Module 1: Introduction to Data Analytics

• What is Data Analytics?
• Types of Analytics: Descriptive, Diagnostic, Predictive & Prescriptive
• Data lifecycle and Data ecosystem
• Roles in Data Analytics
• Real-world applications

Module 2: Excel for Data Analysis

• Basic to Advanced Excel functions
• Data cleaning & formatting
• Lookup operations (VLOOKUP, XLOOKUP, INDEX-MATCH)
• Pivot tables, Pivot charts
• Conditional formatting
• Dashboard creation

Module 3: SQL for Data Analytics

• Introduction to Databases
• SQL Basics (SELECT, WHERE, ORDER BY)
• JOINs (INNER, LEFT, RIGHT, FULL)
• GROUP BY, HAVING
• Subqueries
• Aggregate functions
• Creating tables, inserting data
• Real-world query writing

Module 4: Statistics & Probability

• Types of Data
• Measures of Central Tendency (Mean, Median, Mode)
• Measures of Dispersion (Variance, SD)
• Probability basics
• Hypothesis Testing
• Correlation & Regression
• Outliers & Data distribution

Module 5: Python for Data Analysis

• Python basics & environment setup
• Working with Jupyter Notebook
• Data types, variables, loops, functions
• Libraries:
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Data cleaning with Pandas
• Exploratory Data Analysis (EDA)
• Plotting and Visualization

Module 6: Data Visualization Tools

Power BI

• Power BI Interface
• Importing & transforming data
• DAX basics
• Measures & Calculated Columns
• Visualizations & Dashboards
• Publishing & Sharing Reports

Tableau (optional/advanced)

• Connecting to data sources
• Calculated fields
• Filters & Parameters
• Dashboards & Stories

Module 7: Business Analytics & Domain Knowledge

• Understanding business KPIs
• How companies use data
• Case studies:
• Sales analytics
• Customer analytics
• Marketing analytics
• Finance analytics

Module 8: Machine Learning (Beginner Level)

• Introduction to machine learning
• Data preprocessing
• Linear Regression
• Logistic Regression
• Model evaluation (accuracy, precision, recall, F1 score)
• Real-world prediction projects

Module 9: Visualization & Reporting

• Building dashboards
• Presenting insights
• Report writing
• Storytelling with data

Module 10: Projects & Assignments

• Real-world datasets
• End-to-end project:
• Data collection
• Cleaning
• Analysis
• Visualization
• Final report
• Resume preparation & interview questions