Data Analytics (Master's Program)

Engaging with data using the industry-standard spreadsheet tool. You can use it to enter, manage, manipulate, analyze, and visualize data.

Calculations, fundamental functions, graphing, formatting, and printing.

• Functions like SUMIFs and VLOOKUP.

• Using pivot tables, summarize data.

• Database sorting and filtering. 

• Text manipulation: split and connect text, dropdown menus.

• Advanced database features like MATCH and INDEX.

• Build simple macros.

• For goal-seeking and data tables, use what-if analysis.

Data analytics is the process of drawing insightful conclusions from unstructured data

The students will gain knowledge of how to manage and optimize the analytics value chain, which includes gathering and extracting the appropriate values, choosing the proper data processing, and integrating the data from different sources using programming languages like Python. . It is the science of gathering data and discovering insightful patterns, trends, and other useful information.

1. Data analytics fundamentals.

2. How to utilize descriptive and inferential statistics. 

3. Using data for forecasting and decision-making.

4. Today's world and the role of big data

A general-purpose programming language that is useful for creating machine learning algorithms, automating processes, developing apps, and analyzing and visualizing data.

Discover how to manipulate, examine, and visualize complicated datasets using robust open-source Python tools like Git, Pandas, and Matplotlib.

1. Describe the fundamentals of Python, including types, expressions, and variables.

2. Apply Python programming using Branching, Loops, Functions, Objects & Classes.

3. Control flow programming and conditional statements

4. Object Oriented concepts in details -reusability of code .

5. Data Structures understanding in Python including Lists, Tuples, Dictionaries, Sets.

6. Using Matplotlib -Build graphs and visualizations in python

7. using scikit-learn, Make predictions with linear regression

8. Work with data in Python using Pandas and NumPy libraries and collect data using API's and web scraping

SQL is a language that is used to communicate with databases. It is an abbreviation for Structured Query Language, which is a method of querying (questioning) the information stored in a database.

1. Getting Started and Selecting & Retrieving Data with SQL

2. Filtering, Sorting, and Calculating ,summarize Data with SQL

3. Identify a subset of data needed from a column or set of columns and write a SQL query to limit to those results

4. Subqueries and Joins in SQL

5. Advanced SQL Commands

6. Database Design

7. Modifying and Analyzing Data with SQL

8. Understanding/Querying Adventure Works Database

Although it is rarely discussed, automation may be the single biggest payback for learning Python. I refer to software that directly substitutes for human labour as "automation." In other words, a computer software that performs things that you would ordinarily perform by hand.

1. Write scripts that follow industry best practices for syntax and conventions.

2. Python syntax and program construction

3. How to schedule your programs to run at specific times.

4. Diagnose and fix frequent errors.

5. Python instructions for updating Excel files automatically

6. How to create scripts that can be used to automate manual tasks,

With a focus on data visualization, the class will begin with the Python libraries NumPy and Pandas before moving on to plotting solutions. You will learn how to use Plotly and Dash Enterprise, a potent tool for constructing dashboards, in addition to more standard plotting tools like Matplotlib and Seaborn.

1. Prepare and present a data story

2. collect, analyze, and/or modify data from a variety of sources.

3. Investigate data stories with exploratory data analysis.

4. Utilize Pandas and NumPy to manipulate data.

5. Utilize the cutting-edge Python visualization tools Plotly and Dash

6. Create a dashboard

7. Create expert data science solutions by abiding by the rules of powerful dashboard design.

8. Set up the dashboard on a live server and launch your project.

Making a computer learn by analyzing data and statistics is known as machine learning.

The development of machine learning is a step toward artificial intelligence (AI).

A program that analyzes data and learns to predict outcomes is known as machine learning.

1. utilizing the scikit-learn library to apply machine learning algorithms like logistic regression and random forest.

2. Choosing quality features to feed your algorithms.

3. separating data into training, test, and cross-validation sets appropriately.

4. How to use the Pandas library to clean and balance your data.

5. Important theoretical ideas including bias, variance, and overfitting.

6. Assessing the performance of your machine learning models.

You will learn how to create visualizations, organize data, and design dashboards in order to make more informed business decisions.

You'll understand about statistics, data mapping, and building data connections.

• Understanding data

• Tableau calculations

• Formatting visualizations

• Manipulating data in Tableau

• Advanced visualization tools

• Building Dashboards & Creating Stories

• Publication & Distribution of your visualization

• Intro to data maps

• Bulding visualization maps

• Advanced data manipulations

• Building custom charts

Module 1: Introducing Microsoft Power Bi Desktop  

1. Meet Power BI Desktop

2. Downloading Power BI

3. Adjusting Settings

4. Power BI Desktop Interface & Workflow

5. Resources & Monthly Updates

6. Introduction to the Project 1 dataset

7. Preliminary visualization and dashboard development

Module 2: Connecting & Shaping Data  

1. Power BI Front-end and Back-end

2. Types of data connectors

3. Introduction to Power Query Editor Layout

4. Transformations:

   1. Index and conditional column

   2. Calculated column 

   3. Grouping & Aggregating

   4. Pivoting & Un-pivoting

   5. Merging Queries & Joins

   6. Appending Queries

   7. Data source setting and refreshing queries

   8. Folders and append queries

   9. Advanced queries, M Language, and customisation

Module 3: Data Modelling  

1. Fact Table & Dim Table

2. Primary, Foreign & Composite Key

3. Cardinality – 121, 12Many & Many21, Many2Many

4. Cross Filter Direction & Security

5. Data Model Diagrams and how Visuals interact based on the relationships

6. Star & Snowflake Schemas

7. Introduction to Project 2

Module 4: DAX 

1. Dax v/s M Language

2. Introduction to Project 3

3. DAX as calculated columns

4. DAX as measures

5. Quick Measures

6. Time intelligence functions

7. Dedicated Measure Tables

8. Parameters

9. DAX Syntax & Operators

10. Maths & Stats Operations

11. Conditional Functions

12. Switch Functions

13. Logical Functions

14. Variables

15. Data Modelling & DAX

16. Data & Time 

17. Running Totals

18. TopN Dynamic DAX Functions

19. DAX for date difference excluding weekends

20. All, SelectAll

21. Filter

22. Iterators

23. DAX Best Practice

Module 5: Visualizing data with reports 

Module 6: Power Bi Service 

1. Introduction to Pro & Premium Capacity 

2. Publishing a dashboard 

3. Workspace & Gateways

4. Security

5. Scheduled refresh

6. Sharing dashboard and managing permissions

7. Subscriptions 

8. Live Streaming

9. Connecting to advanced JSON Files 

10. Introduction to data mart

11. Introduction to Project 4

Module 7: Bonus Lecture | Power BI, AI, Microsoft Fabrics & Chat GTP – Prototype Introduction