Live Course Module: Python Course for Data Analytics

Total Duration: 24 Hours (4 Weeks)

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Week 1: Python Foundations for Data Analytics (6 Hours)

1. Introduction to Python & Setup (1 hr)

   • Installing Python & Jupyter Notebook

   • Understanding Anaconda distribution

   • Python IDEs overview

2. Python Basics (2 hrs)

   • Variables, Data Types, Operators

   • Conditional Statements & Loops

   • Functions and Modules

3. Data Structures in Python (2 hrs)

   • Lists, Tuples, Sets, Dictionaries

   • Indexing, Slicing, and Iteration

   • Practical Exercises on Data Manipulation

4. Hands-on Mini Project (1 hr)

   • Simple Data Input/Output & Summary Stats

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Week 2: Working with Data using Python (6 Hours)

1. File Handling & Regular Expressions (2 hrs)

   • Reading/Writing CSV, Excel, and Text files

   • Handling Missing Data

   • Regex basics for data cleaning

2. NumPy for Numerical Computing (2 hrs)

   • Arrays, Indexing, Broadcasting

   • Vectorized Operations

   • Descriptive Statistics using NumPy

3. Pandas for Data Manipulation (2 hrs)

   • DataFrames & Series

   • Data Cleaning, Filtering, Grouping

   • Combining & Merging Datasets

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Week 3: Data Visualization & Exploration (6 Hours)

1. Data Visualization with Matplotlib (2 hrs)

   • Line, Bar, Pie, and Scatter Plots

   • Customizing Visuals (titles, labels, legends)

2. Advanced Visualization with Seaborn (2 hrs)

   • Distribution, Categorical & Relational Plots

   • Pairplots, Heatmaps & Aesthetics

3. Exploratory Data Analysis (EDA) Project (2 hrs)

   • Real-world dataset exploration

   • Insights through visual analytics

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Week 4: Analytics, Automation & Final Project (6 Hours)

1. Data Analytics Fundamentals (2 hrs)

   • Descriptive & Inferential Analysis

   • Correlation, Trends, and Patterns

   • Basic Statistics for Analysts

2. Automating Tasks & Reporting (2 hrs)

   • Using Python for repetitive data tasks

   • Exporting reports to Excel & CSV

   • Scheduling analytics scripts

3. Capstone Project (2 hrs)

   • End-to-end Data Analytics Project

   • Dataset Cleaning, Visualization & Insights Presentation

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🎯 Course Outcomes

By the end of this course, learners will:

• Understand Python syntax and data structures.

• Handle and clean real-world datasets efficiently.

• Use NumPy, Pandas, Matplotlib & Seaborn for data analysis.

• Build and present a complete data analytics project.

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🧰 Tools & Libraries Used

• Python 3.x

• Jupyter Notebook

• NumPy

• Pandas

• Matplotlib

• Seaborn