📊 90-Day Python for Data Analysis Course Structure

Course Breakdown

• Duration: 12 Weeks (3 Months)

• Classes per Week: 3

• Total Classes: 54

• Each Class: 1.5 Hours

🟢 Month 1: Python + Data Handling Foundations (Weeks 1–4)

Goal: Build Python basics specifically for data analysis

Week 1: Python Basics Refresher

• Variables, data types

• Lists, tuples, dictionaries

• Basic operations & input/output

Week 2: Control Flow & Functions

• if-else conditions

• Loops (for, while)

• Functions for reusable code

Week 3: Introduction to Data Analysis

• What is Data Analysis

• Types of data (structured/unstructured)

• Installing and using Jupyter Notebook

Week 4: NumPy Basics

• Introduction to NumPy

• Arrays and operations

• Indexing, slicing

• Basic mathematical functions

🟡 Month 2: Data Manipulation & Visualization (Weeks 5–8)

Goal: Work with real datasets and perform analysis

Week 5: Pandas Fundamentals

• Introduction to Pandas

• Series and DataFrames

• Reading CSV/Excel files

Week 6: Data Cleaning

• Handling missing values

• Removing duplicates

• Data type conversion

Week 7: Data Transformation

• Filtering and sorting data

• GroupBy operations

• Merging and joining datasets

Week 8: Data Visualization

• Introduction to Matplotlib

• Basic plots (line, bar, histogram)

• Intro to Seaborn

• Creating meaningful visuals

🔵 Month 3: Advanced Analysis & Projects (Weeks 9–12)

Goal: Apply data analysis skills to real-world problems

Week 9: Exploratory Data Analysis (EDA)

• Understanding datasets

• Summary statistics

• Finding patterns & trends

Week 10: Working with Real Datasets

• Case study (sales, customer, or financial data)

• Data cleaning + visualization

Week 11: Introduction to SQL with Python

• Connecting Python with databases

• Basic SQL queries for analysis

Week 12: Projects & Final Assessment

• Project 1: Sales data analysis

• Project 2: Customer insights dashboard

• Final presentation & feedback

🕒 Typical Daily Class Structure (1.5 hrs)

• 20 min → Concept explanation

• 30 min → Live coding/demo

• 30 min → Hands-on dataset practice

• 10 min → Mini task

• 10 min → Doubt clearing