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HomeCoursesData Science & AnalyticsIntroduction to Computational Thinking and Data Science

Introduction to Computational Thinking and Data Science

By edX with MIT

Data Science & Analytics

8.4
•1 verified reviewTrustScore
Our ratings use a 10-star system for more precise quality assessment
Online Course
80 hours
Intermediate
Certificate Included
Visit Provider
Write a ReviewWrite a Story
TrustCourse Rating
Verified
B+
TRUST·SCORE
8.4
OUT OF 10
1 verified review
Very Good
Rating Distribution% of reviews
9-10
0%
8-9
100%
7-8
0%
6-7
0%
0-6
0%

DETAILED RATINGS

Content Quality
8.0
Instructor Delivery
9.0
Platform Experience
8.0
Value For Money
9.0
Learning Outcome
8.0
All reviews are verified by TrustCourse

Course Syllabus

1

Optimization and Knapsack Problem

2

Decision Trees and Dynamic Programming

3

Graphs and Graph Optimization

4

Plotting with PyLab

5

Stochastic Thinking and Random Walks

6

Probability and Distributions

7

Monte Carlo Simulations

8

Curve Fitting and Experimental Data

9

Machine Learning Fundamentals

10

Statistical Fallacies and Data Enhancement

TrustCourse AI AnalysisAI Generated
Based on public data analysis
•
Updated May 29, 2025
8.4/10
Very Good

Course Goal

Develop intermediate computational thinking and data science skills using Python, with a focus on practical problem-solving and foundational statistical methods.

One-Minute Verdict

MIT’s 6.00.2x is a challenging, in-depth Python-based data science course best for those with prior coding experience—demanding but highly respected by employers and academia.

PROS

  • Rigorous, MIT-authored curriculum
  • Challenging problem sets foster real skills
  • Free audit option; affordable certificate
  • Respected credential for grad school or jobs

CONS

  • Requires prior Python and math experience
  • High weekly time commitment
  • Some ML topics covered only briefly

DETAILED AI RATINGS

Content Quality
8.0/10

Covers core computational and data science topics with practical projects; some advanced ML content is brief.

Instructor Delivery
9.0/10

MIT professors deliver clear, engaging lectures; explanations praised for depth and approachability.

Platform Experience
8.0/10

edX platform is stable and accessible; lacks live coding but offers structured progression and auto-graded tasks.

Value For Money
9.0/10

Free to audit; certificate available for a modest fee, making it one of the best-value MIT-branded courses.

Learning Outcome
8.0/10

Graduates gain strong skills in computational modeling and data analysis; beginners may find pace steep.

DECISION GUIDE

Take This Course If

You have Python basics and want a deep, hands-on intro to computational data science.

Ideal for those seeking MIT-level rigor, practical coding, and a credential recognized by employers.

Skip This Course If

You lack Python experience or want beginner-paced, low-commitment content.

Not suitable for absolute beginners or those needing lots of hand-holding; expect challenging assignments.

SOURCES & REFERENCES

  • https://www.classcentral.com/report/review-intro-computer-science-programming-python-mit-edx/
  • https://github.com/Yunchieh/MITx-6.00.2x
  • https://coursecorrect.fyi/blog/data-science-courses-edx/
  • https://wayofnumbers.com/p/mit-6.00.1x/2x-review-a-data-scientists-point-of-view/
  • https://www.classcentral.com/course/computer-science-massachusetts-institute-of-techn-1779
  • https://ocw.mit.edu/courses/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/

About this AI Summary

This summary is generated by AI based on public internet data, including social networks and available articles.

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