Online Course Highlight #1 – Data Analysis

One extremely popular MOOC course, offered by Coursera, is Data Analysis, taught by Jeff Leek, an assistant professor of biostatistics at Johns Hopkins University. The course has won a Teaching Excellence Award, voted on by the students at Johns Hopkins, every year Dr. Leek has taught the course.

Overview

As Dr. Leek points out in his intro video, his Data Analysis course is “unlike any statistics class you’ve ever taken in the past. Instead of focusing on equations, we will focus on how to collect, clean, interpret, and communicate data analysis.”

Data analysis at Johns Hopkins is defined as “the process of finding the right data to answer your question, understanding the processes underlying the data, discovering the important patterns in the data, and then communicating your results to have the biggest possible impact.”

This course provides a strong foundation in applied statistics and data analysis. The course begins with an overview of how to organize, perform, and write up data analyses. It also offers the opportunity to critique and assist classmates with their data analyses.

This master’s level course focuses on how to:

  • Perform exploratory data analysis.
  • Fit statistical models and make predictions.
  • Use statistical methods like linear regression, principal components analysis, cross-validation, and p-values.
  • Apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis.
  • Synthesize your results and make a coherent argument.

Details

Familiarity with R statistical analysis programming language is required for students taking this course. Proficiency in English language is strongly recommended. At Johns Hopkins, this course is taken by first year biostatistics graduate students.

The Data Analysis course consists of a series of 10-minute video lecture segments and two major data analysis projects that are peer-graded with instructor oversight. Course grades are determined by the project grades, peer reviews, and message board participation throughout the course. No textbooks are required, but the course will include links to free online resources. The expected workload is three to five
hours per week.

For more information, see Data Analysis.