Data Science
Introduction to Data Science
Data lifecycle, data types, exploratory data analysis and reproducible workflows.
Statistics & Probability
Descriptive stats, distributions, hypothesis testing, confidence intervals and basic probability.
Data Wrangling with Pandas
Loading data, cleaning, transforming, merging, aggregation and handling missing values using pandas.
Data Visualization
Principles of visualization, matplotlib, seaborn, and storytelling with data.
Machine Learning Basics
Supervised vs unsupervised learning, linear models, decision trees, evaluation metrics and cross-validation.
Model Deployment & MLOps
Model serialization, APIs, basic containerization, monitoring and reproducible pipelines.
Big Data & Tools
Introduction to big data concepts, Spark, distributed processing and data storage patterns.