Introduction to Computational Thinking and Data Science

Provided by:
9/10 stars
based on  22 reviews
Provided by:
Cost FREE
Start Date Upcoming
Introduction to Computational Thinking and Data Science

Course Details

Cost

FREE

Upcoming Schedule

  • Upcoming

Course Provider

edX online courses
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be tau...
Harvard University, the Massachusetts Institute of Technology, and the University of California, Berkeley, are just some of the schools that you have at your fingertips with edX. Through massive open online courses (MOOCs) from the world's best universities, you can develop your knowledge in literature, math, history, food and nutrition, and more. These online classes are taught by highly-regarded experts in the field. If you take a class on computer science through Harvard, you may be taught by David J. Malan, a senior lecturer on computer science at Harvard University for the School of Engineering and Applied Sciences. But there's not just one professor - you have access to the entire teaching staff, allowing you to receive feedback on assignments straight from the experts. Pursue a Verified Certificate to document your achievements and use your coursework for job and school applications, promotions, and more. EdX also works with top universities to conduct research, allowing them to learn more about learning. Using their findings, edX is able to provide students with the best and most effective courses, constantly enhancing the student experience.

Provider Subject Specialization
Sciences & Technology
Business & Management
22611 reviews

Course Description

6.00.2x will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving . This course is aimed at students with some prior programming experience in Python and a rudimentary knowledge of computational complexity. You will spend a considerable amount of time writing programs to implement the concepts covered in the course. For example, you will write a program that will simulate a robot vacuum cleaning a room or will model the population dynamics of viruses replicating and drug treatments in a patient's body.

Topics covered include:

  • Plotting with the pylab package
  • Random walks
  • Probability, Distributions
  • Monte Carlo simulations
  • Curve fitting
  • Knapsack problem, Graphs and graph optimization
  • Machine learning basics, Clustering algorithms
  • Statistical fallacies
Reviews 9/10 stars
22 Reviews for Introduction to Computational Thinking and Data Science

Ratings details

  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.

Sort By
Shruti Shah profile image
Shruti Shah profile image

Shruti Shah

10/10 starsCompleted
3 years, 8 months ago
Amazing course with lots of study material. Professor explains each concept precisely. Good homework material
Was this review helpful? Yes0
 Flag
Petr Kosvanec profile image
Petr Kosvanec profile image

Petr Kosvanec

10/10 starsCompleted
3 years, 10 months ago
(4) Get the book, the study is kind of lopsided (you by far not learn as much) without it. (5) possibly have a backup notebook / PC, ... Is it too much only to prepare for the thing? The course is worth it, trust me. Petr Kosvanec
Was this review helpful? Yes0
 Flag
William Vicars profile image
William Vicars profile image

William Vicars

10/10 starsCompleted
3 years, 10 months ago
Excellent course! Probably the best MOOC I've taken so far. Took me from programming newbie to pretty comfortable novice. I'm now taking more advanced courses in algorithms, machine learning, artificial intelligence, and IoT.
Was this review helpful? Yes0
 Flag
Richard Reddy profile image
Richard Reddy profile image

Richard Reddy

10/10 starsCompleted
3 years, 10 months ago
Having background in CS, some parts of the course were familiar territory. Other topics were entirely new. Lectures are very concise and closely connected to the exercises. The assignments are difficult, but they are worth the effort and deal with real-world computing. This course is enlightening even if you have prior course work in data structures, algorithms and statistics, because the context is applications as opposed to a strictly academic approach. Apportion generous amounts of time and you'll enjoy this course.
Was this review helpful? Yes0
 Flag
Student profile image
Student profile image

Student

8/10 starsCompleted
3 years, 10 months ago
The content of the course was great, and it is apparent that the teaching staff had put in a lot of time to produce this course. However, the wording of the exercises and exam questions were often unclear making the experience of taking the course full of frustration. Many times you tried guessing what they meant, wasting hours and hours, just to find out at the end that they wanted to see a very specific expression. In comparison to the 1st part of the series, many exercises have no complementary explanations. Pressing the "show answer" button just shows you the correct answer choice. To exacerbate the situation, the staff recruited a bunch of community TAs from the student pool at the beginning and took off for the rest of the course. There was essentially no knowledgeable teaching staff from MIT monitoring the course to answer questions or addressing issues. Many of the questions students asked in the forum were left unanswere... The content of the course was great, and it is apparent that the teaching staff had put in a lot of time to produce this course. However, the wording of the exercises and exam questions were often unclear making the experience of taking the course full of frustration. Many times you tried guessing what they meant, wasting hours and hours, just to find out at the end that they wanted to see a very specific expression. In comparison to the 1st part of the series, many exercises have no complementary explanations. Pressing the "show answer" button just shows you the correct answer choice. To exacerbate the situation, the staff recruited a bunch of community TAs from the student pool at the beginning and took off for the rest of the course. There was essentially no knowledgeable teaching staff from MIT monitoring the course to answer questions or addressing issues. Many of the questions students asked in the forum were left unanswered. While community TAs were clearly trying to help, many of their answers were unclear and sometimes misleading. When they indicated that the problems were escalated to staff's attention, there were no staff followup. Until MIT revamps this course, it can be a frustrating experience to work on their exercises and exam questions.
Was this review helpful? Yes0
 Flag
Kunal Kalore profile image
Kunal Kalore profile image

Kunal Kalore

8/10 starsCompleted
3 years, 11 months ago
It was an good experience.I liked the teaching and course material provided for the course.Instructor was really good,he made some concepts understand practically.
Was this review helpful? Yes0
 Flag
Anirban Dutta profile image
Anirban Dutta profile image

Anirban Dutta

10/10 starsCompleted
4 years ago
Simply the best course available. I would say it scores over CS50x since once you start using Python you realize how much code shortening is possible, while they still use C in CS50x. Python is really a pleasure to work with. It could not have been better. The only place where CS50x scores over this is its more fun. Final Verdict: 6.00.1 and 2x are the much better
Was this review helpful? Yes0
 Flag
 profile image
 profile image

2/10 starsDropped
  • 1 review
  • 0 completed
4 years, 3 months ago
This course should be called: Monte Carlo simulations using python, plotting and and relevant statistical topics Fundamentals of data science should cover: ETL Data bases (relational,graphic,NoSQL etc) Data exploration. Predictive modeling Clustering etc.
Was this review helpful? Yes0
 Flag
Tarun Goyal profile image
Tarun Goyal profile image

Tarun Goyal

10/10 starsCompleted
4 years, 5 months ago
A must for anyone getting into Data Science.
Was this review helpful? Yes0
 Flag
Mashimo profile image
Mashimo profile image
10/10 starsCompleted
  • 4 reviews
  • 4 completed
4 years, 8 months ago
This was one of the best courses I did on edX. I didn't know about data science nor Python before (I didn't take the 6.00.1x course) but I have programming experience that helped me. The lessons and the instructor were clear and enjoyable. The assignments interesting and fun, although the grader was very picky sometimes forcing you into spending extra hours to polish a solution that was already working, just to make it exactly like the grader wanted.
Was this review helpful? Yes0
 Flag
Ervin Lang profile image
Ervin Lang profile image
10/10 starsCompleted
  • 5 reviews
  • 5 completed
4 years, 9 months ago
I found it just perfect. The pace, the materials, I can't come up with anything. Maybe a including a project as part of the course in the future.
Was this review helpful? Yes0
 Flag
Francisco Goitia profile image
Francisco Goitia profile image
8/10 starsCompleted
  • 7 reviews
  • 6 completed
4 years, 10 months ago
Interesting and worth doing it, but it's not as good as the first part and some weeks had the feeling of being too superficial (machine learning for example). I'm glad I did it since it gave a good understanding of simulations and some general (although certainly superficial) knowledge of what a data scientist does. Some Problem Sets are amazing (the robot simulation, the drug-patient simulation and the weighted graphs applied to MIT campus come to mind) and others are dissapointing (machine learning). The quiz is mainly theory and it's very easy. The final exam is also mainly theory and some parts can be quite tricky. Overall, I recommend it.
Was this review helpful? Yes0
 Flag
Kevin Zhu profile image
Kevin Zhu profile image
10/10 starsTaking Now
  • 7 reviews
  • 5 completed
4 years, 11 months ago
Was this review helpful? Yes0
 Flag
Richard Kirkpatrick profile image
Richard Kirkpatrick profile image
9/10 starsCompleted
  • 6 reviews
  • 6 completed
5 years, 4 months ago
I am about a week from completing this course. So far, I have been very impressed by MIT once again. This course continues on 6.00.1x "Intro to Computer Science and Programming Using Python". My review from that course will be similar with only a few additional comments. PROS: * The professors are fantastic lecturers. Everything is explained very well with visual representations, well-crafted notes and slides, and great examples. * The homework assignments were very challenging and fun. They are much more challenging than 6.00.1x so be ready to spend some extra time on them. The programming assignments in this course are the most helpful mechanism for learning computer programming concepts. * Teaching Assistants and Staff were great when it comes to addressing questions or issues on the forums. They were very courteous, even when people were frustrated and angry. * The grader servers were much improved from last semester. During 6.00... I am about a week from completing this course. So far, I have been very impressed by MIT once again. This course continues on 6.00.1x "Intro to Computer Science and Programming Using Python". My review from that course will be similar with only a few additional comments. PROS: * The professors are fantastic lecturers. Everything is explained very well with visual representations, well-crafted notes and slides, and great examples. * The homework assignments were very challenging and fun. They are much more challenging than 6.00.1x so be ready to spend some extra time on them. The programming assignments in this course are the most helpful mechanism for learning computer programming concepts. * Teaching Assistants and Staff were great when it comes to addressing questions or issues on the forums. They were very courteous, even when people were frustrated and angry. * The grader servers were much improved from last semester. During 6.00.1x, the graders crashed numerous times. I recall the graders only crashing once during 6.00.2x. CONS; * The most challenging programming assignment was on Weighted Digraphs. Although I was able to solve this problem, I spent 20+ hours on this assignment. Personally, I do not mind spending this amount of time on programming. However, MIT may want to consider methods for making this problem more intuitive for other students. * I thought the Problem Set on Machine Learning could have been slightly more challenging. It took me about 1 - 2 hours to finish. MIT might considering adding "Bonus/Optional Problem Sets" for students seeking more challenge. As I mentioned before, this course is very challenging and you will learn a lot if you are devoted and disciplined. If you are looking for a course that is less challenging, I would recommend looking elsewhere.
Was this review helpful? Yes5
 Flag
Steven Frank profile image
Steven Frank profile image
10/10 starsCompleted
  • 59 reviews
  • 57 completed
5 years, 6 months ago
This class picks up where 6.00.01x leaves off, applying the programming skills you've learned to some important topics in data science -- simulations, probability and the use (and misuse) of statistics, the interpretation of experimental data, graph problems, optimization, and machine learning. As in 6.00.01x, the lectures are first-rate and the textbook is not only the best computer-science primer I've ever read, but the best I can imagine -- it's readable and clear, and frequently witty. I've found it to be an excellent reference and refer to it often. Although the topics in 6.00.02x concern "data science and computational thinking," the heart of the problem sets is still programming; so if you're afraid you've left the challenging world of classes, inheritance and recursion behind last semester, you can rest easy -- they're back.
Was this review helpful? Yes4
 Flag
student profile image
student profile image

student

10/10 starsCompleted
5 years, 6 months ago
As the name suggests, this course reinforces the concept of computational thinking and introduces several advanced concepts in data science with minimal requirement of programming proficiency. It serves as a motivation for the beginners to believe how much can be achieved with little programming experience and encourage them to explore them further. A great complement to the introductory course. Looking forward to the next one in the series......
Was this review helpful? Yes3
 Flag
H . profile image
H . profile image
7/10 starsCompleted
  • 11 reviews
  • 10 completed
5 years, 6 months ago
This class is a good introduction to a wide variety of subjects if you don't have much programming experience. But it's not for experienced programmers. You'll learn simple, brute-force solutions to common science problems, but often you won't learn the canonical algorithmic solutions taught in algorithm classes. And it's not really an introduction to data science -- more an introduction to an introduction to data science. The lecturers are engaging, the assignments easy for those with programming experience. The quiz and final can have very frustrating questions that are ill-conceived. If this class is supposed to be about how to write simulations to measure phenomena, you'll find that many quiz/final questions actually ask you to predict how a simulation would behave *without* running the simulation. This is silly in my opinion as it defeats the purpose of a simulation and the course doesn't properly prepare you to answer these sci... This class is a good introduction to a wide variety of subjects if you don't have much programming experience. But it's not for experienced programmers. You'll learn simple, brute-force solutions to common science problems, but often you won't learn the canonical algorithmic solutions taught in algorithm classes. And it's not really an introduction to data science -- more an introduction to an introduction to data science. The lecturers are engaging, the assignments easy for those with programming experience. The quiz and final can have very frustrating questions that are ill-conceived. If this class is supposed to be about how to write simulations to measure phenomena, you'll find that many quiz/final questions actually ask you to predict how a simulation would behave *without* running the simulation. This is silly in my opinion as it defeats the purpose of a simulation and the course doesn't properly prepare you to answer these scientific intuition questions (unless you do a lot of experiments beyond the assignments on your own, or maybe read the included textbook, which is not required reading). It's a decent course but I wouldn't recommend this course to any of my friends, as I don't feel that I got much out of it.
Was this review helpful? Yes3
 Flag
student profile image
student profile image

student

7/10 starsCompleted
5 years, 6 months ago
This class covers a lot of ground, but none of it in any depth. Sufficient as a very basic introduction to modelling, simulations and data munching, follow- up classes covering same in more detail would be highly recommended. Some homework assignments are extremely messy from the design standpoint, and, in my opinion, should be considered harmful for the well-being of budding software developers taking this class.
Was this review helpful? Yes3
 Flag
kr strug profile image
kr strug profile image
10/10 starsCompleted
  • 9 reviews
  • 8 completed
3 years, 9 months ago
A follow-up on 6.00.1x which is a general intro to python. Here some more useful problems are tackled like clasification, plotting using matpotlib, random walks. Very god lectures and very god execices.
Was this review helpful? Yes1
 Flag
Dmitriy Alexeev profile image
Dmitriy Alexeev profile image
10/10 starsCompleted
  • 3 reviews
  • 3 completed
5 years, 6 months ago
Good course. I've got a flavour of what is it to solve real life problems computationally. I especially thankful for intro to Machine Learning since I'm not going to take a whole course about ML yet, but I was really interested in the theme.
Was this review helpful? Yes1
 Flag
student profile image
student profile image

student

10/10 starsCompleted
5 years, 7 months ago
Excellent course. Great teachers and TAs. Prof Guttag explains things very well. According to me you should have decent knowledge of python for this course at the level of 6.00.1x which is also on edx.
Was this review helpful? Yes1
 Flag
student profile image
student profile image

student

9/10 starsCompleted
5 years, 6 months ago
Great course.It helped me build a strong foundation in data science.The simulations and modelling of the data were very well covered.An amazing course.
Was this review helpful? Yes0
 Flag

Rating Details


  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars
  • 5 stars
  • 4 stars
  • 3 stars
  • 2 stars
  • 1 stars

Rankings are based on a provider's overall CourseTalk score, which takes into account both average rating and number of ratings. Stars round to the nearest half.