Data Analysis for Social Scientists

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7/10 stars
based on  18 reviews
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FREE

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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
22829 reviews

Course Description

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal.

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical...

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal.

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively, but it is challenging. Students who are uncomfortable with basic calculus and algebra might struggle with the pace of the class.

Reviews 7/10 stars
18 Reviews for Data Analysis for Social Scientists

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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.

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Roman Sujatinov profile image
Roman Sujatinov profile image
2/10 starsTaking Now
  • 0 reviews
  • 0 completed
1 year, 3 months ago
Quality of lectures is poor. Most of exercises and assignments are formulated in a way that allows double interpretation. You’ll be completely lost, if don’t have previous experience with the subject. Waste of time and money.
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Shen Pon profile image
Shen Pon profile image
2/10 starsTaking Now
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  • 0 completed
11 months ago
I will never recommend it to anyone. The quality is poor. Many statistical terms and concepts are introduced without explanation and discussion. The notations in both lectures and homeworks are confusing. I think this course should not be included in the learning map of MITx SDS Micromaster!
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Marc A profile image
Marc A profile image

Marc A

2/10 starsTaking Now
1 year, 3 months ago
This course is now part of the MITx Micro Master in Statistics and Data Science. After 6 weeks spent attending this course, I still do not understand its exact purpose. It remains unclear what it is trying to achieve with regards to the Statistics and Data Science Micromaster program. It is a less detailed and less rigorous probability course, compared to 6.431x. It is not an hands-on introduction to experimental design and real world hypothesis testing, although its title would have suggested that. It sometimes looks like a poor introduction to R, which is not a prerequisite of the SDS Micromaster. I feel puzzled at the end of each module, really asking myself what i actually learned. Would you be so kind to provide clarification on why this course is part of the SDS Micromaster? On top of that I can not stress how disappointed I am regarding the quality of the content. I understand there have been some technical hiccups and I... This course is now part of the MITx Micro Master in Statistics and Data Science. After 6 weeks spent attending this course, I still do not understand its exact purpose. It remains unclear what it is trying to achieve with regards to the Statistics and Data Science Micromaster program. It is a less detailed and less rigorous probability course, compared to 6.431x. It is not an hands-on introduction to experimental design and real world hypothesis testing, although its title would have suggested that. It sometimes looks like a poor introduction to R, which is not a prerequisite of the SDS Micromaster. I feel puzzled at the end of each module, really asking myself what i actually learned. Would you be so kind to provide clarification on why this course is part of the SDS Micromaster? On top of that I can not stress how disappointed I am regarding the quality of the content. I understand there have been some technical hiccups and I appreciate the efforts made to fix these, but that does not explain the poor quality of the video clips, the lack of rigor in the explanations, especially with the probability notation, the lengthy and sometimes confusing lectures and the lack of structure/challenge in the finger exercises and problem sets. I feel this course is below MIT standards.
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Antonello L. profile image
Antonello L. profile image
2/10 starsTaking Now
  • 0 reviews
  • 0 completed
1 year, 3 months ago
The nice, years-old reviews must be for a different course :-) This course is pretty recent, and with lots of problems (aside the errors, the content is too wide and at the same time not rigorous nor intuitive, the amount of material too much heterogeneous from one week to the other, things presented in classes and referenced in the videos are not made available to online content, poor editing.. I could continue..)
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M profile image
M profile image

M

4/10 starsCompleted
1 year, 3 months ago
This is a review of the fall 2018 session. This course is now part of the "Statistics and Data Science" micromaster series from MIT. Despite being offered previously it still contains a large amount of mistakes and vague formulations in the homework materials. The course staff provided the following excuse on the course forum "Some of these errors were caused because the wrong, prior version of the course was uploaded by IT". The quality of teaching in the videos is equally low. The teachers seem to treat mathematical formulas rather as an object of art and don't care about deriving them, making them error free, or even presenting in a readable instead of a strangely stylized font. Let me give some of the teacher's comments expressed during the lectures: "The proof is actually pretty straightforward, but I don't think it's necessary for me to show it." or "So, anyhow, this doesn't-- you don't have to understand or sort of co... This is a review of the fall 2018 session. This course is now part of the "Statistics and Data Science" micromaster series from MIT. Despite being offered previously it still contains a large amount of mistakes and vague formulations in the homework materials. The course staff provided the following excuse on the course forum "Some of these errors were caused because the wrong, prior version of the course was uploaded by IT". The quality of teaching in the videos is equally low. The teachers seem to treat mathematical formulas rather as an object of art and don't care about deriving them, making them error free, or even presenting in a readable instead of a strangely stylized font. Let me give some of the teacher's comments expressed during the lectures: "The proof is actually pretty straightforward, but I don't think it's necessary for me to show it." or "So, anyhow, this doesn't-- you don't have to understand or sort of completely have a clear intuition of what's going on here." If you have a background in probability and statistics you will be horrified by the mistakes, but you will be able to follow the material since it's pretty standard. If you don't - you will be completely lost and misguided. Other than those uncorrectable flaws (one would have to replace the teachers and re-record the whole course - that won't happen), the course has also a positive element. The teachers offer is a glimpse of how data analysis methods are used in their field of research and there are several homeworks that relate to this.
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student

2/10 starsTaking Now
1 year, 3 months ago
The course is total waste of time and money, I don't know what they were thinking, everything is made to demonstrate how muscular and strong the professor is, not to convey knowledge, this applies to exercise and quizzes, besides the amount of content in the given time is for a full time student not for self-based as they claimed, not to mention too many errors which get you distracted, especially when you're trying to understand new concepts, if it wasn't their first, I could easily say that it's fraud, and a way of give their book sales a push
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 profile image
 profile image

2/10 starsTaking Now
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  • 0 completed
1 year, 3 months ago
These nice reviews must be for the older version of the course. The current version is one of the worst courses I have taken. In description it says introductory, but actually you have to to be pretty familiar with statistics and mathematics behind, because nothing is explained in detail. This looks like a review course for professional statisticians. R tutorials are nice, but in a few homework assignments, the relevant tutorial is posted after the homework. Ridiculous. Not surprised this course is free, but wondering how MIT staff allowed a course in this condition to be run. I am going to complete this, I can pass these stupid assignments, but I am not learning anything. Really dissapointed in MIT.
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Jennifer E profile image
Jennifer E profile image

Jennifer E

6/10 starsTaking Now
1 year, 3 months ago
If I was rating this course based only on the information contained I would give it five stars; the topics covered in the syllabus are wide-ranging and of interest to anyone in the field of data science. Unfortunately, the presentation of the course within the platform is poor. Other MOOCs either provide copies of the slides or take care to summarize essential concepts in text within the same page of the video presentation; neither occurs here. The student is left to re-watch the video if they need to see the formula presented. This is video capture of a live course, and one is left with the sense that print material (textbook, handouts) necessary to fully understand some parts of the course is missing. Questions are frequently ambiguous, with answers that are sometimes directly counter to the material presented in lecture; more than once a question was ungraded after the fact as a result of this type of contradiction. The end... If I was rating this course based only on the information contained I would give it five stars; the topics covered in the syllabus are wide-ranging and of interest to anyone in the field of data science. Unfortunately, the presentation of the course within the platform is poor. Other MOOCs either provide copies of the slides or take care to summarize essential concepts in text within the same page of the video presentation; neither occurs here. The student is left to re-watch the video if they need to see the formula presented. This is video capture of a live course, and one is left with the sense that print material (textbook, handouts) necessary to fully understand some parts of the course is missing. Questions are frequently ambiguous, with answers that are sometimes directly counter to the material presented in lecture; more than once a question was ungraded after the fact as a result of this type of contradiction. The end result is a frustrating experience while learning some extremely interesting material.
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Vritti Bang profile image
Vritti Bang profile image

Vritti Bang

7/10 starsTaking Now
2 years, 7 months ago
The course is suitable for beginners and at the same time its challenging making it remarkably informative. The professors are great and this is definitely a course you’d want to take to dwell into statistics. Further, it’s MIT level education for free which can be backed my a micromasters degree from MIT helping you to apply to MIT or any school in general. Even though the R program is useful to learn as a beginner in this stream you would find it difficult to wrap your head around it. Since the course work is comprehensive and extensive I’m disappointed that we don’t get a certificate.
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Melaine Nyuyfoni Nsaikila profile image
Melaine Nyuyfoni Nsaikila profile image

Melaine Nyuyfoni Nsaikila

8/10 starsTaking Now
2 years, 8 months ago
Economist By Profession and Academic Training. Course content is great and I think the Provide did a detailed analysis of the market needs before coming up with this. It is refreshing and One new thing I am learning is the R Programming Language. This adds to SAS and STATA which I am versed with. Thanks
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student

6/10 starsTaking Now
2 years, 8 months ago
Academically sociologist and data analysis has always remained crucial challenge in my research paper. Hope to gather more knowledge from this course in terms of data analysis.
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Ishvinder Sethi profile image
Ishvinder Sethi profile image

Ishvinder Sethi

9/10 starsTaking Now
2 years, 9 months ago
Providers are best. It's a good Course for beginners. Data analysis is one of the market firing topic, it has a wide scope in future. And a free course from MIT. nothing more anyone needs.
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student

7/10 starsCompleted
3 years, 1 month ago
This was a great class overall: interesting subject, useful examples, and engaging lectures. However, it is not quite data analysis for social scientists. I work in a research company and I found only about half of the lectures relevant to my job: regression models and techniques, study designs, machine learning, prediction, data visualization - all fascinating stuff and I learned quite a bit. The other half of the lectures go deep into probability theory, most of which I found unnecessarily detailed for the job (while knowing the fine mechanics of what is happening behind the scenes when running a regression in a software package is relevant, however, it is not necessary to successfully design and implement studies, in my opinion). I found that most of the statistical lingo and examples used in those lectures were largely applicable to engineers, not social scientists (ex. we don't report the expectation of a variable in our reports... This was a great class overall: interesting subject, useful examples, and engaging lectures. However, it is not quite data analysis for social scientists. I work in a research company and I found only about half of the lectures relevant to my job: regression models and techniques, study designs, machine learning, prediction, data visualization - all fascinating stuff and I learned quite a bit. The other half of the lectures go deep into probability theory, most of which I found unnecessarily detailed for the job (while knowing the fine mechanics of what is happening behind the scenes when running a regression in a software package is relevant, however, it is not necessary to successfully design and implement studies, in my opinion). I found that most of the statistical lingo and examples used in those lectures were largely applicable to engineers, not social scientists (ex. we don't report the expectation of a variable in our reports, we report the means). The workload was heavy (especially if you have a full-time job) but it was as described and I found the exercises quite useful to solidify the material. It is helpful if you know R or another statistical software as the exercises had quite a bit of mistakes in the written codes, that way you can find the errors and not get stuck.
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ABDUL BASIT profile image
ABDUL BASIT profile image

ABDUL BASIT

9/10 starsTaking Now
3 years, 1 month ago
This is one of the best out there as good as andre ng ML or dr Yaser ML or analaytics edge by MITX. one of the best courses out there thorough but simple as well.
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Igor Barkhatov profile image
Igor Barkhatov profile image

Igor Barkhatov

10/10 starsTaking Now
3 years, 5 months ago
Interesting, a lot of real exercises and complicated than I thought before the beginning of course. Also experiance with R is very useful.
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Nirav Lakhani profile image
Nirav Lakhani profile image

Nirav Lakhani

10/10 starsTaking Now
3 years, 5 months ago
Since graduating university in 2014 I have constantly tried to improve my skill set to land a job with JPAL. I was taught STATA as an undergrad but always wanted to learn more/transfer my skill set over to R. This course far surpasses my desires to grow as an econometrician. I am so thankful to MIT for providing this course and their continued support to reach the underprivileged while conducting rigorous research to inform policymakers. I think its great that the instructors are going above and beyond the standard economists skill set to make the coursework relevant to new technology and research methods, specifically machine learning and data visualization. I cannot thank you enough for this course. I look forward to applying for a job with JPAL after this course.
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Eric Do profile image
Eric Do profile image

Eric Do

10/10 starsTaking Now
3 years, 5 months ago
Just one look at the syllabus and I'm seeing so much value. Such rigorous and thorough courses like this are hard to come by. However, there's so surprise because it is from MIT :D Thank you instructors and course staffs for making this course happen! I greatly look forward to it.
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Lekan Ogunjobi profile image
Lekan Ogunjobi profile image
9/10 starsTaking Now
  • 1 review
  • 0 completed
3 years, 6 months ago
Seriously, I am short of words to describe how happy I was when I saw this course on J-PAL Facebook page. For those in the developing world, conducting meaningful research has been the major challenge we have been battling with. With this course, we would be exposed to standard methods of analysing data to yield meaningful results. Also, with the expertise of Prof. Esther (which I always covet positively), and other instructors - I believe this course will be a memorable one. Lastly, I would like to request if Foundation for Development Policy or the challenge of the world poverty could be postponed so that we would able to handle this data analysis and either of those mentioned above together effectively. Those development related courses are so important to us that we cannot not just do any trade off. Thanks for the understanding. Best regards
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