MODULE 1: Introduction to Data Analytics
What is Data Analytics?
Roles and Responsibilities of a Data Analyst
Overview of Data Science vs. Data Analytics vs. Business Intelligence
Types of Data: Structured, Semi-structured, Unstructured
Real-world Applications of Data Analytics
MODULE 2: Excel for Data Analysis
Data Cleaning and Formatting
Formulas, Functions (VLOOKUP, INDEX-MATCH, IF, etc.)
Pivot Tables and Pivot Charts
Conditional Formatting
Data Validation
Basic Dashboards in Excel
MODULE 3: SQL for Data Querying
Introduction to Databases and SQL
CRUD Operations (SELECT, INSERT, UPDATE, DELETE)
Joins (INNER, OUTER, LEFT, RIGHT)
Filtering, Sorting, Grouping
Aggregate Functions (COUNT, SUM, AVG, MAX, MIN)
Subqueries, Window Functions
Hands-on with MySQL/PostgreSQL
MODULE 4: Data Visualization
Introduction to Data Visualization Principles
Tools: Tableau / Power BI / Excel Charts
Creating Interactive Dashboards
Charts: Line, Bar, Pie, Heatmaps, Tree Maps, etc.
Data Storytelling Techniques
MODULE 5: Python for Data Analysis
Python Basics (Variables, Data Types, Conditions, Loops)
Libraries: pandas, numpy, matplotlib, seaborn
Data Cleaning & Transformation
Exploratory Data Analysis (EDA)
Introduction to Jupyter Notebooks
Working with CSV, Excel, and APIs
MODULE 6: Statistics and Probability for Data Analysis
Descriptive Statistics (Mean, Median, Mode, Variance, Std. Dev.)
Probability Concepts
Distributions (Normal, Binomial, etc.)
Hypothesis Testing
Confidence Intervals
Correlation vs. Causation
MODULE 7: Business Intelligence & Reporting
BI Tools Overview (Power BI)
Connecting to Data Sources
Creating Reports and Dashboards
Sharing and Scheduling Reports
Case Studies and Best Practices
Project Work
Handling Live Projects | Corporate level Projects | Job Training
Soft Skill
CV and Resume Writing | Extempore
Positive Attitude | Mock Interview
Presentation & GD | Personality Development
