📍 Module 1: Introduction to Data Analytics

• What is Data Analytics?

• Data Analytics vs. Data Science vs. BI

• Applications in Real World (Finance, Health, Retail)

• Data Analytics Workflow

• Overview of Tools Used in Python for Analytics

📍 Module 2: Python Fundamentals

• Installing Python & Jupyter Notebook

• Variables, Data Types, Operators

• Control Flow (if, loops)

• Functions & Modules

• List, Tuple, Set, Dictionary (with use cases)

📍 Module 3: Working with Data Using Pandas & NumPy

• NumPy Arrays & Vectorized Computation

• Pandas Series & DataFrame Basics

• Reading CSV, Excel, JSON files

• Data Cleaning & Handling Missing Values

• Data Filtering, Sorting, Grouping & Aggregation

• Merging, Joining & Concatenating Datasets

📍 Module 4: Data Visualization

• Importance of Data Visualization

• Plotting with:

  • Matplotlib (Line, Bar, Pie, Histogram)

  • Seaborn (Boxplot, Heatmap, Pairplot)

  • Plotly (Interactive Dashboards)

• Choosing the Right Chart for the Right Data

📍 Module 5: Exploratory Data Analysis (EDA)

• Understanding Data Structure

• Univariate & Bivariate Analysis

• Correlation Analysis

• Outlier Detection

• Summary Statistics & Feature Engineering

📍 Module 6: Statistics for Data Analytics

• Descriptive Statistics (Mean, Median, Mode, SD)

• Data Distributions

• Probability Basics

• Hypothesis Testing

• Confidence Intervals

📍 Module 7: Mini Projects with Real Datasets

• Sales Data Analysis

• Customer Segmentation

• COVID-19 Trend Analysis

• Retail Product Recommendation (EDA Only)

📍 Module 8: Introduction to Business Insights & Reporting

• Defining KPIs

• Translating Data into Insights

• Reporting Formats (Python → Excel/Charts)

• Automation with Python

📍 Module 9: Capstone Project

• Complete Analytics Lifecycle:

  • Data Collection

  • Cleaning & EDA

  • Visualizations & Storytelling

  • Business Recommendation

• Documented & Presentable Project Portfolio