6.0002 Introduction to Computational Thinking and Data Science

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MIT’s 6.0002 Introduction to Computational Thinking and Data Science is an undergraduate course that builds on introductory Python programming and focuses on using computation to understand and solve real-world problems. The course develops computational thinking through optimization, graph-theoretic models, stochastic processes, random walks, Monte Carlo simulation, probability, confidence intervals, sampling, and experimental data analysis. Rather than treating programming as an end in itself, it emphasizes constructing computational models, running simulations, analyzing results, and using quantitative reasoning to make conclusions. 

The second half introduces the foundations of machine learning, particularly clustering and classification, while also emphasizing the dangers of statistical mistakes and misleading interpretations of data. Students work primarily in Python through five programming problem sets involving applications such as transportation optimization, graph models, robot simulation, disease spread, and global warming. 

Overall, the course provides a strong bridge between programming, mathematics, statistics, and data science, making it particularly useful for learning how computational methods can be used to investigate complex phenomena. 

MIT OpenCourseWare — 6.0002 Introduction to Computational Thinking and Data Science