Module 1: Introduction to Data Analytics

• What is Data Analytics?
• Roles & Responsibilities of a Data Analyst
• Types of Analytics (Descriptive, Diagnostic, Predictive, Prescriptive)
• Data Life Cycle
• Data vs Information vs Insights
• Industry Use Cases (Finance, Retail, Healthcare, Marketing)

Module 2: Microsoft Excel for Data Analysis

Basics

• Excel Interface & Formulas
• Functions: SUM, AVERAGE, COUNT, IF, VLOOKUP, HLOOKUP

Intermediate

• Pivot Tables & Pivot Charts
• Conditional Formatting
• Data Cleaning
• Data Validation
• Excel Dashboards

Advanced

• Advanced Functions (INDEX-MATCH, XLOOKUP)
• Power Query
• Power Pivot
• Automation with Macros (Optional)

Module 3: SQL for Data Analysis

• Introduction to Databases
• SQL Queries (SELECT, WHERE, ORDER BY)
• JOINS (INNER, LEFT, RIGHT, FULL)
• GROUP BY, HAVING
• Aggregations
• Subqueries
• Views & Stored Procedures (Basic)
• Real-world SQL practice using datasets

Module 4: Python for Data Analysis

• Python Basics (Variables, Loops, Conditions)
• Data Structures: List, Dictionary, Tuple
• Using Jupyter Notebook
• Libraries:
• NumPy (Arrays, Stats)
• Pandas (Data Cleaning, Merging, Grouping)
• Matplotlib & Seaborn (Data Visualization)
• Exploratory Data Analysis (EDA)
• Handling Missing Values
• Outlier Detection
• Data Transformation

Module 5: Data Visualization Tools

Power BI

• Interface & Data Model
• Power Query (ETL)
• DAX Functions
• Interactive Dashboards
• Creating KPIs & Reports
• Publishing to Power BI Service

Tableau (Optional / Based on Demand)

• Dimensions & Measures
• Filters, Charts, Calculated Fields
• Storytelling Dashboards

Module 6: Statistics for Data Analysis

• Descriptive Statistics (Mean, Median, Mode)
• Probability Basics
• Distribution Types
• Hypothesis Testing
• Correlation & Regression
• Statistical Significance
• A/B Testing

Module 7: Data Cleaning & Preprocessing

• Standardization vs Normalization
• Feature Engineering Basics
• Handling Duplicate Data
• Working with Categorical Data
• Date & Time Formatting

Module 8: Business Intelligence (BI) Concepts

• What is BI?
• KPIs & Metrics
• Data Warehousing Basics
• Dashboards for Business Decisions
• Case Studies (Marketing, Sales, Finance)

Module 9: Domain Knowledge (Optional Based on Institute)

Choose any:

• Finance Analytics
• Marketing Analytics
• HR Analytics
• Retail Analytics
• Supply Chain Analytics

Module 10: Projects & Portfolio

✔ Sales Dashboard (Excel / Power BI)

✔ Customer Segmentation (Python)

✔ Market Basket Analysis

✔ SQL Case Study (E-Commerce or Banking)

✔ Data Cleaning Project (Pandas)

✔ Final Data Analytics Capstone Project

Module 11: Soft Skills & Job Preparation

• Resume for Data Analyst
• Portfolio on GitHub / Kaggle
• Mock Interviews
• Communication Skills
• Real Business Case Studies