Learn how to gather data from various sources and clean it to ensure it’s ready for analysis.

Explore data using statistical techniques to uncover hidden patterns and relationships.

Master the statistical methods used to summarize and analyze data, including hypothesis testing.

Learn how to create compelling visualizations to communicate your findings effectively using libraries like Matplotlib and Seaborn.

Get an overview of machine learning and learn how to implement algorithms like linear regression, decision trees, and k-nearest neighbors (KNN).

Dive deeper into regression and classification algorithms, and understand how to evaluate model performance.

Explore clustering and dimensionality reduction methods like K-means and PCA.

Understand the basics of neural networks and how they are used to solve complex problems.

Learn how to deploy models and integrate them into production systems.