.choices are the functions used to select multiple choices or set.

    • 10:03

  • In this module we will see the difference between the sample function and the choices functions. Further, we will do a random choice from asset with Python, by converting it to tuple.

    • 09:21

  • In this module we will learn about the random Boolean in Python, using random.choice. In order to generate Random boolean, we use the nextBoolean() method of the java. util. Random class. This returns the next random boolean value from the random generator sequence

    • 43:00

  • In this module we will learn about the library available in python that is called Pandas. We will see how Pandas is one of the important tools available in Python. Further we will see how Pandas makes sense to list the things.

    • 15:57

  • In this module we will learn about the basics of Pandas. Further we will see how this an important tool for Data scientist and Analysts and how pandas is the back bone of most of the data projects.

    • 05:53

  • This module is a sequel of the previous tutorial on Pandas. In this module we will see practical project on pandas using series and dataframes. Lastly we will learn how to handle duplicate and how to handle information method and shape attribute.

    • 32:00

  • In this video we will see about column clean and how to clean the column. Further we will see how to rename the columns by eliminating symbols and other different ways.

    • 20:58

  • In this module we will learn about how to work with the missing values or null values. Further we will see if the dataset is inconsistent or has some missing values then how to deal with the missing values when exploring the data.

    • 28:10

  • In this video we will see how to perform the imputation on column, i.e., metascore which has some null values. Further we will see how to use describe function on the genre column of the dataset.

    • 15:19

  • In this module we will learn about the frequency of columns. Further we will see about the functio0n called value counts. The value counts function when used on the genre column tells us the frequency of all the columns.

    • 08:09

  • In this video we will learn about the methods of slicing, selecting and extracting. If these methods are not followed properly then we will receive attribute errors. Further we will learn to manipulate and extract data using column headings and index locations.

    • 18:57

  • 2.7 MATPLOTLIB BASICS

    • 20:22

  • 2.7.1 MATPLOTLIB BASICS

    • 20:38

  • 2.7.2 MATPLOTLIB BASICS

    • 17:32

  • 2.7.3 MATPLOTLIB BASICS

    • 04:00

  • 2.7.4 MATPLOTLIB BASICS

    • 11:31

  • 2.7.5 MATPLOTLIB BASICS

    • 07:23

  • 2.7.6 MATPLOTLIB BASICS

    • 16:55

  • 2.7.7 MATPLOTLIB BASICS

    • 11:53

  • 2.7.8 MATPLOTLIB BASICS

    • 17:16

  • 2.7.9 MATPLOTLIB BASICS

    • 17:40

  • 2.7.9.1 MATPLOTLIB BASICS

    • 16:55

  • 2.7.9.11 MATPLOTLIB BASICS

    • 20:38

  • 2.8 AGE CALCULATOR APP

    • 26:46

  • 2.8.1 AGE CALCULATOR APP

    • 12:22

  • 2.8.2 AGE CALCULATOR APP

    • 33:00

  • 2.8.3 AGE CALCULATOR APP

    • 37:56

  • 3.1 MACHINE LEARNING BASICS

    • 27:27

  • 3.1.1 MACHINE LEARNING BASICS

    • 17:31

  • 3.1.2 MACHINE LEARNING BASICS

    • 17:36

  • 3.1.3 MACHINE LEARNING BASICS

    • 15:38

  • 3.1.4 MACHINE LEARNING BASICS

    • 13:53

  • 3.1.5 MACHINE LEARNING BASICS

    • 11:55

  • 3.1.6 MACHINE LEARNING BASICS

    • 18:51

  • 3.1.7 MACHINE LEARNING BASICS

    • 23:55

  • 3.1.8 MACHINE LEARNING BASICS

    • 22:38

  • 3.1.9 MACHINE LEARNING BASICS

    • 29:13

  • 3.1.9.1 MACHINE LEARNING BASICS

    • 17:36

  • 3.2 MACHINE LEARNING BASICS

    • 08:08

  • 4.1 TYPES OF MACHINE LEARNING

    • 35:11

  • 4.1.1 TYPES OF MACHINE LEARNING

    • 15:03

  • 4.1.2 TYPES OF MACHINE LEARNING

    • 16:18

  • 4.1.3 TYPES OF MACHINE LEARNING

    • 13:58

  • 4.1.4 TYPES OF MACHINE LEARNING

    • 19:45

  • 4.1.5 TYPES OF MACHINE LEARNING

    • 05:12

  • 4.1.6 TYPES OF MACHINE LEARNING

    • 31:39

  • 5.1 TYPES OF MACHINE LEARNING

    • 28:19

  • 5.1.1 TYPES OF MACHINE LEARNING

    • 31:56

  • 5.1.2 TYPES OF MACHINE LEARNING

    • 25:08

  • 5.1.3 TYPES OF MACHINE LEARNING

    • 46:37

  • 5.1.4 TYPES OF MACHINE LEARNING

    • 31:00

  • 5.1.5 TYPES OF MACHINE LEARNING

    • 24:21

  • 5.1.6 TYPES OF MACHINE LEARNING

    • 16:20

  • 5.1.7 TYPES OF MACHINE LEARNING

    • 32:53

  • 5.1.8 TYPES OF MACHINE LEARNING

    • 56:20

  • 5.2 MULTIPLE REGRESSION

    • 34:30

  • 5.2.1 MULTIPLE REGRESSION

    • 37:35

  • 5.2.2 MULTIPLE REGRESSION

    • 40:56

  • 5.2.3 MULTIPLE REGRESSION

    • 56:04

  • 5.2.4 MULTIPLE REGRESSION

    • 46:41

  • 5.2.5 MULTIPLE REGRESSION

    • 38:14

  • 5.2.6 MULTIPLE REGRESSION

    • 37:48

  • 5.2.7 MULTIPLE REGRESSION

    • 1:01:26

  • 5.3 KNN INTRO

    • 26:49

  • 5.3.1 KNN ALGORITHM

    • 48:57

  • 5.3.2 KNN ALGORITHM

    • 11:17

  • 5.3.3 INTRODUCTION TO CONFUSION MATRIX

    • 42:22

  • 5.3.4 INTRODUCTION TO SPLITTING THE DATASET USING TRAINTESTSPLIT

    • 24:37

  • 5.3.5 KNN ALGORITHM

    • 50:29

  • 5.3.6 KNN ALGORITHM

    • 56:10

  • 5.4 INTRODUCTION TO DECISION TREE

    • 44:37

  • 5.4.1 INTRODUCTION TO DECISION TREE

    • 39:32

  • 5.4.2 DECISION TREE ALGORITHM

    • 36:41

  • 5.4.3 DECISION TREE ALGORITHM

    • 20:10

  • 5.4.4 DECISION TREE ALGORITHM

    • 55:37

  • 5.5 UNSUPERVISED LEARNING

    • 23:26

  • 5.5.1 UNSUPERVISED LEARNING

    • 09:16

  • 5.5.2 UNSUPERVISED LEARNING

    • 18:28

  • 5.5.3 UNSUPERVISED LEARNING

    • 29:50

  • 5.5.4 AHC ALGORITHM

    • 46:30

  • 5.5.5 AHC ALGORITHM

    • 19:55

  • 5.6 KMEANS CLUSTERING

    • 23:08

  • 5.6.1 KMEANS CLUSTERING

    • 30:25

  • 5.6.2 KMEANS CLUSTERING

    • 1:01:04

  • 5.6.3 DBSCAN ALGORITHM

    • 37:09

  • 5.6.4 DBSCAN PROGRAM

    • 32:45

  • 5.6.5 DBSCAN PROGRAM

    • 49:56

Course/Topic 14 - Deep Learning Foundation - all lectures

  • In this session we will learn about the introduction to Deep Learning. This video talks about Deep Learning as a series introduction and what is a neural network. Furthermore, we will talk about the 3 reasons to go deep and your choice of Deep net.

    • 52:39

  • In this video tutorial we will discuss about the neural networks and the 3 reasons to go Deep. Further we will also learn about the use of GPU in artificial intelligence and your choice of deep net.

    • 30:06

  • In this session we will learn about the deep learning models basics. After this video you will be able to understand the concept of restricted Boltzmann machines and deep belief network. Furthermore, you will learn about the convolution neural network and recurrent neural network.

    • 43:00

  • In this video course further topics of Deep learning models. After this video you will be able to understand the convolution neural network and its characteristics in detail.

    • 1:26:51

  • In this video course further topics of Deep learning models. After this video you will be able to understand the recurrent neural network and its characteristics.

    • 15:43

  • In this session the tutor talks about the basic Additional Deep Learning Models. In this video you will learn about Auto encoders, Recursive neural tensor network and generative adversarial networks

    • 44:28

  • This session is in continuation to the previous session. In this video we will learn about the Recursive Neural Tensor Network in detail and hierarchical structure of data.

    • 31:44

  • In this Additional Deep Learning Models tutorial, we will proceed with the Generative Adversarial Networks (GAN) and its uses.

    • 22:22

  • In this video the tutor explains the Platforms and Libraries of Deep Learning. We will start with what is a deep net platform, H2O.ai and Dato Graph Lab. Further we will see what is a Deep Learning Library and Theano and Caffe. We will also cover a bit of Keras and TensorFlow.

    • 53:43

  • This tutorial will cover the further part of DatoGraph Lab and its history. Further we wil see the benefits and uses of DatoGraph Lab.

    • 28:19

  • This tutorial will cover the further part of DatoGraph Lab and its history.

    • 28:15

  • In this video we will cover the further topics of Deep Learning platform and Libraries such as what is a Deep Learning Library? when and how to use Theano and Caffe as Deep Learning Library.

    • 29:19

  • In previous video we have leant about Theano and Caffe Deep Learning Library. In this video we will learn about the TensorFlow (free and open source library) as a Deep Learning Library and building Deep Learning Models.

    • 40:18

  • In this video we will learn about the last type of Library i.e. Keras. Keras is an open source neural network library and runs on top of Theano or TensorFlow. We will further see the advantages of Keras in Deep Learning.

    • 25:34

Course/Topic 15 - Generative AI Specialization - all lectures

  • Lecture 1 - Introduction to Generative AI - part 1

    • 34:55

  • Lecture 2 - Introduction to Generative AI - part 2

    • 30:05

  • Lecture 3 - Introduction to Generative AI - part 3

    • 31:14

  • Lecture 4 - Introduction to Large Language Models (LLMs) - part 1

    • 23:54

  • Lecture 5 - Introduction to Large Language Models (LLMs) - part 2

    • 36:54

  • Lecture 6 - Prompt Engineering Basics - part 1

    • 17:13

  • Lecture 7 - Prompt Engineering Basics - part 2

    • 46:41

  • Lecture 8 - Responsible AI

    • 44:48

  • Lecture 9 - Generative AI - Impact - Considerations - Ethical Issues

    • 53:37

Course/Topic 16 - CISSP (Cybersecurity) - all lectures

  • In this lecture session we learn about the basics of cybersecurity and also cover basic functions and factors of cybersecurity in brief.

    • 1:10:40

  • In this lecture session we learn about CISSP certification guide and also talk about factors of CISSP certification guide in cybersecurity.

    • 33:09

  • In this lecture session we learn about cyber information systems security professional certification domain and talk about overview of domain in brief.

    • 2:19:49

  • In this lecture session we learn about CISSP exam preparation guide in cyber security and also talk about more guides for exam preparation.

    • 31:24

  • In this lecture session we learn about CISSP preparation techniques and also talk about cyber security function and importance.

    • 1:04:33

  • In this lecture session we learn about risk analysis in cyber information systems security professionals and also talk about risk analysis factors in brief.

    • 54:10

  • In this lecture session we learn about goals of risk analysis and also talk about risk analysis factors in cybersecurity in brief.

    • 39:20

  • In this lecture session we learn about cybersecurity goals the object of cybersecurity is to prevent the risk and also cover all types of goals in cyber security.

    • 22:03

  • In this lecture session we learn about types of cyber attacks in cybersecurity and also talk about how we prevent us from thes cyber attacks.

    • 15:54

  • In this lecture session we learn about types of cyber attackers in cybersecurity and also cover all attackers in brief.

    • 53:16

  • In this lecture session we learn about cybersecurity archival storage and also talk about storage factors in brief.

    • 54:48

  • In this lecture session we learn about cybersecurity VPNS and also talk about other VPNs of cybersecurity and importance of VPNs.

    • 54:04

  • In this lecture session we learn about cyber security standards in system security professionals and also talk about standard security.

    • 56:36

  • In this lecture session we learn about cyber security challenges in cyber security in cyber attacks.

    • 1:02:03

  • In this lecture session we learn about different mail service providers and also talk about mail service providers factors.

    • 1:11:00

  • In this lecture session we learn about the security and risk management domain and also talk about functions of security and risk management.

    • 59:12

  • In this lecture session we learn about the importance of security and risk management in brief.

    • 23:10

  • In this lecture session we learn about factors of security and risk management in brief.

    • 34:05

  • In this lecture session we learn about implementation of confidentiality and also talk about implementation of integrity in brief.

    • 27:33

  • In this lecture session we learn boat asset security domain and also talk about functions of asset security domain in brief.

    • 38:12

  • In this lecture session we learn about asset security domain importance and also talk about more security domains in brief.

    • 1:14:35

  • In this lecture session we learn about security architecture and engineering domain and also talk about factors of security architecture in brief.

    • 43:22

  • In this lecture session we learn about the function of security architecture and engineering domain in brief.

    • 26:10

  • In this lecture session we learn about governance, intelligence and also talk about operation and management.

    • 34:54

  • In this lecture session we learn about product ciphertext messages and also talk about the importance of security architecture and engineering domain.

    • 22:24

  • In this lecture session we learn about the fundamentals concept of security models and also talk about more concepts in brief.

    • 26:43

  • In this lecture session we learn about migration plans and perform migration and also talk about trust and assurance.

    • 12:34

  • In this lecture session we learn about generating, store and limit the use of cryptography keys.

    • 1:11:38

  • In this lecture session we learn about fire suppression systems in brief and also talk about fire sprinkler systems in cyber security.

    • 35:09

  • In this lecture session we learn about certified information system security professionals and also talk about what is cissp.

    • 26:36

  • In this lecture session we learn about cissp domain and also talk about why we need cissp in cyber security.

    • 33:35

  • In this lecture session we learn about the importance of cissp in security architecture and engineering.

    • 1:19:30

  • In this lecture session we learn about communication and network security and also talk about factors of communication and networks security.

    • 18:15

  • In this lecture session we learn about communication and networks security domain and also talk about functions of network security.

    • 38:59

  • In this lecture session we learn about deals with networks components related topics like networks models in cyber security.

    • 28:43

  • In this lecture session we learn about secure network components and also talk about factors of network components.

    • 29:52

  • In this lecture session we learn about components related topics like networks and also cover more topics in cyber security.

    • 1:37:35

  • In this lecture session we learn about identity and access management and also cover functions of identity access management.

    • 40:52

  • In this lecture session we learn about security assessment and testing domain and also talk about other assessments in domain.

    • 10:37

  • In these lecture sessions we learn about collecting security process data in security assessment in cyber security.

    • 33:23

  • In these lecture sessions we learn about what is security assessment and testing in brief.

    • 19:24

  • In these lecture sessions we learn about three general types of vulnerability assessment and also talk about function of vulnerability in brief.

    • 1:32:31

  • In these lecture sessions we learn about security operation domain in brief and also talk about misuse case testing in brief.

    • 43:36

  • In this lecture session we learn about security operation in cyber security and also talk about factors of security operation.

    • 31:48

  • In this lecture session we learn about access control types in security operation in brief and also talk about protective parameters in cyber security.

    • 25:43

  • In this lecture session we learn about security operation importance and also cover all parameters of security operations.

    • 45:53

  • In this lecture session we learn about data loss prevention, steganography and watermarking in security operations.

    • 31:31

  • In this lecture session we learn about training and awareness and also talk about implementing recovery services.

    • 10:35

  • In this lecture session we learn about a fully functional data center that is always up and running with real time in cyber security.

    • 54:01

  • In this lecture session we learn about standardizing a configuration across the device in security operation and also talk about system resilient.

    • 2:12:29

  • In this lecture session we learn about the basics of software development security domain in cyber security and also talk about what is security domain in brief.

    • 53:04

  • In this lecture session we learn about software development security domain function and importance.

    • 29:18

  • In this lecture session we learn about software assurance maturity model and also talk about building security in maturity model in brief.

    • 24:16

  • In this lecture session we learn about unknown vulnerabilities and also talk about software libraries and operating systems.

    • 18:00

Course/Topic 17 - Microsoft Excel - all lectures

  • Lecture 1 - Introduction to Microsoft Excel

    • 19:48

  • Lecture 2 - Key in Data

    • 12:46

  • Lecture 3 - Font and Alignment

    • 13:52

  • Lecture 4 - Cut Paste and Format Painter

    • 07:20

  • Lecture 5 - Control plus Keys

    • 23:40

  • Lecture 6 - Home Commands and Clipboard

    • 20:09

  • Lecture 7 - File Tab

    • 12:49

  • Lecture 8 - Sorting and Filtering

    • 26:09

  • Lecture 9.1 - Basic Formulas

    • 23:00

  • Lecture 9.2 - Text Formulas

    • 32:49

  • Lecture 10.1 - VLookup - part 1

    • 26:39

  • Lecture 10.2 - VLookup - part 2

    • 06:20

  • Lecture 10.3 - HLookup

    • 14:22

  • Lecture 10.4 - This is a bonus session on Vlookup from a different tutor

    • 10:27

  • Lecture 10.5 - This is a bonus session on Vlookup from a different tutor

    • 05:02

  • Lecture 11.1 - Pivot Tables - part 1

    • 15:16

  • Lecture 11.2 - Pivot Tables - part 2

    • 07:14

  • Lecture 11.3 - Pivot Tables - part 3

    • 23:38

  • Lecture 11.4 - Pivot Tables - part 4

    • 11:47

  • Lecture 12.1 - Charts - part 1

    • 09:05

  • Lecture 12.2 - Charts - part 2

    • 21:01

  • Lecture 12.3 - Column Charts

    • 30:28

  • Lecture 12.4 - Bar Charts

    • 09:07

  • Lecture 12.5 - Line Charts

    • 09:57

Course/Topic 18 - Google Sheets course - all lectures

  • Lesson 1 - Introduction to Google Sheets

    • 02:31

  • Lesson 2 - Menu Options - File

    • 08:40

  • Lesson 3 - Menu Options - Edit

    • 03:30

  • Lesson 4 - Menu Options - View

    • 04:49

  • Lesson 5 - Menu Options - Insert

    • 04:59

  • Lesson 6 - Menu Options - Format

    • 10:21

  • Lesson 7 - Menu Options - Data and more

    • 18:17

  • Lesson 8 - Entering Data and Editing

    • 07:12

  • Lesson 9 - Functions - Numeric Function

    • 15:24

  • Lesson 10 - Functions - Text Function

    • 30:38

  • Lesson 11 - Functions - Date Functions

    • 06:21

  • Lesson 12 - Charts and Conditional Formatting

    • 22:12

  • Lesson 13 - Pivot Tables

    • 23:21

  • Lesson 14 - Saving - Sharing

    • 06:22

Course/Topic 19 - Cloud Computing Basics - all lectures

  • In this lecture session we learn about cloud computing, which means storing and accessing data over the internet instead of a hard disk. It is defined as a service that provides users to work over the internet.

    • 38:26

  • In this lecture session we learn about cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”) to offer faster innovation, flexible resources, and economies of scale.

    • 30:33

  • In this lecture session we learn about Cloud computing is a general term for anything that involves delivering hosted services over the internet. These services are divided into three main categories or types of cloud computing.

    • 33:14

  • In this tutorial we learn about the importance of cloud computing can be found in using services like Google Drive and Apple iCloud. The use of these services allows documents, contacts, pictures, and a whole lot more online.

    • 32:19

Course/Topic 20 - SAP MDG - all lectures

  • In this first video on the SAP Master Data Governance course, you will be learning about the MDG Deployment Options like Co-Deployment and Hub Deployment along with the overview of High-Level Architecture. You will also be learning about the concepts like Work Centers, Roles, Change Request, Workflow-Linear and Workflow-Parallel and the Staging Area Usage.

    • 36:24

  • This video tutorial will teach you how to the complete work process of an SAP MDG Solution and how it is helpful for a business enterprise model. The trainer will also be showing how to work on the “Inactive Data Models” and the methodology of different MDG Solutions like Governance Integration, NWBC, ECC & HANA DB to name a few. Further, you will also be learning about the Simplified Process Flow of MDG.

    • 1:04:50

  • In this tutorial, you will be learning about the Simplified Process Flow in SAP MDG along with the Business Partner Attributes; the Supplier and Customer Attributes. Further, you will be learning about the HTML-based user interface, data flow, and replication and how MDG helps the Business User and with User Interface, Data Quality functions, Material Class and Hierarchy Maintenance, Robust Data Replication, Template Creation, Mass Maintenance, and the Analytical features for the processes.

    • 1:23:08

  • In this video, you will be learning about the 2 types of Deployment Options in the MDG system which are the Co-Deployment and Hub-Deployment options. The trainer will be seen explaining each and every detail in the difference between the two by taking examples of how it works in the real-time SAP MDG system.

    • 2:36:08

  • In this video, you will learn about the communication work process between the Hub Deployment and ECC System. The trainer will be seen explaining the complete workaround of how the Co-Deployment and ECC Operation System works.

    • 57:09

  • In this video, you will learn about the different entity types associated with the SAP MDG system. You will also be learning about the Root Table along with the MARA and the MARC table.

    • 09:37

  • In this tutorial, you will learn about the concept of Mapping in the MDG system along with the different types of attributes and their types. The trainer will be showing the complete work process of the mapping between them in the SAP system of the “Inactive Data Model” of the screen.

    • 1:33:04

  • In this tutorial, you will learn about the fundamental steps to configure a governance scenario like Data Model, User Interface, Change Request Process, User Access, and Using the newly created scenario. Further, you will be learning about the complete concept of the Data Model along with the Entity Types, SU Types Summary, and the Relationships with detailed explanation on the SAP system.

    • 41:12

  • This video is a complete practical training on how to create a Data Model in the SAP MDG system. You will be seeing a detailed practical demonstration of the same by the trainer in the system.

    • 52:09

  • In this video, you will be learning about the Business Rule Framework, its complete and detailed concept, Business Rule; Elements, Mode of Operations, Workflow Integration in BRF Plus. The trainer will be showing the BRF in the SAP system. Along with this, you will also be learning about the MDG UI configuration and under it parts like Business Activity, Logical Action, Change Request Type, and the CR Status.

    • 1:19:09

  • In this video, you will learn about the concept of Step Type and how it is configured. Along with this, you will also be learning about the prerequisites for BRF, what is BRF Workflow, its Key Elements, User-Agent Table, and a complete step-by-step process of how BRF Workflow works.

    • 1:36:06

  • In this video, you will learn about the Model to Dialog-Step-Agent Decision Table, Key Elements for Process Pattern like UI Dialog, Call Sub-Workflow, Call Data Replication, etc. You will be getting a detailed explanation of these Key Elements and also how all these works in the SAP MDG system.

    • 59:48

  • In this tutorial, you will learn about the Static Workflow Template which will be shown in the SAP system. Further, you will be knowing about Floor Plan Manager –its Component Structure, FPM Component Interfaces, Sample Structure of FPM, and lastly about the Fluid Editor which is the tool for working of the FPM in the MDG system.

    • 1:14:18

  • In this video, you will get complete practical knowledge on the FPM Workbench along with working on the Web Dynpro Application. The tutor will be seen explaining each and every step in the SAP MDG system.

    • 1:15:56

  • In this video, you will be learning about the concepts of BOL and Genile which stand for Business Object Layer and Generic Interface Layer resp. The trainer will be seen explaining these concepts taking examples of the practical scenarios; its work process and along with this the component configuration in the SAP MDG system.

    • 58:09

  • In this MDG tutorial, you will be learning about the concept of Roles; creating custom roles under Role Maintenance, change of roles, and others. Moreover, you will be learning about the nwbc screen elements under it concepts like adaptation, Feeder Class, etc and lastly, you will be seeing be having the knowledge on the various MDG UI Layers.

    • 1:05:01

  • In this video, you will learn about the steps involved under Personalization, Customizing and Configuration in the SAP MDG work process, with a detailed component configuration of the steps shown by the trainer in the SAP system.

    • 1:12:08

  • In this tutorial, the trainer will be seen explaining the various application programming contents, answering the queries of the students, and further you will get the knowledge of the Staging Table in SAP MDG.

    • 52:51

  • This video is all about the various concepts associated with the MDG relevant APIs, its history along with different types of APIs like Convenience API, Governance API, and the Abstraction Layer. You will also be learning about the Reading Entity Data the complete work cycle of the API in the MDG system.

    • 1:08:59

  • This tutorial is about how you can implement an access class to save data in the SAP MDG system. The trainer will be seen showing the step-by-step process of the implementation in the system.

    • 1:16:28

  • This MDG tutorial is a complete practical demonstration of the API Programming work process, in addition to another work process in the system like ABAP Debugger, Class Builder, etc. The trainer will be seen explaining all the concepts in the MDG system.

    • 1:01:52

  • This video is a small discussion on the previous MDG concepts being taught by the trainer throughout this course.

    • 04:04

  • This video is a quick recap on the Feeder Class; what is it and how it works in the SAP MDG environment, along with some system-oriented concepts related to it.

    • 29:59

  • In this video, you will be learning about the different activities associated with the SAP MDG module such as Analysing Application Log, Display Workflow, Process work items as Administrators, and others. Along with that, you will be getting an explanation on the Finance Accounting Governance with G/L account centrally and analyze log for outbound implementation.

    • 56:54

  • In this video, you will be learning an in-depth and detailed concept of the Data Replication process in the SAP MDG system, with hands-on working on the data models, structure, and mapping in the system. Further, you will be knowing about Defining Business Systems, Creating a Business Object Type in MDG system, and defining object nodes.

    • 1:21:13

  • In this tutorial, you will learn how to create and edit mappings in the MDG system. Furthermore, you will learn about the Service Mapping Tool, Defining Business Systems, Defining Object Nodes, Assigning Key Structures to Object Identifiers, Defining Filter Objects, and Creating Outbound Interface.

    • 2:13:37

  • In this video, you will learn about Customer Vendor Integration (CVI) and the key concepts associated with it, while showing it in the SAP system such as Synchronization Options, Set BP Role Categories for Customer Integration, Defining Vendor Link for Business Partner Roles and others. You will also be learning about the Web Service Configuration and the Replication Model in the SAP MDG module.

    • 1:15:14

  • This last video on the SAP MDG course will teach you how to define an outbound implementation, business objects, and Web Service Configuration in the SAP MDG system. You will be getting a detailed step-by-step process of the whole workflow.

    • 1:12:04
Course Objectives Back to Top

•Learn about overseeing data management, data analytics, and data governance

•Learn about data quality

•Learn about spearheading data and information strategy

•Learn how to Implement proper data analytics to identify and, hopefully, reduce pain points at all stages of the business process,

•Learn how to increasing not only profit, but also trust in the eyes of stakeholders and clients

Course Syllabus Back to Top
Certification Back to Top

The Chief Data Officer (CDO) Certification ensures you know planning, production and measurement techniques needed to stand out from the competition. 

The CDO oversees a range of data-related functions that may include data management, ensuring data quality, and creating data strategy. They may also be responsible for data analytics and business intelligence — the process of drawing valuable insights from data.

Most CDOs have significant experience – more than 5 years, according to PayScale – in related jobs. In addition to such career expertise, CDOs may also have degrees in subjects such as computer science or engineering.

A chief data officer (CDO) is a C-level executive who is responsible for an organization's data use and data governance. The CDO is expected to guide the organization in its ability to derive maximum value from the data available to the enterprise. 

While some people think CDOs should report to the CIO, many others see the two roles operating best as partners, with the CIO being in charge of the technology infrastructure, and the CDO being in charge of tying the data and insights collected by the infrastructure back to the business priorities.

Uplatz online training guarantees the participants to successfully go through the  Chief Data Officer (CDO) Certification provided by Uplatz. Uplatz provides appropriate teaching and expertise training to equip the participants for implementing the learnt concepts in an organization.

Course Completion Certificate will be awarded by Uplatz upon successful completion of the Chief Data Officer (CDO) online course.

Career & Jobs Back to Top

The Chief Data Officer (CDO) draws an average salary of $125,000 per year depending on their knowledge and hands-on experience.

Reporting to the CEO, you will lead a team of professionals to oversee all aspects of the Data Science function. Your main responsibilities will include: Designing and executing the data strategy and roadmap.

A chief data officer (CDO) is a C-level executive who is responsible for an organization's data use and data governance. The CDO is expected to guide the organization in its ability to derive maximum value from the data available to the enterprise.

Note that salaries are generally higher at large companies rather than small ones. Your salary will also differ based on the market you work in.

Sr. Chief Data Officer.

Jr. Chief Data Officer.

Interview Questions Back to Top

Below are commonly asked interview questions along with sample answers for a Chief Data Officer (CDO) interview:

 

1. Can you describe your experience as a CFO and the key achievements you have had in previous roles?

As a CFO, I have successfully managed financial operations and contributed to the growth of the companies I worked for. At Company A, I implemented cost-saving measures that resulted in a 10% increase in profit margins within the first year.

2. How do you approach financial planning and budgeting as a CFO?

Financial planning is crucial for success. I collaborate with department heads to develop realistic budgets and regularly monitor performance against financial goals.

3. How do you ensure regulatory compliance and adherence to financial reporting standards as a CFO?

Regulatory compliance is a top priority. I stay updated with accounting standards and work closely with our auditors to ensure accurate and timely financial reporting.

4. How do you manage financial risk and ensure the company's financial stability as a CFO?

I prioritize risk management and conduct regular risk assessments. By diversifying investments and maintaining adequate reserves, I ensure the company's financial stability.

5. Can you share an example of a challenging financial decision you had to make as a CFO?

How did you handle it? At a previous company, I had to decide whether to invest in a high-risk project with the potential for significant returns. I conducted thorough analysis and consulted with key stakeholders to make an informed decision.

6. How do you approach financial analysis to identify opportunities for cost reduction and efficiency improvement?

Financial analysis is essential for identifying areas of improvement. I use data analytics to assess operational processes and implement cost-saving strategies.

7. Can you discuss your experience in managing cash flow and optimizing working capital?

Cash flow management is critical. I use cash flow projections and work closely with the finance team to optimize working capital and maintain healthy cash reserves.

8. How do you stay informed about the financial health of the company and communicate it to stakeholders?

I believe in transparent communication with stakeholders. I provide regular financial reports and presentations that highlight the company's financial performance and outlook.

9. How do you approach financial forecasting and planning for business growth as a CFO?

Financial forecasting involves analyzing historical data and market trends to predict future performance. I use this information to plan for strategic business growth.

10. Can you share an example of a successful financial strategy you implemented to support the company's growth?

At my previous company, I secured funding for a strategic acquisition that expanded our market presence and contributed significantly to revenue growth.

11. How do you approach financial risk assessment for new business ventures or investments?

Financial risk assessment involves evaluating potential returns and potential downsides. I conduct thorough due diligence and analysis to assess the risk-reward ratio before making investment decisions.

12. Can you discuss your experience in managing debt and capital structure for the company?

Debt management is crucial for financial health. I maintain a balanced capital structure and prioritize debt reduction to improve the company's financial position.

13. How do you approach financial transparency and accountability within the organization?

Financial transparency is vital for trust. I ensure that financial information is readily available to relevant stakeholders and promote a culture of accountability.

14. Can you share an example of a situation where you successfully managed a financial crisis or turnaround?

At a previous company, I navigated a financial crisis by implementing cost-cutting measures and negotiating with creditors to secure additional funding.

15. How do you approach tax planning and compliance as a CFO?

Tax planning is integral to financial strategy. I work with tax professionals to optimize tax efficiency and ensure compliance with all tax laws and regulations.

16. Can you discuss your experience in managing financial relationships with banks and investors?

Maintaining strong financial relationships is crucial. I ensure open communication with banks and investors to foster trust and secure funding opportunities.

17. How do you approach financial due diligence for potential mergers and acquisitions (M&A)?

Financial due diligence involves thorough examination of financial records and performance. I collaborate with the M&A team to assess the financial viability of potential targets.

18. Can you share an example of how you effectively managed working capital during a period of rapid growth?

During a growth phase, I closely monitored working capital and optimized inventory management to meet increased demand while controlling costs.

19. How do you ensure cost efficiency and control within the finance department?

Cost efficiency is a priority. I regularly assess departmental expenses, leverage technology for process improvement, and identify cost-saving opportunities.

20. Can you discuss your experience in capital budgeting and investment decision-making?

Capital budgeting involves evaluating long-term investment opportunities. I use discounted cash flow (DCF) analysis and other valuation methods to make informed investment decisions.

21. How do you approach financial forecasting during uncertain economic conditions?

Forecasting during uncertain conditions requires scenario analysis and sensitivity testing. I use multiple forecasting models to prepare for different economic scenarios.

22. Can you share an example of a situation where you effectively negotiated financing terms with lenders or investors?

During a financing round, I successfully negotiated favorable terms with investors, securing additional funding at attractive interest rates.

23. How do you ensure cost transparency and efficiency in the procurement process?

Cost transparency in procurement involves vendor evaluation and competitive bidding. I implement procurement policies that prioritize value and quality.

24. Can you discuss your experience in managing financial audits and working with auditors?

I have overseen financial audits and developed strong relationships with auditors. I ensure all financial records are accurate and accessible for audit purposes.

25. How do you approach financial modeling and analysis for new product launches or expansion projects?

Financial modeling for new ventures involves forecasting revenue, expenses, and ROI. I use financial models to evaluate the viability and potential risks of new projects.

26. Can you share an example of a situation where you effectively managed cash flow during a period of market volatility?

During a volatile market phase, I prioritized cash flow management and reduced non-essential expenses to ensure the company's financial stability.

27. How do you approach cost allocation and transfer pricing in a multi-entity organization?

Cost allocation and transfer pricing involve fairness and efficiency. I use cost drivers and benchmarking to ensure appropriate allocation and pricing.

28. Can you discuss your experience in managing financial relations with international partners or subsidiaries?

Managing international financial relations requires understanding different regulations and currency risks. I work with finance teams from international entities to ensure compliance and financial efficiency.

29. How do you approach working with the executive team to align financial goals with overall business objectives?

I collaborate closely with the executive team to understand business goals and align financial strategies accordingly. Regular meetings and communication are key to this alignment.

30. Can you share an example of a situation where you effectively managed financial resources during a period of market downturn?

During a market downturn, I implemented cost-saving measures, renegotiated contracts, and optimized cash flow to protect the company's financial stability.

31. How do you approach financial modeling for long-term capital investments, such as plant expansions or major equipment purchases?

Financial modeling for capital investments involves analyzing expected cash flows, payback periods, and internal rates of return (IRR). I ensure that investments align with our long-term strategic vision.

32. Can you discuss your experience in managing relationships with external stakeholders, such as investors, analysts, and creditors?

I prioritize transparent and open communication with external stakeholders. I provide timely updates on financial performance and strategic initiatives to build trust.

33. How do you approach financial performance evaluation and benchmarking against industry peers?

Financial performance evaluation involves analyzing key financial ratios and metrics. I benchmark our performance against industry peers to identify areas for improvement.

34. Can you share an example of how you effectively managed inventory levels to optimize cash flow and meet customer demand?

By using inventory management tools and demand forecasting, I maintained optimal inventory levels to balance cash flow and meet customer needs.

35. How do you approach financial compliance with international accounting standards or reporting requirements?

Staying compliant with international accounting standards requires continuous education and collaboration with experts in international finance.

36. Can you discuss your experience in managing currency risk and exposure in a global business environment?

Managing currency risk involves using hedging strategies and financial instruments to mitigate exposure to foreign exchange fluctuations.

37. How do you approach financial communication with non-financial stakeholders, such as department heads or employees?

Financial communication with non-financial stakeholders involves simplifying complex financial data and using visuals to convey key insights effectively.

38. Can you share an example of a situation where you successfully implemented cost-saving measures without compromising business operations?

At my previous company, I streamlined procurement processes and renegotiated contracts, resulting in significant cost savings without affecting service quality.

39. How do you approach financial analysis to evaluate potential divestiture or asset disposal opportunities?

Financial analysis for divestiture involves assessing the financial impact and strategic alignment of potential sales to optimize portfolio performance.

40. Can you discuss your experience in managing financial controls and risk management policies within the organization?

I prioritize a strong internal control environment to safeguard assets and mitigate financial risks. I also develop risk management policies to minimize exposure to potential threats.

41. How do you approach financial modeling for revenue forecasting and pricing strategies?

Revenue forecasting involves analyzing market demand and pricing elasticity. I use financial models to predict revenue trends and set competitive pricing strategies.

42. Can you share an example of how you effectively managed accounts receivable to improve cash flow?

By implementing efficient invoicing processes and offering incentives for early payments, I reduced the average collection period and improved cash flow.

43. How do you approach financial data analysis to identify areas for cost optimization and process improvement?

Financial data analysis involves using tools like Excel and data visualization to identify trends and inefficiencies, which informs decision-making for cost optimization.

44. Can you discuss your experience in managing investor relations and communicating financial performance to shareholders?

I prioritize transparency and timely communication with investors. I provide clear financial updates and strategic insights to maintain their confidence in the company.

45. How do you approach financial forecasting and scenario planning during periods of economic uncertainty?

Financial forecasting during uncertainty requires evaluating multiple scenarios and stress testing financial models to prepare for various outcomes.

46. Can you share an example of a situation where you successfully negotiated with suppliers to achieve cost savings?

By consolidating purchases and negotiating volume discounts, I achieved significant cost savings from our suppliers.

47. How do you approach financial analysis to assess the financial health and creditworthiness of potential customers or clients?

Financial analysis for creditworthiness involves evaluating financial ratios and credit history to ensure the financial stability of potential customers.

48. Can you discuss your experience in managing capital raising activities, such as debt issuances or equity offerings?

I have managed several capital raising activities. I work closely with investment bankers and legal advisors to execute successful debt or equity offerings.

49. How do you approach financial forecasting for cyclical or seasonal businesses?

Financial forecasting for cyclical businesses requires analyzing historical patterns and adjusting forecasts to account for seasonal fluctuations.

50. Can you share an example of how you effectively managed capital expenditures to align with the company's strategic goals?

By conducting thorough ROI analyses and prioritizing projects that align with the company's strategic vision, I ensured capital expenditures were optimized for maximum value.

 

Note: The provided responses are sample answers and should be tailored to the individual's specific experiences and accomplishments. Additionally, in a real interview, candidates are encouraged to be concise, confident, and provide concrete examples to support their responses.

Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

Q2. Will I get certificate?
A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

Q3. How long is the course access?
A3. All our video courses comes with lifetime access. Once you purchase a video course with Uplatz you have lifetime access to the course i.e. forever. You can access your course any time via our website and/or mobile app and learn at your own convenience.

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A4. Video courses cannot be downloaded, but you have lifetime access to any video course you purchase on our website. You will be able to play the videos on our our website and mobile app.

Q5. Do you take exam? Do I need to pass exam? How to book exam?
A5. We do not take exam as part of the our training programs whether it is video course or live online class. These courses are professional courses and are offered to upskill and move on in the career ladder. However if there is an associated exam to the subject you are learning with us then you need to contact the relevant examination authority for booking your exam.

Q6. Can I get study material with the course?
A6. The study material might or might not be available for this course. Please note that though we strive to provide you the best materials but we cannot guarantee the exact study material that is mentioned anywhere within the lecture videos. Please submit study material request using the form https://training.uplatz.com/study-material-request.php

Q7. What is your refund policy?
A7. Please refer to our Refund policy mentioned on our website, here is the link to Uplatz refund policy https://training.uplatz.com/refund-and-cancellation-policy.php

Q8. Do you provide any discounts?
A8. We run promotions and discounts from time to time, we suggest you to register on our website so you can receive our emails related to promotions and offers.

Q9. What are overview courses?
A9. Overview courses are 1-2 hours short to help you decide if you want to go for the full course on that particular subject. Uplatz overview courses are either free or minimally charged such as GBP 1 / USD 2 / EUR 2 / INR 100

Q10. What are individual courses?
A10. Individual courses are simply our video courses available on Uplatz website and app across more than 300 technologies. Each course varies in duration from 5 hours uptop 150 hours. Check all our courses here https://training.uplatz.com/online-it-courses.php?search=individual

Q11. What are bundle courses?
A11. Bundle courses offered by Uplatz are combo of 2 or more video courses. We have Bundle up the similar technologies together in Bundles so offer you better value in pricing and give you an enhaced learning experience. Check all Bundle courses here https://training.uplatz.com/online-it-courses.php?search=bundle

Q12. What are Career Path programs?
A12. Career Path programs are our comprehensive learning package of video course. These are combined in a way by keeping in mind the career you would like to aim after doing career path program. Career path programs ranges from 100 hours to 600 hours and covers wide variety of courses for you to become an expert on those technologies. Check all Career Path Programs here https://training.uplatz.com/online-it-courses.php?career_path_courses=done

Q13. What are Learning Path programs?
A13. Learning Path programs are dedicated courses designed by SAP professionals to start and enhance their career in an SAP domain. It covers from basic to advance level of all courses across each business function. These programs are available across SAP finance, SAP Logistics, SAP HR, SAP succcessfactors, SAP Technical, SAP Sales, SAP S/4HANA and many more Check all Learning path here https://training.uplatz.com/online-it-courses.php?learning_path_courses=done

Q14. What are Premium Career tracks?
A14. Premium Career tracks are programs consisting of video courses that lead to skills required by C-suite executives such as CEO, CTO, CFO, and so on. These programs will help you gain knowledge and acumen to become a senior management executive.

Q15. How unlimited subscription works?
A15. Uplatz offers 2 types of unlimited subscription, Monthly and Yearly. Our monthly subscription give you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews each month. Minimum committment is for 1 year, you can cancel anytime after 1 year of enrolment. Our yearly subscription gives you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews every year. Minimum committment is for 1 year, you can cancel the plan anytime after 1 year. Check our monthly and yearly subscription here https://training.uplatz.com/online-it-courses.php?search=subscription

Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certif