Course Curriculum

• •Python for data analysis (NumPy, Pandas)
• •Data wrangling & cleaning
• •Exploratory data analysis
• •SQL for data scientists

Project: Clean and analyze a messy real-world dataset end to end.

• •Descriptive & inferential statistics
• •Hypothesis testing
• •Probability distributions
• •A/B testing fundamentals

Project: Design and analyze an A/B test with real experiment data.

• •Supervised learning (regression, classification)
• •Unsupervised learning (clustering, dimensionality reduction)
• •Model evaluation & tuning
• •Feature engineering

Project: Build and tune a churn-prediction model.

• •Neural network fundamentals
• •Computer vision basics
• •NLP & transformer models
• •Working with pretrained models

Project: Fine-tune a pretrained model for a text classification task.

• •Model deployment (APIs, Docker)
• •Monitoring & versioning
• •Capstone project
• •Portfolio & interview prep

Capstone: Ship an end-to-end ML product with a live API.