About the content
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 a MicroMasters 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 visit https://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 at 14.310Fx 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 visit https://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.
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.
- 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
No prior preparation in probability and statistics is required, but familiarity with algebra and calculus is assumed.
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: Intro to Machine Learning and Data Visualization
Week Eleven: Endogeneity, Instrumental Variables, and Experimental Design
Optional: Writing an Empirical Paper
Abdul Latif Jameel Professor of Poverty Alleviation and Development Economics in the Department of Economics
Sara Fisher Ellison
Senior Lecturer, Economics
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MIT is independent, coeducational, and privately endowed. Its five schools and one college encompass numerous academic departments, divisions and degree-granting programs, as well as interdisciplinary centers, laboratories and programs whose work cuts across traditional departmental boundaries.
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