Data Analysis for Social Scientists
offered by MIT USA

5.0 /5

(1 Review)

Private University
168 acre campus
Estd. 1861

Data Analysis for Social Scientists
at
MIT USA
Overview

Gain a comprehensive overview of Data Analysis for Social Scientists

Duration	11 weeks
Total fee	₹20,703
Mode of learning	Online
Course Level	UG Certificate

Data Analysis for Social Scientists

Table of content

Overview
Highlights
Course Details
Curriculum
Faculty
Student Reviews

Data Analysis for Social Scientists
at
MIT USA
Highlights

Earn a certificate after completion

Data Analysis for Social Scientists
at
MIT USA
Course details

Skills you will learn

R Regression Software Testing Data analysis Data visualization Statistics

What are the course deliverables?

Intuition behind probability and statistical analysis
How to summarize and describe data
A basic understanding of various methods of evaluating social programs
How to present results in a compelling and truthful way
Skills and tools for using R for data analysis

More about this course

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

Data Analysis for Social Scientists
at
MIT USA
Curriculum

Module One: Introduction

Introduction to the software R with exercises. Suggested resources for learning more on the web

Introduction to the power of data and data analysis, overview of what will be covered in the course

Module Two: Fundamentals of Probability, Random Variables, Joint Distributions, and Collecting Data

Basics of probability and introduction to random variables

Discussion of distributions and joint distributions

Introduction to collecting data through surveys, web scraping, and other data collection methods

Module Three: Describing Data, Joint and Conditional Distributions of Random Variable

Principles and practical steps for protection of human subjects in research

Discussion of kernel density estimates

Builds on basics from module 2 to cover joint, marginal, and conditional distributions

Module Four: Joint, Marginal, and Conditional Distributions and Functions of Random Variables

Similarly builds on the basics from module 2 to cover functions of random variables

Discussion of moments of a distribution, expectation, and variance

Basics of regression analysis

Application: Application of some principles of probability to the analysis of auctions (optional)

Module Five: Special Distributions, The Sample Mean, The Central Limit Theorem and Estimation

Discussion of properties of special distribution with several examples

Statistics: Introduction to the sample mean, central limit theorem, and estimation

Data Analysis for Social Scientists
at
MIT USA
Faculty details

Esther Duflo

Esther Duflo is the winner of the 2019 Nobel Prize in Economic Sciences. She is also the Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics at MIT. She was educated at the Ecole Normale Supérieure, in Paris, and at MIT. She has received numerous honors and prizes including a John Bates Clark Medal for the best American economist under 40 in 2010, a MacArthur “Genius” Fellowship in 2009. She was recognized as one of the best eight young economists by The Economist magazine, one of the 100 most influential thinkers by Foreign Policy since the list exists, and one of the “Forty under 40” most influential business leaders under forty by Fortune magazine in 2010.

Sara Fisher Ellison

Sara Fisher Ellison is a Senior Lecturer in the MIT Economics Department. She was an undergraduate at Purdue University and received graduate degrees from both Cambridge University and MIT. She has been a fellow at both the Institute for Advanced Study and the Hoover Institute. Her recent research has investigated a number of questions in industrial organization, with a focus on the pharmaceutical industry and ecommerce. She serves on a number of editorial boards. She has taught at the undergraduate, MBA, and Ph.D. levels, and has received awards for both outstanding teaching and pedagogical innovation.

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Massachusetts Institute of Technology
Students Ratings & Reviews

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1 Rating

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Data Analysis for Social Scientists

Offered by Massachusetts Institute of Technology

High qualified faculty and diverse community of students.

Tips: Before joining this college, students should be aware of its strong emphasis on practical learning and industry collaboration. Additionally, students should be aware of the competitive academic environment, the diverse student body, and the fact that the college is located in a bustling city with ample internship opportunities. The teachers are some of the best I have ever had. They were supportive and experienced and I am excited to continue learning under their guidance. College life is awesome as there is exposure to diversity and it is an informative period marked by learning, growth, and exploration, where individuals actively engage with academics, social interactions, and personal development often considered carefree and joyful. Teachers within the college demonstrate a high level of subject matter expertise, effective teaching practices, strong communication skills, commitment to student learning, and active management in research and scholarly activities.

Reviewed on 28 Jan 2025Read more