About Course

The Tech Bodhi Data Science & Data Analytics Program is a comprehensive, job-oriented program designed to develop practical skills in data analysis, business intelligence, Python-based analytics, statistics, machine learning and AI-assisted analytics.

The program combines the essential technologies used by modern Data Analysts with a focused introduction to Data Science and Machine Learning, enabling learners to progress from analyzing historical data to understanding predictive analytics.

Students work with real-world datasets, business scenarios and practical projects, developing the ability to extract data, clean and transform it, analyze patterns, create interactive dashboards, build predictive models and communicate actionable business insights.

Who Should Enroll

• Fresh Graduates

• Engineering Graduates

• BCA / BCS / BSc / MSc Students

• Aspiring Data Analysts

• Aspiring Data Scientists

• Business Analysts

• Working Professionals

• Career Switchers

• Professionals working with Excel and reporting

• Students interested in Business Intelligence

• Students looking to build a career in Data Analytics and Machine Learning

Prerequisites

• Basic computer knowledge

• No prior programming experience required

• Logical and analytical thinking

• Interest in working with data

MODULE 1 — SQL FOR DATA ANALYTICS

Topics Covered

Database Fundamentals

• Introduction to Databases

• Relational Database Concepts

• Tables, Rows and Columns

• Primary Keys

• Foreign Keys

• Relationships

• Database normalization — fundamentals

• SQL and relational databases

• MySQL environment

SQL Fundamentals

• SELECT

• DISTINCT

• WHERE

• ORDER BY

• LIMIT

• Aliases

• Comparison Operators

• Logical Operators

• IN

• BETWEEN

• LIKE

• IS NULL

SQL Functions

• COUNT

• SUM

• AVG

• MIN

• MAX

• String Functions

• Numeric Functions

• Date Functions

• Conditional Functions

• CASE Statements

Data Aggregation

• GROUP BY

• HAVING

• Conditional Aggregation

• Aggregating Business Metrics

• KPI calculations

SQL Joins

• INNER JOIN

• LEFT JOIN

• RIGHT JOIN

• CROSS JOIN

• SELF JOIN

• Joining multiple tables

Advanced SQL

• Subqueries

• Correlated Subqueries

• Common Table Expressions

• Views

• Temporary Tables

• Window Functions

• ROW_NUMBER

• RANK

• DENSE_RANK

• LEAD

• LAG

• Running Totals

• Moving Averages

• Ranking Analysis

Practical SQL Analytics

• Sales Analysis

• Customer Analysis

• Product Analysis

• Employee Analysis

• Revenue Analysis

• Business KPI Analysis

Hands-on

Students solve real-world SQL problems using business datasets.

Tools & Technologies

MySQL | SQL

Expected Outcomes

Students will be able to:

• Query relational databases

• Extract and transform business data

• Write complex SQL queries

• Analyze large datasets

• Calculate business KPIs

• Use SQL for real-world Data Analytics

MODULE 2 — ADVANCED EXCEL FOR DATA ANALYTICS

Topics Covered

Excel Fundamentals

• Workbook and Worksheet Management

• Data Types

• Tables

• Sorting and Filtering

• Data Validation

• Conditional Formatting

• Named Ranges

Advanced Functions

• IF

• IFS

• AND

• OR

• IFERROR

• SUMIF

• SUMIFS

• COUNTIF

• COUNTIFS

• AVERAGEIF

• AVERAGEIFS

• SUMPRODUCT

Lookup Functions

• VLOOKUP

• HLOOKUP

• XLOOKUP

• INDEX

• MATCH

• INDEX + MATCH

• Multiple-condition lookups

Text Functions

• LEFT

• RIGHT

• MID

• LEN

• TRIM

• CLEAN

• SUBSTITUTE

• TEXT

• CONCAT

• TEXTJOIN

Date & Time Analysis

• TODAY

• NOW

• DATE

• YEAR

• MONTH

• DAY

• EOMONTH

• NETWORKDAYS

• WORKDAY

Data Cleaning

• Duplicate handling

• Missing data

• Data validation

• Error handling

• Data standardization

• Text cleaning

• Data transformation

Pivot Tables & Pivot Charts

• Creating Pivot Tables

• Grouping Data

• Calculated Fields

• Pivot Charts

• Slicers

• Timelines

• Interactive analysis

Advanced Analytics

• What-If Analysis

• Goal Seek

• Scenario Manager

• Data Tables

• Forecasting concepts

Excel Dashboards

• KPI Cards

• Charts

• Interactive Controls

• Dashboard Layout

• Business Reporting

• Data Storytelling

Hands-on

Build professional Excel dashboards using real-world datasets.

Tools & Technologies

Microsoft Excel

Expected Outcomes

Students will be able to:

• Clean and analyze business data

• Perform advanced calculations

• Build dynamic reports

• Create Pivot-based analysis

• Develop interactive Excel dashboards

MODULE 3 — POWER BI FOR BUSINESS INTELLIGENCE

Topics Covered

Power BI Fundamentals

• Introduction to Business Intelligence

• Power BI Ecosystem

• Power BI Desktop

• Power BI Service

• Connecting to Data

• Excel Data

• CSV Data

• Database Connections

• Data Sources

Power Query

• Data Import

• Data Profiling

• Data Cleaning

• Removing Duplicates

• Handling Missing Values

• Changing Data Types

• Splitting Columns

• Merging Columns

• Conditional Columns

• Custom Columns

• Merge Queries

• Append Queries

• Pivot / Unpivot

Data Modeling

• Data Modeling Fundamentals

• Fact Tables

• Dimension Tables

• Star Schema

• Relationships

• Cardinality

• Cross-filter Direction

• Date Tables

• Calendar Tables

• Model Optimization

DAX

• DAX Fundamentals

• Calculated Columns

• Measures

• Calculated Tables

• SUM

• AVERAGE

• COUNT

• DISTINCTCOUNT

• CALCULATE

• FILTER

• ALL

• ALLEXCEPT

• VALUES

• RELATED

• DIVIDE

Time Intelligence

• MTD

• QTD

• YTD

• Previous Year

• Year-over-Year

• Growth Analysis

• Rolling Analysis

Data Visualization

• Charts

• Cards

• KPIs

• Tables

• Matrix

• Maps

• Slicers

• Drill-down

• Drill-through

• Tooltips

• Bookmarks

Dashboard & Data Storytelling

• Dashboard Design

• Visualization Best Practices

• Executive Dashboards

• Interactive Reports

• Business Storytelling

• Insight Generation

• Presenting Findings to Stakeholders

Power BI Service

• Publishing Reports

• Workspaces

• Sharing

• Scheduled Refresh

• Dashboard Management

• Row-Level Security — Introduction

AI-Assisted Power BI

• AI-assisted DAX

• AI-assisted Data Analysis

• AI-assisted Report Development

• AI-assisted Insight Generation

Git & GitHub

• Git Fundamentals

• GitHub Repository

• Version Control

• Project Documentation

• Portfolio Management

Hands-on Projects

• Sales Dashboard

• HR Dashboard

• Customer Analytics Dashboard

• Financial/Business KPI Dashboard

Tools & Technologies

Power BI | Power Query | DAX | Git | GitHub | Generative AI

Expected Outcomes

Students will be able to:

• Connect Power BI to multiple data sources

• Transform data using Power Query

• Build analytical data models

• Write DAX measures

• Create interactive dashboards

• Apply data storytelling techniques

• Publish and share Power BI reports

MODULE 4 — PYTHON FOR DATA ANALYTICS

Topics Covered

Python Fundamentals

• Python Introduction

• Variables

• Data Types

• Operators

• Strings

• Lists

• Tuples

• Sets

• Dictionaries

• Conditional Statements

• Loops

• Functions

• Lambda Functions

• List Comprehension

• Exception Handling

• Modules and Packages

• File Handling

NumPy

• NumPy Arrays

• Dimensions

• Shape

• Data Types

• Indexing

• Slicing

• Array Operations

• Mathematical Operations

• Statistical Operations

• Broadcasting

• Reshaping

• Random Number Generation

Pandas

• Series

• DataFrames

• Reading CSV and Excel

• Data Inspection

• Filtering

• Sorting

• Indexing

• GroupBy

• Aggregation

• Merge

• Join

• Concatenation

• Pivot Tables

• Missing Values

• Duplicate Handling

• Data Transformation

• Date/Time Analysis

Data Cleaning with Python

• Missing Values

• Duplicate Data

• Data Types

• Outliers

• Data Standardization

• Data Transformation

Matplotlib

• Line Charts

• Bar Charts

• Histograms

• Scatter Plots

• Subplots

• Labels

• Titles

• Legends

Seaborn

• Distribution Plots

• Box Plots

• Violin Plots

• Scatter Plots

• Pair Plots

• Heatmaps

• Categorical Plots

• Statistical Visualization

Exploratory Data Analysis

• Univariate Analysis

• Bivariate Analysis

• Multivariate Analysis

• Distribution Analysis

• Correlation Analysis

• Outlier Analysis

• Pattern Identification

• Anomaly Detection

• Business Insight Generation

Hands-on

• Sales Data Analysis

• Customer Data Analysis

• HR Data Analysis

• E-Commerce Data Analysis

Tools & Technologies

Python | NumPy | Pandas | Matplotlib | Seaborn | Jupyter Notebook

Expected Outcomes

Students will be able to:

• Use Python for data analysis

• Clean and transform datasets

• Perform EDA

• Analyze large datasets

• Create statistical visualizations

• Generate business insights using Python

MODULE 5 — DATA SCIENCE & MACHINE LEARNING

This module acts as the advanced progression from Data Analytics to Data Science.

Topics Covered

Introduction to Data Science

• What is Data Science?

• Data Analytics vs Data Science

• Data Analyst vs Data Scientist

• Data Science Lifecycle

• AI vs Machine Learning vs Data Science

• Business Problems and Machine Learning

• Predictive Analytics

Machine Learning Fundamentals

• What is Machine Learning?

• Supervised Learning

• Unsupervised Learning

• Regression

• Classification

• Clustering

• Features and Target

• Training and Testing Data

• Overfitting

• Underfitting

Statistics for Data Science

• Probability

• Probability Distributions

• Sampling

• Central Limit Theorem

• Confidence Intervals

• Hypothesis Testing

• p-value

• Type I and Type II Errors

• t-Test

• Chi-Square Test

• ANOVA

• Statistical Significance

Libraries

NumPy | SciPy | Statsmodels | Pandas

Data Preprocessing

• Missing Values

• Outlier Treatment

• Encoding

• Feature Scaling

• Train/Test Split

• Data Leakage

• Data Preparation for ML

Libraries

Pandas | NumPy | Scikit-learn

Feature Engineering

• Feature Creation

• Feature Transformation

• Numerical Features

• Categorical Features

• Date/Time Features

• Encoding

• Scaling

• Feature Selection

• Feature Importance

• Multicollinearity

Libraries

Pandas | NumPy | Scikit-learn | Statsmodels

Regression

• Linear Regression

• Multiple Linear Regression

• Polynomial Regression

• Ridge Regression

• Lasso Regression

• Regression Model Evaluation

Metrics

• MAE

• MSE

• RMSE

• R²

Libraries

Scikit-learn | Statsmodels

Classification

Algorithms

• Logistic Regression

• K-Nearest Neighbors

• Decision Tree

• Random Forest

• Support Vector Machine — Introduction

• Gradient Boosting — Introduction

Evaluation

• Confusion Matrix

• Accuracy

• Precision

• Recall

• F1 Score

• ROC-AUC

Libraries

Scikit-learn | Pandas | NumPy

Unsupervised Learning

Clustering

• K-Means

• Elbow Method

• Silhouette Score

• Customer Segmentation

• Hierarchical Clustering — Introduction

Dimensionality Reduction

• PCA

• Principal Components

• Explained Variance

Libraries

Scikit-learn | SciPy

Ensemble Learning & XGBoost

• Ensemble Learning

• Bagging

• Boosting

• Random Forest

• Gradient Boosting

• XGBoost

• Feature Importance

Libraries

Scikit-learn | XGBoost

Model Explainability

• Why Explainable AI?

• Feature Importance

• Model Interpretation

• Global Explanation

• Local Explanation

• SHAP

• SHAP Summary Plot

• Business Interpretation

Libraries

SHAP | Scikit-learn

Generative AI for Data Science

• AI-assisted Python

• AI-assisted EDA

• AI-assisted Feature Engineering

• AI-assisted ML Code

• AI-assisted Statistical Analysis

• AI-assisted Model Interpretation

• Debugging ML Code using AI

• Prompt Engineering for Data Science

• Validating AI-generated results

• AI hallucinations

• Data Privacy

• Responsible AI

Tools

ChatGPT | AI Coding Assistants

REAL-WORLD DATA SCIENCE PROJECTS

Customer Churn Prediction

Python | Pandas | Scikit-learn | XGBoost | SHAP

Students build a model to identify customers likely to leave and understand the key factors influencing churn.

Loan Default Prediction

Python | Pandas | Scikit-learn

Students build a classification model to identify customers with higher default risk.

Customer Segmentation

Python | Pandas | Scikit-learn | Matplotlib | Seaborn

Students use clustering to identify customer groups and generate business recommendations.

INTEGRATED CAPSTONE PROJECT

Students complete an end-to-end project combining the major technologies covered in the program.

Project Flow

Business Problem

↓

Excel / Raw Data

↓

SQL Data Extraction

↓

Python Data Cleaning & EDA

↓

Statistical Analysis

↓

Power BI Dashboard

↓

Machine Learning Model

↓

Model Evaluation

↓

Business Insights

↓

Final Presentation

Technologies

Excel | MySQL | Power BI | Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit-learn | XGBoost | SHAP

GENERATIVE AI ACROSS THE PROGRAM

Rather than restricting GenAI to a single topic, it can be integrated across all modules.

Students learn to use AI for:

• Excel formulas

• SQL query generation

• SQL debugging

• DAX development

• Power BI analysis

• Python coding

• Pandas operations

• Data cleaning

• EDA

• Statistical analysis

• Machine Learning

• Documentation

• Project presentation

• Interview preparation

The focus is on AI-assisted productivity + human validation, not blindly accepting AI-generated output.

INDUSTRY PROJECTS

Students build portfolio-ready projects in areas such as:

• Sales Analytics

• Customer Analytics

• HR Analytics

• Financial Analytics

• E-Commerce Analytics

• Customer Churn

• Loan Default

• Customer Segmentation

CAREER OPPORTUNITIES

After completing the program, learners can pursue opportunities such as:

• Data Analyst

• Business Data Analyst

• BI Analyst

• Reporting Analyst

• Power BI Analyst

• SQL Data Analyst

• Python Data Analyst

• Analytics Associate

• Advanced Analytics Associate

• Junior Data Science / ML roles depending on prior experience and project depth

EXPECTED OUTCOMES

After completing the program, students will be able to:

• Analyze real-world business datasets

• Perform advanced Excel analysis

• Write complex SQL queries

• Build Power BI data models and dashboards

• Develop DAX measures

• Perform data storytelling

• Use Python for Data Analytics

• Work with NumPy and Pandas

• Create visualizations using Matplotlib and Seaborn

• Perform statistical analysis

• Conduct exploratory data analysis

• Understand Machine Learning fundamentals

• Build regression and classification models

• Perform clustering

• Understand XGBoost

• Evaluate ML models

• Interpret predictions using SHAP

• Use Generative AI throughout the analytics workflow

• Maintain projects using Git/GitHub

• Build a professional Data Analytics/Data Science portfolio

TOOLS & TECHNOLOGIES

Advanced Excel
MySQL / SQL
Power BI
Power Query
DAX
Python
NumPy
Pandas
Matplotlib
Seaborn
SciPy
Statsmodels
Scikit-learn
XGBoost
SHAP
Git
GitHub
Generative AI / ChatGPT