Introduction to Computational Thinking and Data Science
Massachusetts Institute of Technology via MIT OpenCourseWare
-
177
-
- Write review
Lead AI Strategy with UCSB's Agentic AI Program — Microsoft Certified
Learn AI, Data Science & Business — Earn Certificates That Get You Hired
Overview
Syllabus
1. Introduction, Optimization Problems (MIT 6.0002 Intro to Computational Thinking and Data Science).
2. Optimization Problems.
3. Graph-theoretic Models.
4. Stochastic Thinking.
5. Random Walks.
6. Monte Carlo Simulation.
7. Confidence Intervals.
8. Sampling and Standard Error.
9. Understanding Experimental Data.
10. Understanding Experimental Data (cont.).
11. Introduction to Machine Learning.
12. Clustering.
13. Classification.
14. Classification and Statistical Sins.
15. Statistical Sins and Wrap Up.
Taught by
Prof. Eric Grimson , Prof. John Guttag and Dr. Ana Bell
Tags
Reviews
4.8 rating, based on 4 Class Central reviews
-
This course, Introduction to Computational Thinking and Data Science by MIT, is truly outstanding. The instructors explain complex concepts in a very simple and engaging way. I learned how to approach problems like a computer scientist and how to use Python for data analysis. The course structure is well-designed, and the examples are real-world and interesting. It gave me a strong foundation in both thinking logically and working with data. I highly recommend this course to anyone who wants to start learning data science or improve their problem-solving skills. It’s one of the best free course out there!
-
This is a very beginner friendly course just need a basic probability and statistics of school level.
-
"Introduction to Computational Thinking and Data Science by MIT OpenCourseWare provides a solid foundation in key concepts of data science, from computational thinking to practical applications. The course is well-structured, with clear explanations and engaging problem sets that help you build real-world skills. The course material is challenging but rewarding, and the inclusion of programming in Python makes it practical for beginners and experienced learners alike. I highly recommend it for anyone interested in computational thinking and data science."
-
The course “Introduction to Computational Thinking and Data Science” provides a comprehensive introduction to the fundamental concepts of computational thinking and data science. It offers a great opportunity for individuals looking to develop their analytical and problem-solving skills in the context of data analysis.
The course covers various essential topics, including algorithms, data structures, programming concepts, and statistical analysis. By understanding these concepts, students gain the necessary foundation to approach real-world problems and make informed decisions using data-driven insights.