This course walks you through the entire data science workflow, starting with data acquisition and cleaning, advancing into exploratory data analysis, and culminating in machine learning and predictive modeling. You’ll gain practical exposure by working on real datasets and business case studies.

Core modules include Python for data science, probability and statistics, supervised and unsupervised machine learning, model evaluation techniques, and real-time capstone projects. You’ll also gain exposure to domain-specific applications like finance, healthcare, retail, and e-commerce analytics.

By the end of the course, you’ll be confident in your ability to design, develop, and deploy end-to-end data science solutions.

1. Build strong foundations in Python, statistics, and ML algorithms
2. Perform data wrangling, feature engineering, and exploratory data analysis
3. Train, test, and evaluate machine learning models
4. Work on real-world datasets to solve predictive analytics problems
5. Create a data science portfolio for interviews and job readiness