Introduction to Probability and Data

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.You will produce a port

( 495 Reviews )

Created by: Mine Aetinkaya-Rundel

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Overall Score : 98 / 100

Course Description

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

Time: 22h

Instructor Details

Mine Aetinkaya-Rundel

4.9 Rating
495 Reviews

Mine Aetinkaya-Rundel is an Assistant Professor of the Practice at the Department of Statistical Science at Duke University. She received her Ph.D. in Statistics from the University of California, Los Angeles, and a B.S. in Actuarial Science from New York University's Stern School of Business. Dr. Aetinkaya-Rundel is primarily interested in innovative approaches to statistics pedagogy. Some of her recent work focuses on developing student-centered learning tools for introductory statistics courses, teaching computation at the introductory statistics level with an emphasis on reproducibility, and exploring the gender gap in self-efficacy in STEM fields. Her research interests also include spatial modeling of survey, public health, and environmental data. She is a co-author of OpenIntro Statistics and a contributing member of the OpenIntro project, whose mission is to make educational products that are open-licensed, transparent, and help lower barriers to education. She is also a co-editor of the Citizen Statistician blog and a contributor to the Taking a Chance in the Classroom column in Chance Magazine.

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By Reza B on 14-Aug-16

This course is definitly suitable for learners who don't have any related background. Dr. Mine Cetinkaya-Rundel has an amiable speaking style and always highlighted the key points in teaching videos, which helped me understand other contents in the textbook. Besides, the time and assignment arrangement of this course are also very reasonable. The only thing I could complain about is a system bug of the Amazon AWS and the grading system. My final project file went blank after the system told me the file uploading is sucessfully completed on August 3, and I got three 0 point from three peers since they only saw a blank file, of which I had no idea, and all I could see is "Grading in progress" on the system. Until the final grading day is over, which is August 12, the system finally reminded me of this horrible thing. I came to mentors, disscussion forum as well as the help center, reuploaded my file, desperately tried to find peers who can still spare some time to review my file, and it is finally fixed today. I got my certificate in the end, but the grading process is really frustrating. Hope this bug won't happen to anyone ever ag

By Jesse M B on 23-Mar-18

excellent course! Videos were very instructive, book and problems reinforced the course material well. All in all great. Had a little problem getting the knit function to work initially, and it appears as some others did since I saw one project submitted that wasn't knit into html.

By NIKHIL K on 6-Apr-18

This course is really helpful to have a better understanding of fundamentals of probability and data statistics. The course mainly focuses on basic concepts of probability and how to apply them. The assignment provided was very helpful and challenging. The peer graded project allows me to evaluate my fellow course mates which really boost my confidence as make me feel like an invigilator and provide the basis for my academic career.

By Jose ÃƒÂ P L on 19-Nov-17

The course gave a great introduction to statistics and R. I think it would have been more helpful to have a pre-requisite reading for RStudio before jumping into the projects but the course was overall very good.

By Shadi A on 29-Jul-17

Excellent course !

By Doronina L V on 15-Apr-18

A beatuifully designed course for beginners. This course explains in great detail the basics and various prinicples of statistical analysis required for analyzing data.

By Jose P on 17-May-17

Instructor is great and curriculum was well organized and easy to understand.

By Jasper R on 22-Aug-17

Simply explained.

By Christian E on 25-May-17

Probably the best introduction to probability and exploratory data analysis; easy to follow and lots of learning behind this course.

By Jose ÃƒÂ P L on 3-Jul-17

Great introductory course. Content is conveyed very nicely, so it's easy to understand. Labs are really fun since it's based on real life data!

By Ross O on 9-Jun-16

Very informational, really good speed and content. Nice examples to illustrate the learnings.

By Victor V d A on 29-Jan-17

This course is a great introduction to learning about statistical thinking in R. The emphasis is of course on probability and data (especially distributions and exploratory analysis), but there is also a very nice integration of R code and introductory coding to complement the main material.