The Data Science Foundations course is a beginner-friendly, hands-on program designed to introduce learners to the essential tools, techniques, and thinking patterns used in the world of data science. Using Python and real-world datasets, this course guides you through the full data science pipeline—from raw data to actionable insights—through clear, structured lessons and practical assignments.

You’ll start with core libraries like NumPy, pandas, and Matplotlib to explore, clean, and visualize data. As you progress, you’ll develop foundational statistical knowledge and apply machine learning algorithms like linear regression, k-nearest neighbors, and k-means clustering using scikit-learn. You’ll also dive into essential topics like feature scaling, model evaluation, and time-series analysis, giving you the skills to work with different types of data.

By the end of the course, you’ll create your own interactive dashboard using Streamlit, analyze a real-world dataset, and deliver a capstone project that demonstrates your ability to work through a complete data science workflow—from exploration to presentation.

This course is ideal for beginners in programming, aspiring data analysts, or professionals transitioning into data-related roles. No prior experience is required—just a passion for learning and problem-solving.