1.1 What is Data Analytics?
1.2 Types of Analytics: Descriptive, Diagnostic, Predictive, Prescriptive
1.3 Data Analytics Lifecycle
1.4 Real-world Applications & Case Studies
1.5 Overview of Tools: Excel, Python, SQL, Power BI

2.1 Excel Interface and Navigation
2.2 Data Entry, Formatting, and Cleaning
2.3 Important Formulas
2.3.1 Logical: IF, AND, OR
2.3.2 Text: LEFT, RIGHT, LEN, CONCATENATE, TEXT
2.4 Sorting and Filtering
2.5 Conditional Formatting
2.7 Dashboards (with slicers, charts)

3.1 Introduction to Relational Databases
3.2 SQL Basics
3.2.1 SELECT, WHERE, ORDER BY, LIMIT
3.2.2 Filtering: LIKE, IN, BETWEEN, IS NULL
3.3 Aggregate Functions
3.3.1 COUNT(), SUM(), AVG(), MIN(), MAX()
3.3.2 GROUP BY, HAVING
3.4 Table Joins
3.4.1 INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
3.5 Subqueries and Aliases
3.6 Table Operations
3.6.1 CREATE, INSERT, UPDATE, DELETE
3.7 Importing/Exporting CSV files in MySQL

4.1 Python Basics
4.1.1 Variables, Data Types, Operators
4.1.2 Lists, Tuples, Dictionaries, Sets
4.2 Control Flow
4.2.1 if-else, for, while
4.2.2 Functions and Lambda Expressions
4.3 File Handling
4.3.1 Reading and writing CSV/JSON files
4.4 Comprehensions and Exception Handling

5.1 Introduction to NumPy Arrays
5.2 Array Indexing and Slicing
5.3 Mathematical and Statistical Functions
5.4 Broadcasting and Vectorized Operations
5.5 Handling NaN and Inf values

6.1 Series and DataFrame Basics
6.2 Reading & Writing Files (CSV, Excel, JSON)
6.3 Data Cleaning
6.3.1 Handling Null Values: fillna(), dropna()
6.3.2 Duplicates, Data Types
6.4 Data Selection and Filtering
6.5 Sorting, Renaming Columns, Resetting Index
6.6 Grouping and Aggregation
6.6.1 groupby(), agg(), Pivot Tables
6.7 Joining and Merging DataFrames
6.8 Applying Functions: apply(), map(), custom functions

7.1 Introduction to Matplotlib
7.2 Plot Types
7.2.1 Line Plot, Bar Plot, Pie Chart, Histogram, Box Plot
7.3 Customization
7.3.1 Titles, Labels, Colors, Legends, Grids
7.4 Subplots and Figure Size
7.5 Saving Plots to File

8.1 Power BI Interface and Workflow
8.2 Connecting to Data Sources (Excel/CSV)
8.3 Data Cleaning with Power Query Editor and Transformation
8.4 Creating Visualizations
8.4.1 Bar, Line, Pie, Map, Card, KPI
8.5 Filters and Slicers
8.6 Basic Dashboard Creation
8.7 Publishing Reports to Power BI Service

9.1 What is ETL?
9.2 Extracting Data from Files (CSV, Excel)
9.3 Transforming Data using Pandas
9.3.1 Cleaning, Normalizing, Renaming Columns
9.4 Loading Data into MySQL
9.4.1 Using pymysql or sqlalchemy
9.5 ETL Automation with Python
9.6 Logging and Error Handling in ETL Jobs