Starting From: 31 August, 2025

Days: Saturday, Sunday, Tuesday, Thursday

Time: 9:30 to 10:30 PM IST

Duration: 1 Month

Python Course Outline for Data Science & Analytics

Section 1: Introduction to Python

Project: Simple Calculator App in Python

• Build a basic calculator that handles +, -, *, and / with user input.
• Practice running it in Jupyter Notebook.

Section 2: Python Basics

Project: Student Grade Analyzer

• Take input for student marks and calculate grades.
• Use type casting and operators to analyze pass/fail and grade bands.

Section 3: Control Structures

Project: Number Guessing Game

• Implement a number guessing game with limited attempts.
• Use loops and conditionals for logic and user interaction.

Section 4: Data Structures

Project: Contact Book

• Build a mini contact book using dictionaries and lists.
• Allow adding, deleting, searching, and updating contact details.

Section 5: Functions and Modules

Project: BMI Calculator with Custom Module

• Write a BMI calculator using functions.
• Create a separate module for reusable functions (e.g., height converter, BMI category logic).

Section 6: File Handling & Exception Handling

Project: Expense Tracker with File I/O

• Track daily expenses, save them to a .csv or .txt file.
• Implement error handling for invalid input or missing files.

Section 7: Working with NumPy

Project: Matrix Operations App

• Create two arrays and perform addition, subtraction, multiplication, and transpose.
• Apply reshaping and indexing to simulate image data or pixel arrays.

Section 8: Data Analysis with Pandas

Project: COVID-19 Case Analysis

• Use a CSV file of COVID-19 cases.
• Analyze top countries, daily growth, and cumulative trends using Pandas.

Section 9: Data Cleaning & Preparation

Project: Netflix Dataset Cleaner

• Use a messy Netflix shows dataset.
• Clean missing values, correct data types, and normalize strings (titles, genres).

Section 10: Data Visualization (Matplotlib & Seaborn)

Project: Visualize Sales Data

• Plot revenue trends, category-wise sales, and customer segments.
• Create heatmaps for region-wise performance.

Section 11: Working with Real-World Datasets

Project: Airbnb Price Analysis

• Import Airbnb listings from CSV or API.
• Perform EDA: identify most expensive locations, price trends, etc.

Section 12: Introduction to Statistics with Python

Project: Survey Data Insights Dashboard

• Analyze survey responses for mean, median, mode.
• Show distributions and correlation heatmaps between factors like age, income, and satisfaction.

Section 13: Intro to Machine Learning with scikit-learn

Project: Predict Student Scores (Linear Regression)

• Use a dataset of study hours and scores.
• Train a linear regression model, split train/test data, and evaluate R² score.

Section 14: Capstone Mini-Project

Project: Customer Churn Prediction

• Use telecom data to predict whether a customer will leave.
• End-to-end: load → clean → explore → model → present insights.

More Projects:

1. HackerNews Scrapper
2. Reduce lmage Size
3. Easy Video Player
4. GeeksforGeeks Article downloader
5. PDF to Text
6. Unstructured Supplemenrary Service Data
7. Duplicate Files remover
8. PNG to ICO converter
9. Find IMDB RatingsTerminal
10. Based Hangman Game