Before you start
What you will learn
- 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
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
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.
Meet your instructors
Frequently asked questions
How can I earn a certificate for this course?
First, you need to decide which MicroMasters program the certificate will go towards because the certificates for each of the two MicroMasters program are not interchangeable.
For each of the MicroMasters programs you will need to pass this online course with a final grade of 50% or above. However, each program will then require different final assessments in order to earn a course certificate toward the full MicroMasters credential with details and logistics as follows:
The Data, Economics, and Development Policy (DEDP) Program: If you are interested in pursuing the DEDP MicroMasters credential, you will need to create a MicroMasters in DEDP profile, pay your course fee, pass this online course (50% or above) and pass an additional in-person proctored exam. You will then have fulfilled one class towards the DEDP MicroMasters credential, and will receive a certificate.
The Statistics and Data Science (SDS) Program: If you are interested in pursuing the DS MicroMasters credential, you will need to pass this online course (50% or above), enroll and verify for the assessment course 14.310Fx Data Analysis in Social Sciences-Assessment on edX by November 14, and successfully pass the assessment course (60% or above). You will then have fulfilled this component of the SDS MicroMasters credential and will receive a certificate.
When are the exams for either program?
For DEDP: For Fall 2018 the in-person testing session runs from December 6th – December 20th. See future exam dates and more information at the MIT MicroMasters site. Deadline to pay is October 12th.
For SDS: the exam 14.310Fx runs at the end of 14.310x. For Fall 2018, the exam is open November 29 to December 11. Deadline to verify for the exam is November 14th.
What if I am interested in both MicroMasters Program?
To earn credit towards BOTH MicroMasters Program, you need to pass this course and pass BOTH final exams.
Who can take this course?
Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. EdX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.