Week 2: Data Wrangling and Cleaning

Importing datasets.

Data cleaning techniques.

Handling missing values and outliers.

Week 3: Exploratory Data Analysis

Descriptive statistics.

Data visualization tools (Matplotlib, Seaborn).

Identifying trends and patterns.

Week 4-5: Introduction to Machine Learning

Supervised vs. Unsupervised learning.

Key algorithms (e.g., Linear Regression, K-Nearest Neighbors, K-Means).

Hands-on projects with simple datasets.

Week 6: Advanced Topics

Introduction to Deep Learning.

Natural Language Processing (NLP).

Time-series analysis.

Week 7-8: Capstone Project

Students work on a real-world dataset.

Final presentation of findings and methods used.