Introduction to Probability and Data

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Course Details

Cost

FREE,
Add a Verified Certificate for $79

Upcoming Schedule

  • Upcoming
  • Oct. 23, 2017

Course Provider

Coursera online courses
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with yo...
Coursera's online classes are designed to help students achieve mastery over course material. Some of the best professors in the world - like neurobiology professor and author Peggy Mason from the University of Chicago, and computer science professor and Folding@Home director Vijay Pande - will supplement your knowledge through video lectures. They will also provide challenging assessments, interactive exercises during each lesson, and the opportunity to use a mobile app to keep up with your coursework. Coursera also partners with the US State Department to create “learning hubs” around the world. Students can get internet access, take courses, and participate in weekly in-person study groups to make learning even more collaborative. Begin your journey into the mysteries of the human brain by taking courses in neuroscience. Learn how to navigate the data infrastructures that multinational corporations use when you discover the world of data analysis. Follow one of Coursera’s “Skill Tracks”. Or try any one of its more than 560 available courses to help you achieve your academic and professional goals.

Provider Subject Specialization
Humanities
Sciences & Technology
4432 reviews

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.
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Reviews 7/10 stars
1 Review for Introduction to Probability and Data

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8/10 starsCompleted
  • 1 review
  • 1 completed
1 year, 5 months ago
This course offers you a really basic but, well taught, Introduction to Probability and Data and is probably one of the best materials available on the internet for someone that wants to start in the subject In the videos, the Instructor, Dr. Mine Çetinkaya-Rundel, does an amazing job with her simple yet creative explanations supported by quite good images and animations. The weekly quizzes are well written and with a interesting level of difficulty. It's common to fail here a few times but the retry it helps you to understand better what you saw in the video content. The assignments of this course probably are its main weak spot, specially regarding the progression on its level of difficulty: The first 3 weeks assignments are more like tutorials, with small datasets, of how to use R to play with some basic stats. They make a really good job on that but do not offer any challenges for the student, the student doesn't have... This course offers you a really basic but, well taught, Introduction to Probability and Data and is probably one of the best materials available on the internet for someone that wants to start in the subject In the videos, the Instructor, Dr. Mine Çetinkaya-Rundel, does an amazing job with her simple yet creative explanations supported by quite good images and animations. The weekly quizzes are well written and with a interesting level of difficulty. It's common to fail here a few times but the retry it helps you to understand better what you saw in the video content. The assignments of this course probably are its main weak spot, specially regarding the progression on its level of difficulty: The first 3 weeks assignments are more like tutorials, with small datasets, of how to use R to play with some basic stats. They make a really good job on that but do not offer any challenges for the student, the student doesn't have to actually do much by himself, just fill in some data in some already given commands to place data in a quiz. The last assignment of the course is a totally different deal and it probably got by surprise almost everyone that did this course. It's a peer review, very open assignment that requests you to use R to complete a very open task using a quite large and complex dataset. I'm not sure if it was just because of how the difficulty of the course escalated here but, all the assignments that I've corrected were far of being complete, I never made a peer review in Coursera like this, where my colleagues work were so incomplete. I've managed to complete my assignment but I'm a experienced developer and I knew R fairly well before this class.
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