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Online Course

Data Analysis for Social Scientists

Massachusetts Institute of Technology via edX

Overview

This course is now part of two independent MITx MicroMasters programs. For both MicroMasters programs, learners will need to first enroll in and pass this course. However, each program will then require different final assessments for a course certificate toward the full MicroMasters credential:

1.MicroMasters in Data, Economics, and Development Policy (DEDP).

To pursue the DEDP MicroMasters credential, pass this course, create aMicroMasters in DEDP profile, and pass an additional in-person proctored exam.

To learn more about the DEDP program and how it integrates with MIT’s new blended Master’s degree, please visithttps://micromasters.mit.edu/dedp/.

2.MicroMasters in Statistics and Data Science (SDS).
To pursue the SDS MicoMasters credential, pass this course, and enroll in and pass the final assessment at14.310Fx Data Analysis in Social Sciences-Assessment on EdX.

Complete all 4 courses and the capstone exam in the SDS program to accelerate your path towards graduate studies at MIT or other universities. To learn more, please visithttps://micromasters.mit.edu/ds.

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.

Course Previews:

Our course previews are meant to give prospective learners the opportunity to get a taste of the content and exercises that will be covered in each course. If you are new to these subjects, or eager to refresh your memory, each course preview also includes some available resources. These resources may also be useful to refer to over the course of the semester.

A score of 60% or above in the course previews indicates that you are ready to take the course, while a score below 60% indicates that you should further review the concepts covered before beginning the course.

Please use the this link to access the course preview.

Syllabus

14.310x – Data Analysis for Social Scientists

Week One: Introduction
Week Two: Fundamentals of Probability, Random Variables, Joint Distributions and Collecting Data
Week Three: Describing Data, Joint and Conditional Distributions of Random Variables
Week Four: Functions and Moments of a Random Variables & Intro to Regressions
Week Five: Special Distributions, the Sample Mean, the Central Limit Theorem
Week Six: Assessing and Deriving Estimators - Confidence Intervals, and Hypothesis Testing
Week Seven: Causality, Analyzing Randomized Experiments, & Nonparametric Regression
Week Eight: Single and Multivariate Linear Models
Week Nine: Practical Issues in Running Regressions, and Omitted Variable Bias
Week Ten: Endogeneity, Instrumental Variables, and Experimental Design
Week Eleven: Intro to Machine Learning and Data Visualization
Optional: Writing an Empirical Paper

Taught by

Esther Duflo and Sara Fisher Ellison

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Reviews

3.2 rating, based on 13 reviews

Start your review of Data Analysis for Social Scientists

  • James Solomon-Rounce completed this course, spending 12 hours a week on it and found the course difficulty to be medium.

    Writing a review for this course is hard. The content of the course is ambitious and the promise is considerable. I am grateful that the Professors and MIT have made this course available online. That being said, I find it hard to recommend this course....
  • Seylan Naidoo

    Seylan Naidoo completed this course.

    I did not enjoy this course at all. Here are the main reasons why: 1) Lecture videos. The lecturers themselves might be masters in their respective fields, but the lecture videos are not suitable for an online course. The videos are literally from the...
  • Dileep Nackathaya

    Dileep Nackathaya completed this course, spending 12 hours a week on it and found the course difficulty to be medium.

    tl;dr - poorly put together MOOC trying to cram too many things into one course. Doesn't leave you with a lot of confidence that you can analyse data independently on big projects. Confusing approach without concrete examples and demos of how to run...
  • Profile image for Ivan Z
    Ivan Z

    Ivan Z is taking this course right now.

    I am taking this course because it is required for my edx micromasters. It is a second MIT course in my specialization and a 5th MIT course on edx. All of the courses have been great. None of them were introductory or easy. It's a lot of work (although...
  • Antonello L. is taking this course right now, spending 15 hours a week on it and found the course difficulty to be easy.

    It is a strange "mixed beast" course.
    At the end I don't know what this course is good for. Too many different things (prob theory, programming in R, Statistics..) approached superficially, and most important, without even give the "intuitions" behind what it is used..
    Not much added value, and honestly I don't know why it has been added to the list of required courses for the new MIT MicroMaster program in data science.
  • Paul F. Groepler Sr. is taking this course right now, spending 16 hours a week on it and found the course difficulty to be hard.

    To say this class is thorough is an understatement. The lectures are extremely detailed, sometimes with additional detailed references(!), and it occasionally warrants going back and replaying one or two of the lectures before moving on. There is a good deal of statistics and probability review and training prior to getting to the "methods" of this class (around week 8). I recommend this course as I cannot imagine a better, more thorough treatment for the topic, taught by some of the "best" there are out there today in Economics and Statistics.
  • Ayse N.

    Ayse N. is taking this course right now, spending 15 hours a week on it and found the course difficulty to be very hard.

    There is just too much theory in the course. I only wanted to learn some Data Analysis and possibly Machine Learning. But no, it just doesn't happen. I was excited about this course, but there is little practical value compared to the effort you need to spend on the coursework. Unless you are already good at multivariable calculus and probability theory at the level of this course: https://www.edx.org/course/probability-the-science-of-uncertainty-and-data
    then you will probably feel the same frustration as me.
  • Mariana Marcondes

    Mariana Marcondes is taking this course right now.

    I was very excited about this course - its scope and the fact that it did not require any knowledge in statistics. That is not true: you should know some probability and statistics, otherwise you will not be able to keep up with the workload (or the classes, to be honest) and will drop out - like I did.
    Will try again later, when I have gained some statistics knowledge.
  • Anonymous

    Anonymous completed this course.

    I've just completed the course. I'd high expectations and I was hoping to learn a lot more practical skills in R. However, the course tries to cram way too much content into shorter term and covers the topics at a very shallow level. The instructors are...
  • Anonymous

    Anonymous is taking this course right now.

    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!
  • Anonymous

    Anonymous completed this course.

    Good combination of lectures related to probability and statistics, big data and data analysis (where to find data, data visualization, etc) and application of statistical methods in social sciences in real life with researches all over the world. Also, I got an understanding of the obstacles that data scientists meet along the way. I liked this course a lot.
  • Anonymous
    This is a part of a micromaster programm, you can not expect that the professors explainig every terms or basics of statistics. For a good quality the course should be challenging! I find the course content very hard, but if you want pass, you have to do more than the course provides
  • Harunpehlivan completed this course.

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