This module covers the basics of statistics, including descriptive and inferential techniques, data types, and key measures like mean, median, variance, and standard deviation. You'll also explore probability concepts and distributions (Normal, Binomial, Poisson), along with hands-on statistical analysis using Microsoft Excel. Career coaching support is also included.

Formatting Concepts in Excel
Learn how to apply cell formatting, styles, and conditional formatting to enhance data presentation.

Using Formulas in Excel
Understand and apply basic to intermediate formulas for calculations and data manipulation.

Statistical Analysis Techniques
Perform statistical operations such as mean, median, standard deviation, and more within Excel.

Data Summarization and Visualization
Learn to summarize data effectively using tables, charts, and graphs for clear data interpretation.

Introduction to Additional Excel Features
Explore useful features like data validation, sorting, filtering, and protecting worksheets.

Data Cleaning and Preparation
Understand how to identify and correct inconsistencies, remove duplicates, and prepare raw data for analysis.

Pivot Tables and Pivot Charts
Master the creation and use of pivot tables and charts to dynamically analyze large data sets.

Data Lookup and Reference Functions
Use functions like VLOOKUP, HLOOKUP, INDEX, and MATCH to search and retrieve data efficiently.

Basic Introduction to Excel Macros
Get introduced to automation in Excel using simple macros to perform repetitive tasks.

Tools: Python, Jupyter notebook, VS Code 

• Python Installation & Setup 

• Python Basics (Variables, Strings, Operators, Control Flow - If Condition, For & While Loop) 

• Data Structures (Lists, Tuples, Dictionaries, Sets) 

• Functions & Modules (Built-in, User-Defined functions & Libraries) 

• File Handling & Exception Handling (File Handling, Exception Handling)

• Working with NumPy (Introduction & Benefits, Basic Operations, Matrix Operations, Slicing & Stacking)

• Data Analysis with Pandas (Pandas Introduction and Installation) 

• Object-Oriented Programming (OOP) (Classes and Objects, Inheritance, Operator Overloading)

• Advanced Python Concepts (Recursion, Generators, Decorators, Lambda Functions) 

• Working with APIs & Web Scraping (Requests, Beautiful Soup) 

Project: Auto Mobile Sales Market Analysis.

This project will analyze automobile sales data to uncover market trends, customer preferences, and the impact of various factors like price, brand, and seasonality. You will explore data-driven insights for business decisions, covering Python programming, data analysis and visualization.

Tools: MySQL 

Overview of SQL and Relational Database Concepts
Gain a foundational understanding of relational databases, how data is structured using tables, and how SQL (Structured Query Language) is used to interact with and manage this data.

Writing Basic SQL Queries
Learn how to write simple SQL statements to select, filter, sort, and retrieve data from one or more tables. This includes understanding SELECT, WHERE, ORDER BY, and LIMIT clauses.

Aggregating and Summarizing Data
Use aggregate functions such as COUNT, SUM, AVG, MIN, and MAX to analyze data. Learn to group results using the GROUP BY clause and apply filters with HAVING for meaningful summaries.

Performing Join Operations Across Tables
Understand how to combine data from multiple tables using various types of joins—INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL OUTER JOIN—to analyze relationships between data sets.

Using Subqueries and Nested Queries
Learn how to embed queries within other queries to perform complex filtering and data retrieval tasks, allowing for more flexible and powerful data analysis.

Exploring Advanced SQL Techniques
Dive into advanced SQL topics such as window functions (e.g., RANK, ROW_NUMBER), Common Table Expressions (CTEs), and advanced filtering to solve complex data problems efficiently.

Introduction to Data Visualization and Power BI

Gain an understanding of how visual representation of data helps in decision-making and how Power BI can be used as a tool for data analysis and reporting.

Understanding Power BI Building Blocks

Explore the foundational components of Power BI, including datasets, reports, dashboards, and visualizations, and how they interact.

Managing Data Connections

Learn how to connect Power BI to various data sources (Excel, SQL, Web, etc.), and manage those connections for seamless data refresh and integration.

Basic Data Visualization

Start creating basic visual elements such as bar charts, pie charts, tables, and line graphs to display key metrics.

Advanced Data Visualization

Enhance your reports with advanced visuals like maps, gauge charts, custom visuals, and KPI indicators to provide deeper insights.

Slicing and Dicing Data

Understand how to use filters, slicers, and drill-down features to analyze data dynamically and from different angles.

Power BI Basic & Advanced Calculations

Learn DAX (Data Analysis Expressions) to create calculated fields, measures, and perform both simple and complex data computations.

Formatting and Dashboard Designing

Discover best practices for designing professional and interactive dashboards, including layout, color schemes, and user-friendly navigation.

Publishing and Sharing Reports

Understand how to publish Power BI reports to the Power BI Service and securely share insights with teams or stakeholders.

Data Preprocessing

Prepare raw data for analysis by cleaning, transforming, and organizing it. This includes removing duplicates, converting data types, encoding categorical variables, and normalizing values.

Web Scraping

Learn how to extract data from websites for use in your analysis. This is useful when data is not readily available in downloadable formats like CSV or Excel.

Techniques for Handling Outliers and Missing Data

Understand how to identify and treat outliers and missing values using statistical techniques such as imputation, filtering, or transformation to maintain data integrity and reliability.

Using Beautiful Soup and Scrapy for Data Collection

Get hands-on experience with Beautiful Soup (a Python library for parsing HTML and XML) and Scrapy (a powerful web crawling framework) to automate data extraction from web sources for EDA purposes.

Tools: Tableau 

Introduction to Tableau

Get an overview of Tableau as a leading data visualization tool, understanding its interface, features, and how it helps turn raw data into interactive and shareable dashboards.

Data Preparation and Cleaning in Tableau

Learn how to connect, clean, and transform data directly in Tableau using features like Data Interpreter, calculated fields, and data blending to ensure accuracy and consistency. 

Working with Date and Time Data

Explore techniques for handling and analyzing date and time fields, including creating time series charts, using date filters, and performing time-based calculations.

Data Aggregation and Grouping

Understand how to summarize and group data using Tableau features such as aggregations, hierarchies, groups, and bins for more meaningful visualizations.

Creating and Using Dashboards

Design interactive dashboards by combining multiple worksheets. Learn best practices for layout, interactivity (filters, actions), and performance optimization.

Publishing and Sharing Tableau Workbooks

Learn how to publish dashboards to Tableau Public or Tableau Server, manage access permissions, and share insights effectively with your audience.

Basics of Machine Learning

Get introduced to the fundamental concepts of machine learning, including the difference between supervised and unsupervised learning, and how machines learn from data.

Overview of Regression and Classification

Explore key supervised learning techniques:

• Regression is used to predict continuous values (e.g., predicting house prices).
• Classification is used to categorize data into predefined classes (e.g., spam vs. non-spam emails).

Advanced Analytics

Dive into more complex analytical techniques that go beyond basic prediction.

• Clustering
  Learn unsupervised learning methods like K-Means and Hierarchical Clustering to group similar data points.
• Association Rule Learning
  Discover hidden patterns in large datasets (e.g., market basket analysis to identify frequently bought item combinations).
• Time Series Analysis
  Analyze data that changes over time using techniques like trend analysis, seasonality detection, and forecasting (e.g., stock price prediction).

Tools: Python, Open AI LLMs, DeepSeek. 

• Introduction to Generative AI (Overview, Applications)

• Large Language Models (LLMs, Context Window, Temperature, Top-p, Top-k)

• Prompt Engineering (Elements of a Good Prompt, Zero-Shot, One-Shot, Few-Shot Prompting)

• Generative AI Application Development (Development Steps, Hallucinations, Security, Cost)

• LangChain Framework (Installation, Calling LLM from LangChain, Prompt Templates & Chains)

• Retrieval-Augmented Generation (RAG) (Vector Database, ChromaDB, Metadata Filtering, Euclidean & Cosine Distance)

• Agents and Agentic AI (Understanding Agents in AI)

• Streamlit UI Development (Building UI for Generative AI Apps)

• Database Integration (SQLite for Storing Data in AI Applications)

Project : AI-Powered Knowledge Assistant.  

This project will develop an AI-powered knowledge assistant that retrieves relevant information from a custom database using Generative AI and Retrieval-Augmented Generation (RAG). The assistant will integrate with a vector database for efficient search, use LangChain for LLM interaction, and have a Streamlit-based user interface for seamless interaction.

End - To - End Data analytics projects  including Data Privacy and Security, Data Ethics, Data Governance, Data Story telling