This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy. To enroll in the full program, go to MIT’s MicroMasters site. If you want to enroll in this course only, click “Enroll Now.” Learn more about this program and how it integrates with MIT’s new blended master’s degree.
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.
MODULE 0: THE BASICS OF R
Introduction to the software R with suggested resources.
MODULE 1: INTRODUCTION
Introduction to the power of data and data analysis, and course overview
MODULE 2: FUNDAMENTALS OF PROBABILITY, RANDOM VARIABLES, DISTRIBUTIONS AND JOINT DISTRIBUTIONS
Basics of probability and introduction to random variables
Distributions and joint distributions
MODULE 3: GATHERING AND COLLECTING DATA, ETHICS, AND KERNEL DENSITY ESTIMATES
Collecting data through surveys, web scraping, and other data collection methods
Principles and practical steps for protection of human subjects in research
Discussion of kernel density estimates
MODULE 4: JOINT, MARGINAL, AND CONDITIONAL DISTRIBUTIONS & FUNCTIONS OF RANDOM VARIABLES
Further exploration on joint, marginal, and conditional distributions
Deep dive intofunctions of random variables
MODULE 5: MOMENTS OF A RANDOM VARIABLE, APPLICATIONS TO AUCTIONS, & INTRO TO REGRESSION
Moments of a distribution, expectation, and variance
Applying principles of probability to the analysis of auctions
Basics of regression analysis
MODULE 6: SPECIAL DISTRIBUTIONS, THE SAMPLE MEAN, CENTRAL LIMIT THEOREM, AND ESTIMATION
The properties of special distributions with several examples
Statistics: Introduction to the sample mean, central limit theorem, and estimation
MODULE 7: ASSESSING AND DERIVING ESTIMATORS- CONFIDENCE INTERVALS AND HYPOTHESIS TESTING
Deriving and assessing estimators
Constructing and interpreting confidence intervals
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you. They also have student discussion forums, homework/assignments, and online quizzes or exams.
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.
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.