1. Foundations of Data Science: Understand data types, data collection, cleaning, wrangling, and essential statistical concepts.

2. Programming for Data Science: Build strong foundations in Python and its core data libraries NumPy, Pandas, and Matplotlib for analysis and automation.

3. Data Visualization: Learn to communicate insights effectively with charts, plots, and visual storytelling using Matplotlib and Seaborn.

4. Machine Learning Essentials: Get hands-on with algorithms for regression, classification, clustering, and recommendation systems, plus learn model evaluation and optimization using Scikit-Learn.

5. Databases & SQL: Master querying, managing, and analyzing structured datasets with SQL and database integration.

6. Big Data & Cloud Tools: Introduction to modern tools like Hadoop and Spark for handling large-scale data efficiently.

7. AI & Deep Learning Basics: Explore the fundamentals of neural networks, natural language processing (NLP), and computer vision with simple deep learning models.

8. Capstone Projects: Apply all your knowledge by working on real datasets ranging from business analytics to predictive modeling, building projects you can showcase to employers.