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Career Path - Data Architect

Define data standards & principles for visualizing & designing an organization's enterprise data management framework & end-to-end data architecture.
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Course Duration: 300 Hours
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The Data Architect Career Path by Uplatz consists of the following courses:

1. SQL Programming with MySQL Database

2. Oracle PL-SQL Programming

3. Oracle 21c DBA

4. Talend

5. Business Intelligence & Data Analytics

6. SAP BO

7. Tableau

8. Power BI

9. Python Programming

10. R Programming

11. Data Visualization with Python

12. Data Visualization with R

13. Data Science with Python

14. Data Science with R

15. Cloud Computing Basics

 

Data Architecture refers both to the IT systems that facilitate the collection, storage, distribution, and consumption of data within an organization, and to the policies that govern how data is collected, stored, distributed, and accessed within an organization. From an IT standpoint, an organization’s data architecture typically includes data storage and warehousing systems (e.g., databases), computer networks that serve as data pipelines and provide access to stored data, and software platforms and analytics applications that process data in order to further an organization’s goals. In terms of organizational structure, data architecture may encompass personnel who have access to relevant and potentially sensitive data, policies governing data access, and the protocols for the secure distribution of data to relevant parties, including analytics specialists, operations managers, marketing departments, and others, depending on the size and type of the organization.

 

Data Architects visualize and design an organization's enterprise data management framework, aligned with enterprise strategy and business architecture. As a general rule, data architects are professions who have formal training and/or professional experience in IT management, computer programming, and data systems engineering, as well as in the processes by which data is mined, sorted, stored, and analyzed. Data architects typically interact with and respond to the needs of non-technical managers and business administrators within an organization, which may require some training in professional communication. And it may be helpful for data architects to have familiarity and/or experience with business intelligence systems, data mining tools, and data analytics operations.

Data architects design and manage vast electronic databases to store and organize data. They investigate a company’s current data infrastructure and develop a plan to integrate current systems with a desired future state. Data architects then write code to create new, secure framework for databases that may be used by hundreds or thousands of people.

 

Data Architect Roles

Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles. The data architect is responsible for visualizing and designing an organization's enterprise data management framework. This framework describes the processes used to plan, specify, enable, create, acquire, maintain, use, archive, retrieve, control, and purge data. The data architect also provides a standard common business vocabulary, expresses strategic requirements, outlines high-level integrated designs to meet those requirements, and aligns with enterprise strategy and related business architecture.

 

Uplatz provides a deep dive into Data Architecture with this course covering different technologies and tools associated with data architecture. This Data Architect Career Path program will help you become a successful data and/or solution architect in any organization.

Course/Topic 1 - 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 2 - Oracle PL/SQL - all lectures

  • Overview of PL/SQL

    • 27:21

  • Declaring PL/SQL Variables

    • 13:02

  • Writing Executable Statements

    • 13:07

  • Interacting with Oracle DB Server

    • 9:37

  • Writing Control Structure

    • 39:35

  • Working with Composite Data Types

    • 43:14

  • Using Explicit Cursors

    • 22:03

  • Handling Exceptions

    • 22:34

  • Creating Procedures

    • 15:39

  • Creating Functions

    • 12:37

  • Creating Packages

    • 10:02

  • Working with Packages

    • 10:27

  • Using Oracle-supplied Packages in Application Development

    • 05:59

  • Using Dynamic SQL

    • 11:58

  • Design considerations for PL/SQL Code

    • 13:35

  • Creating Triggers - Creating Compound, DDL, and Event Database Triggers

    • 22:37

  • Using PL/SQL Compiler

    • 16:00

  • Managing PL/SQL Code

    • 08:55

  • Managing Dependencies

    • 12:35

Course/Topic 3 - Oracle 21c DBA - all lectures

  • Episode 1 - Installation of Oracle Linux 8 on Oracle VM

    • 21:57

  • Episode 2 - Introduction to Oracle 21c Database

    • 01:20

  • Episode 3 - Oracle 21c Architecture

    • 17:46

  • Episode 4 - Practice 1 - Creating a CDB

    • 43:37

  • Episode 5 - Practice 2 - Creating PDBs from the Seed

    • 21:05

  • Episode 6 - Practice 3 - Performing Basic CDB Administration Tasks

    • 32:43

  • Episode 7 - Practice 4 - Managing Common and Local Users

    • 43:16

  • Episode 8 - Practice 5 - Backup and Recovery in CDB and PDBs

    • 38:18

  • Episode 9 - Practice 6 - Flashback and Point-in-time Recovery

    • 41:34

Course/Topic 4 - 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 5 - Business Intelligence and Data Analytics - all lectures

  • In this lecture session we discuss about Bi concepts, examples and application of business intelligence and data analytics and also cover other concepts of BI.

    • 14:48

  • In this lecture session we learn about basic concepts of BI and also cover factors of business intelligence in brief.

    • 19:22

  • In this lecture session we learn about data warehouse data access and data dashboarding and also cover presentation in BI.

    • 24:10

  • In this lecture session we learn about product database, advertise database and customer demographic database and also cover data analyst concepts.

    • 19:54

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

    • 31:10

  • In this lecture session we learn about introduction of predictive modeling and also cover functions of predictive modeling in brief.

    • 1:05:08

  • In this lecture session we learn about data related to customer services and also talk about customer relation databases in brief.

    • 32:37

  • In this lecture session we learn about introduction of NoSQL and also cover basic functions of NoSQL in business intelligence.

    • 33:46

  • In this lecture session we learn about graph stores and also talk about the advantages and disadvantages of graph stores in BI.

    • 25:52

  • In this lecture session we learn about hierarchical clustering in business intelligence and also talk about clustering factors in BI.

    • 29:58

  • In this lecture session we learn about introduction of salesforce in business intelligence and also talk about some basic uses of salesforce.

    • 34:25

  • In this lecture session we learn about introduction to NLP and also cover what is natural language processing in artificial intelligence.

    • 18:30

  • In this lecture session we learn about natural language processing speech to text conversion and also cover the importance of natural language processing.

    • 25:22

  • In this lecture session we learn about introduction of apache server in business intelligence and also talk about basics of apache server.

    • 44:24

  • In this lecture session we learn about deep drive into business intelligence and also talk about factors or deep drive in business intelligence.

    • 30:54

  • In this lecture session we learn about data warehousing in business intelligence and data analytics and also talk about factors and features of data warehousing.

    • 39:54

  • In this lecture session we learn about types of data in business intelligence and also talk about different types of data in BI.

    • 25:07

  • In this lecture session we learn about mobile BI and also talk about open source BI software replacing vendor offering.

    • 39:18

  • In this lecture session we learn about real time BI in business intelligence and also talk about factors of real time BI in brief.

    • 1:35:17

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

    • 23:43

  • In this lecture session we talk about data analytics vs business analytics and also talk about the importance of data analytics.

    • 41:04

  • In this lecture session we learn about Embedded analytics and also talk about functions of Embedded analytics in data analytics.

    • 1:02:55

  • In this lecture session we learn about collection analytics and also cover the importance of collection analytics.

    • 59:03

  • In this lecture session we learn about survival analytics and also cover duration analytics in brief.

    • 29:35

  • In this lecture session we learn about machine learning techniques and also cover the importance and factors of machine learning techniques in business intelligence.

    • 37:34

  • In this lecture session we learn about geospatial predictive analytics and also talk about functions of geospatial predictive analytics in business intelligence.

    • 1:01:31

  • In this lecture session we learn about cohort analysis in data analyst and we also cover functions and importance of cohort analysis.

    • 21:36

  • In this lecture session we learn about data mining in business intelligence and also talk about data mining functions and why we need data mining in business intelligence.

    • 45:40

  • In these lecture sessions we learn about anomaly detection and also talk about functions of anomaly detection in brief.

    • 1:00:36

  • In these lecture sessions we learn about statistically sound association and also talk about factors of statistically sound association in business intelligence.

    • 31:29

  • In this lecture session we learn about cluster analysis. We’ll cover all types of cluster analysis in brief and also cover the importance of cluster analysis in business analysis.

    • 36:21

  • In this lecture session we learn about DBSCAN in business intelligence and also talk about DBSCAN functions and importance.

    • 59:58

  • In this lecture session we learn about regression models in business intelligence and also talk about the function of regression models.

    • 31:57

  • In this lecture session we learn about extraction based summarization in business intelligence and also cover all types of summarization in data analyst.

    • 10:57

  • In this lecture session we learn about machine learning in BI and also talk about factors and importance of machine learning in brief.

    • 1:00:50

  • In this lecture session we learn about machine learning vs BI we also discuss the basic difference between machine learning and business intelligence.

    • 1:15:37

  • In this lecture session we learn about how ml can make BI better and also talk about ml basic functions.

    • 1:18:01

  • In this lecture session we learn about data warehousing and also talk about how we manage data warehousing in business intelligence.

    • 18:28

  • In this lecture session we learn about data warehousing in business intelligence and data analytics and also talk about factors and features of data warehousing.

    • 29:22

  • In this lecture session we learn about data mart in business intelligence and also talk about data mart function.

    • 32:40

  • In this lecture session we learn about data dimensions in business intelligence and also cover all types of data dimension in BI.

    • 30:31

  • In this lecture session we learn about data dimension in business intelligence and also cover functions and importance of data dimension.

    • 24:02

  • In this lecture session we learn about data vault modeling in business intelligence and also cover different types of vault modeling in brief.

    • 29:14

  • In this lecture session we learn about links and satellites and also cover the importance and factors of links and satellites in business intelligence.

    • 28:59

Course/Topic 6 - SAP BO - all lectures

  • In this video tutorial, you will get a brief introduction on SAP BO, how it came to be known as SAP BI, what are the different layers in SAP BI, characteristics in Data Warehouse, schema, master data tables and also a detailed explanation on how a student can access and work on the server for practicing his daily SAP system works.

    • 1:35:18

  • In this tutorial, you will learn how you can access the Business Object components in the SAP system through remote desktop. You will also be able to understand a detailed explanation on the different designing components. Lastly, you will get a detailed demonstration on how to work on the different design tools in the SAP system.

    • 57:33

  • In this lecture, you will be able to understand what a Designer is in the SAP BO module and how to work with the different designing tools available in the SAP system. You will also learn what is a Universe, Role of a Universe and the Semantic Layer in the initial screen of the designing tool.

    • 2:05:35

  • In this tutorial, you will learn how to create a Universe in the designing tool of the BO module, manually. This is explained in a detailed demonstration by the Instructor which will help you in getting a practical experience on the work process. Further, you will also understand what the different types of OLTP systems are and what is their role in the SAP BO design tool.

    • 1:00:08

  • In this tutorial, you will learn how you can create the characters in the SAP BO system, how IDT and UDT helps in the Online Analytic Process (OLAP). You will also learn how to create an infocube and what are the steps to be followed while creating an infocube in the SAP BW system.

    • 2:10:35

  • In this video, you will learn how to create data from scratch using the Universe Design tool in the SAP BO module. You will also learn how you can create the BSO in the system tool, using a pictorial description of the entire work process.

    • 1:30:11

  • To access anything from the ECC server, one needs to know the Transaction Codes. In this video, you will learn about these Transaction Codes or T-Codes which will be used in working on the SAP BO system module. You will also get an overview on the Object Hierarchies, which allows users in performing multi-dimensional analysis.

    • 1:24:06

  • In this tutorial, you will be learning the role of IDT and UDT in the SAP BO Design Tool. You will also learn about the types of Web Intelligence and the entire work process with a detailed demonstration from the instructor. Along with this, you will be learning on how to create reports and the different fields and attributes required in creating the reports.

    • 2:30:53

  • In this video lecture, you will learn the different formatting options available to create and modify reports in the SAP BO Design Tools. You will learn how to add rows and columns, category, margins, pictures, articles and others. Further, you will learn how to work on the sub-reports and the functionalities available in the crystal reports. Moreover, you will also get an overview on the dashboard components.

    • 2:00:16

  • In this lecture, you will learn what are the parameters to be used while working on a crystal report by a user. Also, you will see a detailed demonstration on how to work on the Table Article Label, Article Lookup and the Body of the report.

    • 1:01:37

  • In this lecture, you will learn about the different types of dashboard designing components used in the SAP BO module. Further, you will learn how to work on the SAP Lumira Design component, how to add new data set, characteristic dimensions and the different modes in the Lumira Designing Tool.

    • 1:40:21

  • In this video tutorial, you will get a detailed and practical demonstration on how to work on the Information and Quick Design Tool in the SAP BO module. You will also get some more detail concepts and work around on the SAP Lumira Designing Tool.

    • 1:30:58

  • In this tutorial, you will be learning how to connect to a SAP HANA database. Also, you will learn how to use the database and the data models. Moreover, you will also learn how to do the HANA connectivity. This whole work process will be practically demonstrated by the instructor in detail.

    • 2:23:01

  • In this video, you will learn how to access the views using Information Design Tool (IDT), which can be done using the HANA Data Acquisition Connector. You will also be learning how to assign a data source and also working on the whole of the data source work process.

    • 1:29:35

  • In this tutorial, you will learn what is a Web Application Designer (WAD) and what is its role in connecting with the BW Server. You will also learn how to work on the Basis Analysis Layout Template, which is a pre-defined standard template used for ad-hoc slicing and dicing data sources. You will also learn how to work on the attribute views.

    • 2:14:32

  • In this last video lecture, you will learn how you can connect to HANA tables and modules using IDT. You will further learn to work on the SAP HANA business layer and other concepts related to the SAP BO module.

    • 1:09:15

Course/Topic 7 - Tableau (comprehensive) - all lectures

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

    • 17:31

  • In this session we will introduce you about Tableau which is a widely used business intelligence (BI) and analytics software trusted by companies like Amazon, Experian, and Unilever to explore, visualize, and securely share data in the form of Workbooks and Dashboards. With its user-friendly drag-and-drop functionality it can be used by everyone to quickly clean, analyze, and visualize your team’s data.

    • 33:45

  • This session is all about the history of Tableau which was founded by Pat Hanrahan, Christian Chabot, and Chris Stolte from Stanford University in 2003. The main idea behind its creation is to make the database industry interactive and comprehensive.

    • 21:38

  • In this tutorial, we will discuss the Tableau interface and understand its functioning in detail. Followed by the general understanding of Tableau’s working. Along with this, we will learn the Components of Tableau Server.

    • 34:10

  • In this session, you will get to know how to use Tableau Prep Builder to clean and prepare your data, start a new flow by connecting to your data, just like in Tableau Desktop. You can also open an existing flow and pick up where you left off.

    • 43:54

  • In this video, once you have chosen the best Tableau product for you, it is time to start finding insights in your data! Much like Tableau’s suite of products, data connections come in many shapes and sizes. As of this writing, Tableau Desktop: Personal has four different types of data connections, and Tableau Desktop.

    • 33:13

  • This session teaches you about the Data blending which is a method for combining data from multiple sources. Data blending brings in additional information from a secondary data source and displays it with data from the primary data source directly in the view.

    • 30:50

  • If you are connected to a data source that has been modified, you can immediately update Tableau Desktop with the changes by selecting a data source on the Data menu and then selecting Refresh.

    • 41:22

  • In this Tableau tutorial, we are going to study about what is sorting in Tableau. We will also discuss how to use Quick Sort in Tableau. At last, we will see why is my king broken and combined filed. Tableau sort is the process of arranging or ordering the data in Ascending Order or Descending Order.

    • 19:38

  • In this video, we will show you How to perform sorting in Tableau reports with example. For this Tableau sort demo, we are going to use the report we created in our previous article.

    • 13:43

  • In this tutorial, we will show you How to perform grouping in Tableau reports with example. For this Tableau Grouping demo, we are going to use the report we created in our previous article. Tableau Grouping is the process of merging or combining two or more values for further analysis.

    • 12:18

  • In this video, we will show you How to perform grouping in Tableau reports with example? For this Tableau Grouping demo, we are going to use the report we created in our previous video.

    • 24:55

  • In this video, we will show you how to create Tableau Set, Constant Sets, and Computed Sets. First, Drag and Drop the State Name from Dimension Region to Rows Shelf and Profit from Measures region to Columns Shelf.

    • 26:50

  • In addition to a Set Action, you can also allow users to change the membership of a set by using a filter-like interface known as a Set Control, which makes it easy for you to designate inputs into calculations that drive interactive analysis. For details, see Show a set control in the video.

    • 38:06

  • In this session you begin filtering data in Tableau, it's important to understand the order in which Tableau executes filters in your workbook. Filtering is an essential part of analyzing data. This article describes the many ways you can filter data from your view. It also describes how you can display interactive filters in the view, and format filters in the view.

    • 26:07

  • In this video you will get to know about filtering which is an essential part of analyzing data. This article describes the many ways you can filter data from your view. It also describes how you can display interactive filters in the view, and format filters in the view.

    • 20:49

  • In this tutorial, we will learn about another interesting and useful feature of Tableau that is Tableau parameters. Here, we will try and gain a good understanding of the parameters in Tableau and their use in Tableau. We will start by discussing the definition of parameters followed by learning how to create parameters and use them in Tableau.

    • 31:36

  • In this session you will understand how to use parameter actions to let your audience change a parameter value through direct interaction with a viz, such as clicking or selecting a mark. You can use parameter actions with reference lines, calculations, filters, and SQL queries, and to customize how you display data in your visualizations.

    • 24:43

  • In this Tableau tutorial, we will study What is Tableau Reference Lines, functions of Reference lines in Tableau and the steps involved in creating / Adding reference lines to the Tableau Bar Chart. At last, we will how to create reference lines in Tableau with example. So, let us start Tableau Reference Lines.

    • 20:42

  • In the tutorial you will get to know how to show trend lines in a visualization to highlight trends in your data. You can publish a view that contains trend lines, and you add trend lines to a view as you edit it on the web. When you add trend lines to a view, you can specify how you want them to look and behave.

    • 25:39

  • In this Tableau tutorial, you will learn about the story which is a sequence of visualizations that work together to convey information. You can create stories to tell a data narrative, provide context, demonstrate how decisions relate to outcomes, or to simply make a compelling case.

    • 23:15

  • In this session you will understand how to Use stories to make your case more compelling by showing how facts are connected, and how decisions relate to outcomes. You can then publish your story to the web or present it to an audience. Each story point can be based on a different view or dashboard, or the entire story can be based on the same visualization seen at different stages, with different filters and annotations.

    • 41:34

  • In this video, we will show you, How to Format Tableau Dashboard Layout with an example. For this, we are going to use the below-shown dashboard. Once you created your dashboard (added required Sheets), you can use the layout tab to format those Sheets or Items as per your requirements.

    • 23:52

  • Tableau Layout Containers control the spacing between dashboard components. They allow you to format common elements and move multiple dashboard objects at the same time. 

    • 36:38

  • In our last Tableau tutorial, we discuss How to Format Tableau Dashboard Layout. Here, in this tutorial, we are going to learn about How to Tableau Interactive Dashboard with Data Granularity, Interactivity, and Intuitiveness in Tableau. In other word or in general words we can call this playing with maps in a tableau. so, let us start with How to Create Tableau Interactive Dashboard.

    • 29:40

  • This tutorial walks you through some of the most common tasks you might perform when creating maps in Tableau. You’ll learn how to connect to and join geographic data; format that data in Tableau; create location hierarchies; build and present a basic map view; and apply key mapping features along the way. If you're new to building maps in Tableau, this a great place to start.

    • 07:55

  • This tutorial describes how to create and use calculated fields in Tableau using an example. You'll learn Tableau calculation concepts, as well as how to create and edit a calculated field. You will also learn how to work with the calculation editor, and use a calculated field in the view. If you're new to Tableau calculations or to creating calculated fields in Tableau, this is a good place to start.

    • 21:08

  • You can build several different types of maps for your geographic analysis in Tableau. If you're new to maps, or simply want to take advantage of the built-in mapping capabilities that Tableau provides, you can create a simple point or filled (polygon) map.

    • 13:07

  • You can always customize a table calculation by editing it in the Table Calculations dialog box, but there are other, more specialized ways to customize a table calculation.

    • 14:15

  • This video introduces the basics of understanding calculations in Tableau. In this topic, you'll learn why and when to use calculations.

    • 14:17

  • This session explains the types of LOD expressions you can use in Tableau, as well as when to use them, and how to format them. It also uses an example to demonstrate how to create a simple LOD expression. Level of Detail expressions (also known as LOD expressions) allow you to compute values at the data source level and the visualization level. However, LOD expressions give you even more control on the level of granularity you want to compute.

    • 27:50

  • To edit a table calculation Right-click the measure in the view with the table calculation applied to it and select Edit Table Calculation. In the Table Calculation dialog box that appears, make your changes.

    • 18:31

  • Tableau can create interactive visualizations customized for the target audience. In this tutorial, you will learn about the measures, chart types and its features.

    • 15:18

  • When you save a level of detail expression, Tableau adds it to either the Dimensions or the Measures area in the Data pane. FIXED level of detail expressions can result in measures or dimensions, depending on the underlying field in the aggregate expression.

    • 37:12

  • In this Tableau tutorial, we are going to learn about using a Histogram in Tableau. Here, we will find answers to questions like what is a histogram, and how do we create it in our Tableau software.

    • 16:11

  • In this tutorial, 'Sample-Superstore.csv' is used for the demonstration. You can connect to the data source and follow the steps given in the tutorial. Tableau can create interactive visualizations customized for the target audience. In this tutorial, you will learn about the measures, chart types and its features.

    • 25:01

  • In this Tableau Tutorial, we are going to learn about an interesting chart that is a bubble chart or packed bubble chart. Here, we will learn how to create a bubble chart in Tableau in a stepwise manner. You can create your first Tableau bubble chart with us on your own device. All you need, as of now is a sample data set and Tableau software in your device.

    • 09:54

  • A histogram is a chart that displays the shape of a distribution. A histogram looks like a bar chart but groups values for a continuous measure into ranges, or bins.

    • 11:51

  • Tableau Bubble Chart is used to display the data in circles. We can define each bubble using any of our Dimension members and size by Measure value.

    • 09:54

  • In this tutorial we will learn about Tree maps which are the relatively simple data visualization that can provide insight in a visually attractive format. Use packed bubble charts to display data in a cluster of circles. Dimensions define the individual bubbles, and measures define the size and color of the individual circles.

    • 10:42

  • In this Video we will get to know about the best practices which are key to developing informative visualizations that drive your audience to act. A dashboard is successful when people can easily use it to derive answers. Even a beautiful dashboard with an interesting data source could be rendered useless if your audience can’t use it to discover insights.

    • 48:07

Course/Topic 8 - Power BI (comprehensive) - all lectures

  • Lesson 1.1 - Introduction to Power BI - theory

    • 46:46

  • Lesson 1.2 - Introduction to Power BI - practical

    • 19:15

  • Lesson 2.1 - Connecting to a Database - theory

    • 27:14

  • Lesson 2.2 - Connecting to a Database - practical

    • 27:00

  • Lesson 3.1 - Working with Dates - theory

    • 21:36

  • Lesson 3.2 - Working with Dates - practical

    • 23:36

  • Lesson 4.1 - Conditional Columns - theory

    • 12:14

  • Lesson 4.2 - Conditional Columns - practical

    • 19:26

  • Lesson 5.1 - Merge Queries - theory

    • 34:11

  • Lesson 5.2 - Merge Queries - practical

    • 32:28

  • Lesson 6.1 - Creating Calculated Columns - theory

    • 15:39

  • Lesson 6.2 - Creating Calculated Columns - practical

    • 40:58

  • Lesson 7.1 - Creating Calculated Measures - theory

    • 55:41

  • Lesson 7.2 - Creating Calculated Measures - practical

    • 39:54

  • Lesson 8.1 - Creating and Managing Hierarchies - theory

    • 35:19

  • Lesson 8.2 - Creating and Managing Hierarchies - practical

    • 29:52

  • Lesson 9.1 - Manually typing in a Data Table - theory

    • 18:45

  • Lesson 9.2 - Manually typing in a Data Table - practical

    • 14:25

  • Lesson 10.1 - Include and Exclude - theory

    • 19:09

  • Lesson 10.2 - Include and Exclude - practical

    • 13:14

  • Lesson 11.1 - Pie Chart and Tree Map - theory

    • 42:53

  • Lesson 11.2 - Pie Chart and Tree Map - practical

    • 23:16

  • Lesson 12.1 - Filters - theory

    • 27:16

  • Lesson 12.2 - Filters - practical

    • 16:46

  • Lesson 13.1 - Slicers - Date Slicers - theory

    • 51:03

  • Lesson 13.2 - Slicers - Date Slicers - practical

    • 24:54

  • Lesson 13.3 - Slicers - Date Slicers - practical

    • 16:20

  • Lesson 14.1 - Map Visualization - theory

    • 32:34

  • Lesson 14.2 - Map Visualization - practical

    • 29:16

  • Lesson 15.1 - Tables and Matrix - theory

    • 38:47

  • Lesson 15.2 - Tables and Matrix - practical

    • 36:30

  • Lesson 16.1 - Table Styles - theory

    • 08:56

  • Lesson 16.2 - Table Styles - practical

    • 11:40

  • Lesson 17.1 - Waterfall Gauge Card and KPI - theory

    • 38:18

  • Lesson 17.2 - Waterfall Gauge Card and KPI - practical

    • 53:19

  • Lesson 18.1 - Shapes - Text Boxes - Images - theory

    • 12:16

  • Lesson 18.2 - Shapes - Text Boxes - Images - practical

    • 24:54

  • Lesson 19.1 - Page Layout and Formatting - theory

    • 16:31

  • Lesson 19.2 - Page Layout and Formatting - practical

    • 11:03

Course/Topic 9 - Python Programming - all lectures

  • In this lecture session we learn about introduction to python programming for beginners and also talk about features of python programming.

    • 10:21

  • In this lecture session we learn about basic elements of python in python programming and also talk about features of elements of python.

    • 19:37

  • In this lecture session we learn about installation of python in your system and also talk about the best way of installation of python for beginners.

    • 13:18

  • In this lecture session we learn about input and output statements in python programming and also talk about features of input and output statements.

    • 24:05

  • In this lecture session we learn about data types in python programming and also talk about all the data types in python programming.

    • 23:05

  • In this lecture session we learn about operators in python and also talk about how we use operators in python programming.

    • 47:07

  • In this lecture session we learn about different types of operators in python programming and also talk about features of operators in python.

    • 29:47

  • In this lecture session we learn about type conversion in python programming and also talk about features of type conversion in python.

    • 23:39

  • In this lecture session we learn about basic programming in python programming for beginners.

    • 15:56

  • In this lecture session we learn about features of basic programming in python and also talk about the importance of programming in python.

    • 05:13

  • In this lecture session we learn about math modules in python programming and also talk about features of math modules in python.

    • 26:43

  • In this lecture session we learn about conditional statements in python and also talk about conditional statements in python programming.

    • 28:24

  • In this lecture session we talk about basic examples of conditional statements in python.

    • 19:27

  • In this lecture session we learn about greater and less then conditional statements in python programming.

    • 13:39

  • In this lecture session we learn about nested IF Else statements and also talk about features of nested IF else statements.

    • 11:04

  • In this lecture session we learn about looping in python in programming for beginners and also talk about looping in python.

    • 25:06

  • In this lecture session we learn about break and continue keywords and also talk about features of break continue keywords.

    • 20:48

  • In this lecture session we learn about prime number programs in python and also talk about functions of prime number programs in python.

    • 17:31

  • In this lecture session we learn about while loop in python programming and also talk about features of while loop in python.

    • 35:35

  • In this lecture session we learn about nested For loop in python programming and also talk about features of nested For loop.

    • 12:34

  • In this lecture session we learn about features of nested for loop in python and also talk about the importance of nested For loop in python.

    • 12:49

  • In this lecture session we learn about functions in python and also talk about different types of functions in pythons.

    • 19:28

  • In this lecture session we learn about passing arguments to functions in python programming and also talk about features of passing arguments to functions

    • 08:59

  • In this lecture session we learn about return keywords in python and also talk about features of return keywords in python.

    • 12:16

  • In this lecture session we learn about calling a function in python programming and also talk about calling a function.

    • 15:07

  • In this lecture session we learn about factors of calling a function in python programming and also talk about features of calling a function.

    • 20:17

  • In this lecture session we learn about a program to swap 2 numbers using calling a function in python programming.

    • 19:27

  • In this lecture session we learn about functions of arbitrary arguments in python programming and also talk about features of arbitrary arguments.

    • 10:34

  • In this lecture session we learn about functions keywords arguments in python programming and also talk about features of keyword arguments.

    • 06:55

  • In this lecture session we learn about functions default arguments in python programming and also talk about features of default argument.

    • 06:57

  • In this lecture session we learn about global and local variables in python programming and also talk about features of global and local variables.

    • 19:37

  • In this lecture session we learn about global and local keywords and also talk about features of global and local keywords.

    • 10:44

  • In this lecture session we learn about strings in python programming and also talk about features of string in python.

    • 17:42

  • In this lecture session we learn about string methods in python programming and also talk about features of string methods in python.

    • 21:53

  • In this lecture session we learn about string functions in python and also talk about features of strings functions in python.

    • 28:02

  • In this lecture session we learn about string indexing in python programming and also talk about features of string indexing in python programming.

    • 13:51

  • In this lecture session we learn about introduction of lists in python programming and also talk about features of introduction to lists.

    • 06:31

  • In this lecture session we learn about basics of lists python programming and also talk about features of basics of lists in python.

    • 33:09

  • In this lecture session we learn about list methods and also talk about features of list method python programming.

    • 32:43

  • In this lecture session we learn about linear search on list and also talk about features of linear search on list in brief.

    • 23:20

  • In this lecture session we learn about the biggest and smallest number of the list and also talk about features of MAX and Min in a list.

    • 14:40

  • In this lecture session we learn about the difference between 2 lists in python programming and also talk about features of 2 lists.

    • 13:22

  • In this lecture session we learn about tuples in python programming and also talk about tuples in python programming.

    • 20:19

  • In this lecture session we learn about introduction to sets in python and also talk about functions of introduction to sets in python.

    • 32:43

  • In this lecture session we learn about set operations in python programming and also talk about features of set operation in brief.

    • 26:56

  • In this lecture session we learn about set examples and also talk about features set examples.

    • 11:05

  • In this lecture session we learn about introduction to dictionaries in python programming and also talk about featured dictionaries.

    • 14:47

  • In this lecture session we learn about creating and updating dictionaries in python programming and also talk about features of creating and updating dictionaries.

    • 32:49

  • In this lecture session we learn about deleting items in a dictionary in python programming and also talk about features of deleting items in a dictionary.

    • 08:06

  • In this lecture session we learn about values and items in a dictionary in python programming and also talk about features of values and items in the dictionary.

    • 13:14

  • In this lecture session we learn about dictionary methods in python programming and also talk about features of dictionary methods.

    • 18:46

  • In this lecture session we learn about built in methods in python programming and also talk about features of built in methods in python.

    • 20:25

  • In this lecture session we learn about lambda functions and also talk about features of lambda function in python programming.

    • 15:29

  • In this lecture session we learn about file handling in python programming and also also talk about the importance of file handling in python.

    • 15:58

  • In this lecture session we learn about file handling in python programming and also talk about features of file handling in python.

    • 36:13

  • In this lecture session we learn about exception handling in python and also talk about features of exception handling in python.

    • 08:46

  • In this lecture session we learn about exception handling examples in python programming.

    • 25:04

  • In this lecture session we learn about python programs in python programming and also talk about features of python programs

    • 18:40

  • In this lecture session we learn about the program of printing odd numbers in python programming and also talk about the best way of printing.

    • 10:46

  • In this lecture session we learn about counting the number of vowels and consonants in a string and also talk about features of these programs.

    • 21:38

  • In this lecture session we learn about python programs of swapping two numbers in a list by taking indexes as parameters.

    • 14:08

  • In this lecture session we learn about bubble sort and also talk about features of bubble sort in brief.

    • 35:36

  • In this lecture session we learn about operator precedence in python and also talk about features of operator precedence in python.

    • 14:51

  • In this lecture session we learn about operator precedence in python and also talk about features of operator precedence types.

    • 11:28

  • In this lecture session we learn about recursion in python and also talk about features of recursion in python.

    • 22:15

  • In this lecture session we learn about binary search in python and also talk about features of binary search in python programming.

    • 23:18

  • In this lecture session we learn about binary search in python and also talk about the importance of binary search in python.

    • 35:04

  • In this lecture session we learn about object oriented programming and also talk about features of object oriented programming in brief.

    • 21:52

  • In this lecture session we learn about factors and types of object oriented programming in python programming.

    • 17:41

  • In this lecture session we learn about OOPS and procedural programming and also talk about features of OOPS and procedural programming in OOPS.

    • 06:36

  • In this lecture session we learn about OOPS programs in python and also talk about the importance of OOPS.

    • 27:50

  • In this lecture session we learn about inheritance in python programming and also talk about features of inheritance.

    • 37:24

  • In these lecture sessions we learn about features of object creation in python programming and also talk about object creation in python.

    • 24:10

  • In this lecture session we learn about OOPS terminology and functions and also talk about features of OOPS terminology and functions.

    • 24:41

  • In this lecture session we learn about built in class attributes and garbage collection in python programming.

    • 27:26

  • In this lecture session we learn about inheritance in python and also talk about features of inheritance in python.

    • 19:02

  • In this lecture session we learn about the importance of inheritance in python programming and also talk about functions of inheritance.

    • 29:26

  • In this lecture session we learn about programs in inheritance in python programming and also talk about features of inheritance in python.

    • 31:43

  • In this lecture session we learn about polymorphism in python programming polymorphism and also talk about polymorphism in python.

    • 24:47

  • In this lecture session we learn about features of polymorphism in python and also talk about the importance of polymorphism in python.

    • 14:01

  • In this lecture session we learn about the time module in python and also talk about features time module in python in features.

    • 36:22

  • In this lecture session we learn about the importance of time modules in python time module in python in brief.

    • 44:51

  • In this lecture session we learn about the calendar module in python programming in brief.

    • 32:04

  • In these lecture sessions we learn about calendar methods in python programming and also talk about the importance of calendar methods.

    • 37:03

  • Class 28.1 - Boolean in Python

    • 09:32

  • In this lecture session we learn about python iterators and also talk about features of python iterators in brief.

    • 09:30

  • In this lecture session we learn about python programs and summary in python programming and also talk about python programs.

    • 46:37

  • In this lecture sessions we learn about python programs and also talk about features of python programs and summary.

    • 23:27

Course/Topic 10 - R Programming - all lectures

  • In this lecture session we learn about basic introduction of R programming for beginners and also talk about basic functions of R programming for beginners.

    • 14:34

  • In this tutorial we learn about how we install r programming in our software and also talk about the best way of installing R programming for beginners.

    • 08:50

  • In this lecture session we learn about R's basic data structures including the vector, list, matrix, data frame, and factors. Some of these structures require that all members be of the same data type (e.g. vectors, matrices) while others permit multiple data types (e.g. lists, data frames). Objects may have attributes, such as name, dimension, and class.

    • 07:10

  • In this lecture session we learn about A vector is the basic data structure in R, or we can say vectors are the most basic R data objects.

    • 04:41

  • In this lecture session we learn about R is an ideal tool when it comes to data wrangling. It allows the usage of several preprocessed packages that makes data wrangling a lot more easier. This is one of the main reasons as to why R is preferred in the Data Science community.

    • 19:10

  • In this lecture session we learn about R packages are a collection of R functions, compiled code and sample data. They are stored under a directory called "library" in the R environment. By default, R installs a set of packages during installation. More packages are added later, when they are needed for some specific purpose.

    • 11:25

  • In this tutorial we learn about R is an open-source programming language that is widely used as a statistical software and data analysis tool.

    • 22:33

  • In this lecture session we learn that R can be used as a powerful calculator by entering equations directly at the prompt in the command console. Simply type your arithmetic expression and press ENTER. R will evaluate the expressions and respond with the result.

    • 18:48

  • In this tutorial we learn about Conditional statements are those statements where a hypothesis is followed by a conclusion. It is also known as an " If-then" statement.

    • 28:48

  • In this tutorial we learn about In coding, you ask your computer to check conditions by writing conditional statements. Conditional statements are the way computers can make decisions.

    • 16:15

  • In this lecture session we learn about It is a type of control statement that enables one to easily construct a loop that has to run statements or a set of statements multiple times. For loop is commonly used to iterate over items of a sequence.

    • 25:02

  • In this lecture session we learn about Repeat loop, unlike other loops, doesn't use a condition to exit the loop instead it looks for a break statement that executes if a condition within the loop body results to be true.

    • 15:32

  • In this lecture session we learn that Sum of n natural numbers can be defined as a form of arithmetic progression where the sum of n terms are arranged in a sequence with the first term being.

    • 07:17

  • In this lecture session we learn about The formula to find the sum of n terms in AP is Sn = n/2 (2a+(n−1)d), in which a = first term, n = number of terms, and d = common difference between consecutive terms.

    • 05:52

  • In this tutorial we learn about A switch statement that allows a variable to be tested for equality against a list of values. Each value is called a case, and the variable being switched on is checked for each case.

    • 33:57

  • In this lecture session we learn about Data preprocessing, a component of data preparation, describing any type of processing performed on raw data to prepare it for another data processing procedure.

    • 1:01:55

  • In this tutorial we learn about Data preprocessing is essential before its actual use. Data preprocessing is the concept of changing the raw data into a clean data set. The dataset is preprocessed in order to check missing values, noisy data, and other inconsistencies before executing it to the algorithm.

    • 37:44

  • In this lecture session we learn about Factor in R is a variable used to categorize and store the data, having a limited number of different values. It stores the data as a vector of integer values. Factor in R is also known as a categorical variable that stores both string and integer data values as levels.

    • 37:01

  • In this tutorial we learn about A data frame is a table or a two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.

    • 37:47

  • In this tutorial we learn about In R we use merge() function to merge two dataframes in R. This function is present inside the join() function of the dplyr package.

    • 20:37

  • In this lecture session we learn about The R merge function allows merging two data frames by common columns or by row names. This function allows you to perform different database (SQL) joins, like left join, inner join, right join or full join, among others.

    • 25:10

  • In this tutorial we learn about The two data frames must have the same variables, but they do not have to be in the same order.

    • 23:49

  • In this lecture session we learn about merge is a generic function whose principal method is for data frames: the default method coerces its arguments to data frames and calls the "data. frame" method. By default the data frames are merged on the columns with names they both have, but separate specifications of the columns can be given by.

    • 10:08

  • In this lecture session we learn about The functions which are already created or defined in the programming framework are known as a built-in function. R has a rich set of functions that can be used to perform almost every task for the user.

    • 14:32

  • In this lecture session we learn about Melting and Casting are one of the interesting aspects in R programming to change the shape of the data and further, getting the desired shape.

    • 11:35

Course/Topic 11 - 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 12 - 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 13 - 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 14 - 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 15 - 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 Objectives Back to Top

 

This syllabus provides a comprehensive overview of the key topics typically included in a course designed to prepare individuals for a career as a Data Architect, emphasizing both technical skills in database management, ETL processes, and data integration, as well as proficiency in business intelligence tools and data analytics techniquesThe "Career Path: Business Architect" course is designed to equip participants with the essential skills and knowledge necessary to excel in the role of a Business Architect, focusing on key areas such as business intelligence, data analytics, cloud computing, programming languages, data visualization, and project management. This course is ideal for aspiring Business Architects, data analysts, business intelligence professionals, project managers, and individuals looking to enhance their skills in leveraging technology for business strategy and operational excellence. By the end of the course, participants will be able to Master Business Intelligence and Data Analytics, Understand Cloud Computing Basics, Develop Proficiency in Python Programming and to Gain Skills in R Programming.

 

-Key Course Objectives-

1.SQL basics and advanced querying techniques                           

2.Database design and normalization                                           

3.PL/SQL fundamentals and syntax                                             

4.Stored procedures, functions, and triggers.           

5.Introduction to Oracle database administration                 

6.Installation, configuration, and maintenance                             

7.Overview of Talend and ETL (Extract, Transform, Load) processes                                                                                   

8.Data integration and workflow orchestration                           

9.Concepts and principles of business intelligence (BI)               

10.Data warehouse architecture and design                           

11.SAP BusinessObjects architecture and components

12.Creating and managing reports and dashboar                     

13.Introduction to Tableau and data visualization principles

14.Building interactive dashboards and visualizations         

15.Power BI overview and ecosystem                                             

16.Data modeling and transformation                             

17.Python basics and programming fundamentals                       

18.Data analysis and manipulation with Python libraries (NumPy, Pandas)                                                                               

19.Introduction to R programming language                               

20.Statistical analysis and data visualization                         

21.Visualizing data using Python libraries (Matplotlib, Seaborn)

22.Interactive visualizations with Plotly and Bokeh                       

23.Data visualization techniques using ggplot2 in R

 

This syllabus provides a comprehensive overview of the key topics typically included in a course designed to prepare individuals for a career as a Data Architect, emphasizing both technical skills in database management, ETL processes, and data integration, as well as proficiency in business intelligence tools and data analytics techniques.

 

 

 

Course Syllabus Back to Top

Course Title: Career Path in Data Architecture

Module 1: Introduction to Data Architecture

This module provides a foundational overview of data architecture, defining its role in the broader context of data management and analytics. Students will learn about the significance of data architects in organizations and how they contribute to effective data governance and strategy. The module will introduce key concepts such as data modeling, data integration, and the importance of designing data systems that support business objectives.

 

Module 2: Educational Pathway and Certification Requirements

In this module, students will explore the educational requirements for becoming a data architect. A degree in computer science, information systems, or a related field is typically essential. The module will also cover various certification options, such as Certified Data Management Professional (CDMP) and AWS Certified Data Analytics. Students will learn about the value of hands-on experience and internships in building a strong foundation for their careers.

 

Module 3: Core Skills and Competencies

This module focuses on the essential technical and soft skills necessary for success as a data architect. Key technical skills include proficiency in database management systems (DBMS), data modeling tools, and big data technologies. Additionally, students will explore critical soft skills such as communication, problem-solving, and collaboration, as these are vital for working with cross-functional teams and conveying technical concepts to non-technical stakeholders.

 

Module 4: Data Modeling and Database Design

In this module, students will gain in-depth knowledge of data modeling techniques and database design principles. Topics will include conceptual, logical, and physical data modeling, as well as normalization and denormalization processes. The module will emphasize the importance of creating efficient and scalable database architectures that support data integrity and accessibility, with practical exercises to apply these concepts.

 

Module 5: Data Integration and ETL Processes

This module delves into data integration techniques and the Extract, Transform, Load (ETL) processes essential for data architects. Students will learn about various data integration tools and frameworks, including data warehousing concepts. The module will cover best practices for designing ETL workflows that ensure data quality and consistency across disparate systems, along with hands-on projects to reinforce learning.

 

Module 6: Big Data Technologies and Cloud Data Solutions

In this module, students will explore the landscape of big data technologies and cloud-based data solutions. The module will cover popular big data frameworks, such as Apache Hadoop and Apache Spark, and how they are integrated into data architectures. Additionally, students will learn about cloud data services offered by major providers like AWS, Azure, and Google Cloud, focusing on how to leverage these technologies for scalable data solutions.

 

Module 7: Data Governance and Security

This module emphasizes the importance of data governance and security in data architecture. Students will explore frameworks for managing data quality, privacy, and compliance with regulations like GDPR and CCPA. The module will also cover security best practices for protecting sensitive data and ensuring that data architectures are designed with security and compliance in mind.

 

Module 8: Career Development and Networking

In this module, students will learn strategies for advancing their careers as data architects. The module will explore various career paths within data management, such as Data Engineer, Data Analyst, and Chief Data Officer. Additionally, students will understand the importance of networking, mentorship, and continuous learning through professional organizations, online communities, and industry events to enhance their career opportunities.

 

Module 9: Future Trends in Data Architecture

In the final module, students will examine emerging trends and future directions in data architecture. Topics will include the rise of real-time data processing, the impact of artificial intelligence and machine learning on data architecture, and the growing importance of data analytics in business decision-making. The module will encourage students to think critically about how these trends will shape the role of data architects and the skills required to remain competitive in the field.

 

Course Delivery and Assessment

1).Format: The course will be delivered through a mix of online lectures, hands-on labs, interactive workshops, and guest speaker sessions from industry professionals.

2).Assessment: Students will be evaluated through quizzes, practical assignments, and a capstone project that involves designing a data architecture solution for a real-world scenario.

Learning Outcomes

By the end of the course, students will be able to:

1).Understand the fundamentals of data architecture and its significance in organizations.

2).Identify the educational pathways and certification requirements for data architects.

3).Develop the core technical and soft skills necessary for the profession.

4).Design efficient data models and database architectures.

5).Implement data integration and ETL processes effectively.

6).Leverage big data technologies and cloud solutions in data architecture.

7).Recognize the importance of data governance and security in data management.

8).Plan for career development and networking opportunities in the field.

9).Analyze future trends and their implications for data architecture.

 

Certification Back to Top

The Data Architect EngineerCertification ensures you know planning, production and measurement techniques needed to stand out from the competition. 

Data architects are IT professionals who leverage their computer science and design skills to review and analyze the data infrastructure of an organization, plan future databases, and implement solutions to store and manage data for organizations and their users.

To be a Data Architect, the bare minimum qualification requirement is a bachelor's degree in either computer science, computer engineering, or a related field.

The Certified Analytics Professional (CAP) certification is a trusted, independent verification of the critical technical expertise and related soft skills possessed by accomplished analytics and data science professionals, and valued by analytics-oriented organizations.

Uplatz online training guarantees the participants to successfully go through the Data Architectcertification 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 Data Architectonline course.

Career & Jobs Back to Top

 

 

Data architects play a crucial role in designing and managing the complex data systems that underpin modern organizations. Here's a breakdown of the essential skills they need:

Technical Skills

1).Database Management

a.Expertise in relational database management systems (RDBMS), particularly SQL (e.g., MySQL, Postgres, Oracle, SQL Server).

b.Familiarity with NoSQL databases (e.g., MongoDB, Cassandra) for handling unstructured data.

c.Ability to design and optimize database schemas, create and execute queries, and manage data integrity.

 

2).Data Modeling

a.Proficiency in data modeling techniques (e.g., entity-relationship diagrams, data flow diagrams) to create logical and physical data models.

b.Understanding of data normalization and denormalization principles for efficient storage and retrieval.

 

3).Data Warehousing and Business Intelligence

a.Knowledge of data warehousing architecture (e.g., ETL processes, dimensional modeling), data marts, and OLAP cubes.

b.Experience with BI tools (e.g., Tableau, Power BI) for data visualization and analysis.

 

4).Programming Languages

a.Strong foundations in programming languages like Python, Java, or C# for scripting, data manipulation, and integration tasks.

 

5).Big Data Technologies

a.Familiarity with Hadoop ecosystem tools (e.g., MapReduce, Hive, HBase, Spark) for handling large-scale data processing.

b.Understanding of cloud-based data platforms (e.g., AWS, Azure, GCP) for data storage and analytics.

 

Soft Skills

1).Communication and Collaboration

a.Ability to communicate complex technical concepts to both technical and non-technical audiences effectively.

b.Strong teamwork skills to collaborate with data engineers, analysts, developers, and business stakeholders.

 

2).Problem-Solving and Critical Thinking

a.Ability to identify and solve data-related challenges, analyze performance bottlenecks, and recommend solutions.

b.Adaptability to changing technologies and business requirements.

 

3).Business Acumen

a.Understanding of business processes and how data can be leveraged to drive strategic decision-making.

b.Ability to align data architecture with business goals and objectives.

 

Additional Valuable Skills

1).Data Governance: Expertise in data governance principles and best practices to ensure data quality, security, and compliance.

2).Machine Learning: Understanding of machine learning concepts and how to integrate ML models into data architectures.

3).Cloud Computing: Experience with cloud-based data platforms and services (e.g., AWS, Azure, GCP).

4).Agile Methodologies: Familiarity with agile development practices for delivering data solutions iteratively.

 

Career Path & Job Scope for a Data Architect

Data architects are in high demand, and their careers offer exciting opportunities for growth and advancement. Here's an overview of the potential trajectories and job scopes you can explore:

1. Data Architect Path

1).Entry-Level: Junior Data Architect, Data Warehouse Architect, Data Modeling Specialist

2).Mid-Level: Lead Data Architect, Enterprise Data Architect, Chief Data Architect

3).Senior-Level: Chief Data Officer (CDO), Head of Data Governance, Director of Data Engineering

 

2. Specialized Data Architect Roles

1).Big Data Architect: Focuses on designing and managing big data infrastructure and pipelines.

2).Cloud Data Architect: Specializes in cloud-based data platforms and solutions.

3).Business Intelligence Architect: Designs and implements BI solutions to support data-driven decision-making.

4).Security and Privacy Architect: Ensures data security and compliance within the data architecture.

 

3. Non-Technical Career Paths

1).Data Governance Specialist: Develops and enforces data governance policies and procedures.

2).Data Strategy Consultant: Advises organizations on data strategy and implementation.

3).Solution Architect: Designs and implements data-driven solutions for specific business problems.

 

The specific job scope of a data architect can vary depending on the organization, industry, and level of experience. However, some common responsibilities include:

1).Designing and implementing data architectures: This involves defining data models, choosing technologies, and building data pipelines.

2).Collaborating with stakeholders: Data architects work closely with business users, data engineers, and IT teams to ensure data meets business needs.

3).Developing and enforcing data governance policies: This includes ensuring data quality, security, and compliance.

4).Staying up-to-date with the latest technologies: The data landscape is constantly evolving, so data architects need to be continuous learners.

 

Overall, a career as a data architect offers a dynamic and rewarding path for those with a passion for data and technology. The diverse range of specializations and career options ensures there's something for everyone, making it a truly exciting field to be in.

 

 

 

Interview Questions Back to Top

Q1. Data Science Roles

Ans-

Data Architect

Data Engineer

Data Analyst

Data Scientist

Data Warehouse Solutions

Extractions, Transformation and Load(ETL)

Data Collection and Processing

Data Cleansing and Processing

Extractions, Transformation and Load(ETL)

Installing Data Warehousing Solutions

Programming

Predictive modeling

Data Architecture Development

Data Modeling

Machine Learning

Machine Learning

Data Modeling

Data Architecture Construction And Development

Data munging

Identifying Questions

 

Database Architecture Testing

Data Visualization

Running Queries

 

 

Applying Statistical Analysis

Applying Statistical Analysis

 

 

 

Correlating Disparate Data

 

 

 

Story Telling and Visualization

 

Q2. Who is a data architect, please explain?

Ans-The individual who is into the data architect role is a person who can be considered as a data architecture practitioner. So when it comes to data architecture it includes the following stages:

1).Designing

2).Creating

3).Deploying

4).Managing

All of these activities are carried out with the organization's data architecture.With their help and skill set, the organization can take a constructive decision of how the data is stored, how the data is consumed, and how the data is integrated into different IT systems. In a sense, this process is closely aligned with business architecture, because they should be aware of this process so that the security policies are also taken into consideration.

 

Q3. What are the fundamental skills of a Data Architect?

Ans-The fundamental skills of a Data Architect are as follows:

1).The individual should possess knowledge about data modeling in details

2) .Physical data modeling concepts

3). Should be familiar with ETL process

4).Should be familiar with Data warehousing concepts

5).Hands-on experience with data warehouse tools and different software

6).Should have experience in terms of developing data strategies

7).Build data policies and plans for executions

 

Q4.What is a data block and what is a data file? Please explain briefly?

Ans-A data block is nothing but a logical space where the Oracle database data is stored.A data file is nothing but a file where all the data is available. For every Oracle database, we will be having one or more data files associated.

 

Q5. What is cluster analysis? What is the purpose of cluster analysis?

Ans-Cluster analysis is defined as a process where an object is defined without giving any label to it. It uses statistical data analysis techniques and processes the data mining job. Using cluster analysis, an iterative process of knowledge discovery is processed in the form of trails.

The purpose of cluster analysis:

1).It is scalable

2).It can deal with a different set of attributes

3).High dimensionality

4).Interpretability

Watch this video on “Top 10 Highest Paying IT Jobs in 2021” and know how to get into these job roles.

 

Q6. What is virtual Data warehousing?

Ans-A virtual data warehouse provides a view of completed data. Within Virtual data warehousing, it doesn’t have any historical data and it can be considered as a logical data model which has the metadata. A virtual data warehouse is a perfect information system where it acts as an appropriate analytical decision-making system.

It is one of the best ways of portraying raw data in the form of meaningful data for executive users which makes business sense and at the same time it provides suggestions at the time of decision making.

 

Q7.What is a snapshot with reference to the data warehouse?

Ans-As the name itself implies, the snapshot is nothing but a set of complete data visualization when a data extraction is executed. The best part is that it uses less space and it can be easily used to take backup and also the data can be restored quickly from a snapshot.

 

Q8. What is XMLA?

Ans-XMLA is nothing but XML for analysis purposes. This is considered as a standard for access to data in OLAP. XMLA actually uses discover and execute methods. So Discover method actually is used to fetch the information from the internet and execute method is used for the applications to execute against all the data sources that are available.

 

Q9.What is the main difference between view and materialized view?

Ans-The main difference between the view and the materialized view is as follows:

View

1).Data representation is provided by a view where the data is accessed from its table.

2).The view has a logical structure that does not occupy space

3).All the changes are affected in the corresponding tables.

Materialized View:

1).Within materialized view, pre-calculated data is available

2).The materialized view has a physical structure that does occupy space

3).All the changes are not reflected in the corresponding tables.

 

Q10. What is the junk dimension?

Ans-A junk dimension is nothing but a dimension where a certain type of data is stored which is not appropriate to store in the schema. The nature of the junk dimension is usually a Boolean has flag values.

A single dimension is formed by a group of small dimensions got together. This can be considered as a junk dimension.

 

 

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