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 exercises. Suggested resources for learning more on the web.
MODULE 1: INTRODUCTION
Introduction to the power of data and data analysis, overview of what will be covered in the course.
MODULE 2: FUNDAMENTALS OF PROBABILITY, RANDOM VARIABLES, DISTRIBUTIONS AND JOINT DISTRIBUTIONS
Basics of probability and introduction to random variables.
Discussion of distributions and joint distributions.
MODULE 3: GATHERING AND COLLECTING DATA, ETHICS, AND KERNEL DENSITY ESTIMATES
Introduction to 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
Builds on the basics from module 2 to cover joint, marginal, and conditional distributions.
Similarly builds on the basics from module 2 to cover functions of random variables.
MODULE 5: MOMENTS OF A RANDOM VARIABLE, APPLICATIONS TO AUCTIONS, & INTRO TO REGRESSION
Discussion of moments of a distribution, expectation, and variance.
Application of some principles of probability to the analysis of auctions.
Basics of regression analysis.
MODULE 6: SPECIAL DISTRIBUTIONS, THE SAMPLE MEAN, CENTRAL LIMIT THEOREM, AND ESTIMATION
Discussion of 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.
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.
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 ge
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.