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Career Path - Data Strategist

Unlock the power of Data for strategic decision-making & business success. Deep-dive into data collection, analysis, interpretation, and visualization
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Save 68% Offer ends on 30-Jun-2024
Course Duration: 250 Hours
Preview Career Path - Data Strategist course
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Courses included in the Data Strategist Career Path program by Uplatz are:

1. Business Intelligence and Data Analytics

2. Data Science with Python

3. Data Science with R

4. Data Visualization with Python

5. Data Visualization with R

6. Machine Learning

7. SAP BPC (Business Planning and Consolidation)

8. SAP MDG (Master Data Governance)

9. Cloud Computing Fundamentals

10. SQL Programming

 

A data strategist is a professional who combines expertise in data analysis, business strategy, and technology to develop and execute data-driven strategies that align with an organization's goals and objectives. Their primary role is to leverage data as a strategic asset to drive decision-making, operational efficiency, and business growth.

 

Roles and responsibilities of a data strategist include:

1. Strategic Planning: Developing a data strategy that aligns with the organization's overall business goals and objectives.

2. Data Governance: Establishing data governance frameworks, policies, and standards to ensure data quality, security, and compliance.

3. Data Analysis: Utilizing advanced analytical techniques to uncover insights and trends within large datasets, helping to inform strategic decisions.

4. Business Intelligence: Designing and implementing business intelligence solutions to provide stakeholders with timely and actionable insights.

5. Predictive Modeling: Building predictive models and utilizing machine learning algorithms to forecast future trends and outcomes.

6. Data Infrastructure: Collaborating with IT teams to design and maintain scalable data infrastructure, including data warehouses and data lakes.

7. Stakeholder Collaboration: Working closely with various departments and stakeholders to understand their data needs and provide solutions that support their objectives.

8. Performance Metrics: Defining key performance indicators (KPIs) and metrics to measure the effectiveness of data-driven strategies.

9. Innovation: Identifying emerging technologies and trends in data analysis and recommending their application to improve business processes.

10. Change Management: Guiding the organization through cultural shifts towards a more data-driven decision-making approach.

11. Ethical Considerations: Ensuring that data collection, analysis, and usage align with ethical and legal standards, respecting user privacy and data security.

12. Continuous Improvement: Regularly evaluating the effectiveness of data strategies and making adjustments based on feedback and outcomes.

13. Communication: Effectively communicating complex data concepts and insights to non-technical stakeholders.

14. Training and Education: Providing training and education to team members and colleagues on data-related concepts and tools.

15. Risk Management: Identifying potential risks associated with data strategy implementation and developing mitigation plans.

In essence, a data strategist plays a pivotal role in bridging the gap between data analysis and business strategy, helping organizations make informed decisions, optimize operations, and gain a competitive edge in today's data-driven landscape.

 

Data strategists need the technical skills to understand data. It isn’t necessarily about coding lots of Python every day or discussing the minutiae of a particular regression technique. Instead, it’s more about having the intuition to know which datasets are likely to be valuable for the firm’s trading style and to leave those which are likely irrelevant. Data strategists also need excellent soft skills, given that a data strategist acts as a bridge between the outside firms and internal clients such as data scientists and portfolio managers.

Course/Topic 1 - 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 2 - 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