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Johns Hopkins University

Data Analysis

Johns Hopkins University via Coursera

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You have probably heard that this is the era of “Big Data”. Stories about companies or scientists using data to recommend movies, discover who is pregnant based on credit card receipts, or confirm the existence of the Higgs Boson regularly appear in Forbes, the Economist, the Wall Street Journal, and The New York Times. But how does one turn data into this type of insight? The answer is data analysis and applied statistics. Data analysis is 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. There is a critical shortage of people with these skills in the workforce, which is why Hal Varian (Chief Economist at Google) says that being a statistician will be the sexy job for the next 10 years.

This course is an applied statistics course focusing on data analysis. The course will begin with an overview of how to organize, perform, and write-up data analyses. Then we will cover some of the most popular and widely used statistical methods like linear regression, principal components analysis, cross-validation, and p-values. Instead of focusing on mathematical details, the lectures will be designed to help you apply these techniques to real data using the R statistical programming language, interpret the results, and diagnose potential problems in your analysis. You will also have the opportunity to critique and assist your fellow classmates with their data analyses.

Taught by

Jeff Leek


4.2 rating, based on 20 Class Central reviews

Start your review of Data Analysis

  • Anonymous
    View this course as a master class in statistics. Jeff Leek is a master statistician; he shows how experts do academic statistical research. To benefit from this course you should: • Know about statistics beyond the basics • Be familiar with the…
  • Olena Bosenok
    Here is what I liked about this class: 1. It is well designed course -- informative lectures with many examples and challenging but fair quizzes. 2. Clear instructions for peer-graded assignments (there were 2) with given example. 3. Incredibly…
  • Anonymous
    Early lectures were exceedingly easy, but the difficulty jumped suddenly in the third week. The professor does not adequately explain underlying concepts. On one hand, we can't fault him -- the topic of the course is performing an analysis, not on the statistical methods underlying it -- but on the other hand, teaching us to perform statistical tests without a good understanding of what we are doing will lead to poor analyses.

    I really wanted to like this course, but found the content too poorly explained to continue.
  • Anonymous
    One of the worst courses I ever took. Video are basically the teacher reading some printed phrases or R commands: no value added compared to personal reading of R manuals and tutorials. The peer reviewed projects where exposed to very subjective evaluation.. unavoidably, I presume, considering that the class do not cover adequately all the points required to complete the task. Such wide topic probably requires to be covered in more than one course and with a more involving teaching style.
  • Anonymous
    I would recommend to take Machine Learning with Andrew Ng, otherwise it could be overwhelming. I enjoyed most of the course, except for the first assignment that required knowledge of linear regression and ANOVA , but it was due the same week these topics were covered. Week 7 material needs an extra week, otherwise it's too much. Special thanks to my classmates whose forums' posts fulfilled the gap in lectures and helped with homework.
  • Anonymous
    Great class, don't miss! It get me started with R. Very practical, many exercises. Lectures are available on youtube.

    The instructor is also a co-editor of, fine source for data nerds.
  • Anonymous
    So far, the course is good. The instructor's style is a little dry (somewhat military flavor) but the description is structured and consistent. However, keep in mind that the course pace is very slow, so if you are already somewhat familiar with the subject, you will fall asleep.

  • Anonymous
    Excellent course. Superb lectures, great assignments, fair quizzes, understanding and engaged teacher. I've been discovering the new universe of powerful statistical models and R language and enjoying it all the way through.
  • Anonymous
    With few tweaks will be an excellent course. Challenging - yes! Boring - no!

    Hands on real life data and questions. Thank you, Jeff, and team!
  • Anonymous
    Very impressed so far with the learning experience offered by this course.
  • Troddel
    Very good class. Excellent assignments of exploratory nature.
  • Anonymous
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