This end-to-end program takes you from absolute beginner to confident practitioner across the full data science lifecycl...
Understand the data science landscape, roles, workflows, and tools. Learn problem framing, reproducibility, and ethical principles that guide successful projects from idea to impact.
Gain fluency in Python as the primary language for data work. Write clean, maintainable code and leverage core libraries to build reliable data science workflows.
Acquire, access, and manage data from files, web APIs, and relational databases. Write efficient SQL and design robust data access patterns.
Transform raw data into analysis-ready datasets using vectorized operations and powerful tabular transformations with NumPy and Pandas.
Assess data quality and apply systematic cleaning, encoding, and transformation techniques to prepare robust training and analysis datasets.
Extract insights through statistical exploration and clear visual narratives. Learn to choose appropriate charts and communicate findings effectively.
Build statistical intuition for uncertainty, inference, and experimentation. Apply hypothesis testing and estimation to data-driven decisions.
Learn core ML principles, from data splits and metrics to bias variance and optimization. Set up reliable, reproducible training workflows.
Implement and compare core supervised models for regression and classification. Understand practical tradeoffs and interpretation.
Discover structure in unlabeled data and reduce dimensionality for visualization, compression, and improved learning.
Choose appropriate metrics, validate rigorously, and tune models systematically for robust, generalizable performance.
Craft informative features and build maintainable pipelines that automate preprocessing and modeling for repeatable results.
Model temporal data with classical and modern methods. Evaluate forecasts and deploy scalable time series solutions.
Process and model text data from traditional vectorization to modern transformer based approaches for NLP applications.
Learn neural network fundamentals and apply practical MLOps to ship, monitor, and maintain production grade AI systems.