📚 Course Materials & Resources

Complete learning materials, assessments, exercises, and resources for all 12 courses

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Python for Data Science

Data Analysis and Scientific Computing with Python

📖 Course Overview

Master Python fundamentals, data manipulation with Pandas, visualization with Matplotlib, and introduction to machine learning concepts.

Duration

6-8 weeks

Level

Beginner to Intermediate

Modules

Lessons

🎯 Learning Outcomes

✓ Write Python code for data processing and analysis
✓ Use Pandas for data manipulation and cleaning
✓ Create visualizations with Matplotlib and Seaborn
✓ Work with CSV, Excel, and JSON data formats
✓ Perform statistical analysis on datasets
✓ Build simple machine learning models
✓ Use Jupyter notebooks for data exploration
✓ Automate data processing tasks

📋 Course Topics

Python Fundamentals

Syntax, variables, data types, control flow

⏱ ~5 hours

Functions & Modules

Function definition, imports, libraries

⏱ ~4 hours

Data Structures

Lists, tuples, dictionaries, sets

⏱ ~4 hours

Working with Files

Reading/writing files, JSON, CSV processing

⏱ ~3 hours

NumPy Fundamentals

Arrays, mathematical operations, matrix operations

⏱ ~4 hours

Pandas for Data Analysis

DataFrames, data cleaning, grouping, merging

⏱ ~5 hours

Data Visualization

Matplotlib, Seaborn, creating meaningful charts

⏱ ~4 hours

Intro to Machine Learning

Basic ML concepts, scikit-learn introduction

⏱ ~5 hours

🚀 Course Projects

Data Cleaning & Analysis Project

Clean a real-world dataset and perform exploratory analysis

Difficulty: Intermediate

⏱ ~12 hours

Data Visualization Dashboard

Create comprehensive visualizations from a dataset

Difficulty: Intermediate

⏱ ~10 hours

Predictive Model Project

Build and evaluate a simple machine learning model

Difficulty: Advanced

⏱ ~15 hours

📥 Course Resources

Resources coming soon

✅ Assessments

Quizzes

Python Syntax Quiz

20 questions • 30 minutes • 70% to pass

Pandas & Data Analysis Quiz

15 questions • 60 minutes • 75% to pass

Capstone & Projects

Data Analysis Project

Portfolio • ~600 minutes

Clean and analyze a real-world dataset

📋 Prerequisites

Basic programming knowledge (helpful but not required)
High school mathematics
Curiosity about data

🛠️ Required Tools

Python 3.8+
Anaconda or pip package manager
Jupyter Notebook
VS Code
Google Colab (alternative to Jupyter)

Course Information

Level:
Beginner to Intermediate

Duration:
6-8 weeks

Weekly Commitment:
10-12 hours per week

Price:
Free

✓ Certificate Available

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