Python for Data and AI

What you'll learn

• Write clear Python using core types, containers, comprehensions, functions and exception handling, and manage isolated environments with recorded package versions
• Read and write files, CSV and JSON safely, extract fields from text with string methods and regular expressions, and handle time zones and daylight saving time correctly
• Compute with NumPy arrays using axes, vectorized operations, broadcasting, Boolean masks, matrix multiplication and seeded random numbers
• Use Monte Carlo simulation, the bootstrap and permutation tests to estimate probabilities, uncertainty and the significance of differences
• Load, clean, filter, group, join and resample tabular and time-series data with pandas, handling missing values deliberately
• Profile and clean messy data with logged decisions, detect outliers robustly, prepare features and avoid data leakage
• Explore data with Matplotlib charts suited to each question, identify outliers and relationships, and interpret correlation honestly
• Summarize distributions, build confidence intervals, compare groups and proportions, recognize Simpson's paradox and interpret simple regression
• Train, evaluate and tune scikit-learn models with pipelines, cross-validation and cost-based thresholds
• Query relational databases with parameterized SQL, combine SQL with pandas, handle data larger than memory and collect data from APIs robustly
• Make analyses reproducible with clean notebooks, controlled seeds, pinned environments, versioned data, an organized project, Git and tests
• Carry a project from problem framing through baseline, model, error analysis, explanation and deployment with monitoring