1 GETTING STARTED 2 INTRODUCTION TO PYTHON 3 SOME SIMPLE NUMERICAL PROGRAMS 4 FUNCTIONS, SCOPING, AND ABSTRACTION 5 STRUCTURED TYPES and MUTABILITY 6 Recursion and Global variables 7 Modules and Files 8 TESTING AND DEBUGGING 9 EXCEPTIONS AND ASSERTIONS . 10 CLASSES AND OBJECT-ORIENTED PROGRAMMING 11 A SIMPLISTIC INTRODUCTION TO ALGORITHMIC COMPLEXITY 12 SOME SIMPLE ALGORITHMS AND DATA STRUCTURES . 13 PLOTTING AND MORE ABOUT CLASSES 14 KNAPSACK AND GRAPH OPTIMIZATION PROBLEMS 15 DYNAMIC PROGRAMMING 16 RANDOM WALKS AND MORE ABOUT DATA VISUALIZATION 17 STOCHASTIC PROGRAMS, PROBABILITY, AND DISTRIBUTIONS 18 MONTE CARLO SIMULATION 19 SAMPLING AND CONFIDENCE . 20 UNDERSTANDING EXPERIMENTAL DATA 21 RANDOMIZED TRIALS AND HYPOTHESIS CHECKING . 22 LIES, DAMNED LIES, AND STATISTICS 23 EXPLORING DATA WITH PANDAS 24 A QUICK LOOK AT MACHINE LEARNING 25 CLUSTERING 26 CLASSIFICATION METHODS PYTHON 3.8 QUICK REFERENCE INDEX
John V. Guttag is Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT.