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.
Data Analysis
Johns Hopkins University via Coursera
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Overview
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.
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
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Reviews
4.2 rating, based on 20 Class Central reviews
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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…
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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…
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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. -
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.
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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.
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Great class, don't miss! It get me started with R. Very practical, many exercises. Lectures are available on youtube. https://www.youtube.com/watch?v=OfgjgEXxskg&list=PLXBDYmaCbeL8efhOZS4g9W6Z3m9_hFSnT
The instructor is also a co-editor of http://simplystatistics.org/, fine source for data nerds. -
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.
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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.
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With few tweaks will be an excellent course. Challenging - yes! Boring - no!
Hands on real life data and questions. Thank you, Jeff, and team! -
Very impressed so far with the learning experience offered by this course.
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Very good class. Excellent assignments of exploratory nature.
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