Back to Introduction to Probability and Data

4.7

stars

3,785 ratings

•

873 reviews

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....

Jan 24, 2018

This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.

Sep 04, 2019

Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.

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By James P

•May 03, 2020

I came to this course already armed with some stats skills with the hopes of brushing up on my stats whilst getting to learn to use R. The course is a nice, easy to follow introduction to data and probability. I had no R experience at the start of the course and the weekly assignments helped me build my R skills. However, the R skills taught were not sufficient for me to tackle the final data analysis project. I had to spend a lot of additional time teaching myself R. Before or shortly after starting this course I would suggest others take some time to self-learn (through youtube etc) about data frames, r markdown, dplyr, producing nice looking tables of summary statistics to be shown in r markdown (for the final project), and ggplot2 for nice looking data visuals.

By James B

•Feb 27, 2020

This was a very comprehensive course. I am currently undertaking a PhD in Medical Studies, and this course gave me a better understanding of the basic theory underlying Probability. This was a useful shoehorn into learning and (importantly) understanding Bayesian statistics.

I would have given this course 5 stars, although the end project was disproportionately difficult compared to the rest of the content. The description lead me to believe that no background in R was necessary, however this did not appear to be the case. I highly suggest having a firm grasp on R and R programming language before looking to complete this course.

By Shawn T R

•Jul 26, 2017

Pretty good course and I definitely came away with a good understanding of the fundamentals of basic statistics. The video lectures move pretty fast and kind of assume that you're getting everything which is not always the case. The student forums help a bit, but responses either from the instructors or other students is unpredictable and inconsistent, so you really can't depend on it when you need clarification or can't understand a concept. I had to go outside of the course several times to really grasp all of the concepts. Fortunately there are plenty of resources on the net that can help though.

By Mikkel R

•Feb 13, 2019

Offers a good introduction to probability as promised. Great material, you can really tell that the teachers have made an effort making the content presentable.

The only thing I did miss however, was a lecture introducing coding in R especially since that is what makes up most of the time in doing the peer-reviewed assignment. Nothing fancy, just a single lecture introducing the logic behind the dplyr and ggplot2 packages would have been ever so helpful and could have been covered in less than 30 minutes.

Thanks for a good course!

By Mailei V

•Apr 07, 2017

The stats info and exercises were helpful for learning stats/probability. The labs were somewhat helpful, but did NOT prepare you for the project at the end of the course. Prerequisites say not necessary to know R coding, but I REALLY struggled getting through the assignment in a timely manner and eventually wasn't happy with what I had to submit to make the deadline. I highly recommend taking a few courses in R in DataCamp or watching videos about R Studio (dplyr and tidyr) before attempting the labs and project.

By Jack M

•Nov 02, 2017

This course is a solid introduction to conducting statistical analysis using the software R. The video and reading materials are clear and informative, and ideal for those with little to no familiarity with the theory behind probability and statistics. Outside of the weekly assignments, there is relatively less exposure and direct instruction for operating R, but I was able to make use of guides and forums in the coursera page to teach myself about R commands and codes as I navigated the provided datasets.

By Gregory G

•May 05, 2020

Excellent lectures. Final project requires at least an advanced-beginner knowledge of R, especially of dplyr and ggplot2 packages. Not sure that's clear from the description. I came in having taken a short Intro to R course -- I'd have been overwhelmed by the data analysis project had I not. I relied on numerous YouTube R tutorials to get it done (thank goodness for the generosity of the R community!) and ended up really pushing myself. The instructor is a star. Many thanks if she's reading this!

By Joshua W

•May 04, 2020

Overall, I recommend this course. It has the right balance of instructed work and explorative work (when it comes to the lab work). I would have wanted the probability calculations to have been more closely tied to the R programming in the lessons. That being said, I have no prior experience with R and I come from a humanist academic background, and I found the course material instructive enough to help me learn how to code with R and do basic probability mathematics.

By Shawn G

•Oct 09, 2016

Great videos and testing structure. Great feedback to all of my questions in the forums too. 7 hours a week seems about right for me, with the recommended reading and watching the videos. However the course got continually harder and harder and by the time I was at the final exam I was really worried. In the end it got a bit difficult and stressful but I do feel it was valuable, and like I said, getting the feedback, even on weekends in the forums, helped a lot.

By Chris S

•Jun 27, 2016

The content of this course is not terribly difficult, I thought, but it's a very good introduction into most aspects of Data Science. You learn to familiarize yourself with R quite well and get a lot of independence to create a final project based on a huge data set. One thing I would've liked is a sample completed project, start to finish, to see what was expected- the things that got produced (which you peer review) varied hugely in quality.

By Tom B

•Jan 17, 2018

Great introduction/review of basic stats concepts. I think the course designers assume a knowledge of/familiarity with R beyond what they claim in the Course Description. The weekly labs were somewhat helpful, but could benefit from providing the students with a bit more instruction on the functions of R. The learning curve during the Week 5 project was a little steep for a complete novice like me, but overall I found this course worthwhile.

By Alycia K

•Mar 11, 2018

I had to drop this course because I had too many other things on my plate, but hope to enroll again another time. I thought the course was well structured, with very good examples, or explanations, of experiment design. Thanks to the instructors for providing free access to external materials. I thoroughly enjoyed what I was learning. Currently, the only reason I didn't give 5 stars is because of the trouble I kept having with R Studio.

By Alison W

•Aug 25, 2016

The course videos are good, but the R programming is not well explained. I've enrolled in other Coursera courses that use R, Python or Octave, and they all provide clear demo videos for beginners to get up to standard with the code. This course doesn't do that so it's not a good intro to R, which is a shame because working in statistics these days is all about using R and similar tools so there should be a stronger emphasis on that.

By Ihor F

•Dec 28, 2016

Presentation of the content, course slides and labs are best from what I've seen on Coursera. The only downside was that to my feeling the final project and the course content are somehow disconnected. The course itself deals with introduction to probability, while final project is EDA. I don't think there was enough materials on EDA in the course, so the final project took more effort and was confusing at first.

By Deleted A

•Jun 26, 2017

Really good foundation course for those who aren't familiar to statistics and gives out great resources to learn or refresh some material. For the assignments, I like how they give an option of either doing it from the DataCamp website or RStudio. I wish there was somewhat a better way of understanding the R libraries in the assignment, but I just don't know what. Overall, I love it.

By Kanchan K

•Jun 30, 2017

This course enables one to start right from basics and develop strong fundamentals in exploratory data analysis. One thing that could be improved is providing for more "R programming" commands or reference materials which can be used by the learner to gather more variations of the commands used in that section and thereby improve code and formatting of plots/graphs.

By Bryan L

•Jan 07, 2020

This is a useful statistical course for anyone who seeks to gain a basic understanding of probability. The R coding assignments are especially useful but one could benefit more if they already knew how certain functions works in R. The dplyr package is especially emphasized and I suggests going to Youtube to know the main functions that are used for data wrangling.

By Christopher T

•Aug 20, 2016

Solid and efficient introduction to content, does not do enough teaching in R for the final project - which is *fine* because finding things out for yourself is the best way to learn, but R help online is often so dense that it's not that helpful to a beginner. More responsive mentors - especially nearer the end of the course - would be really helpful.

By Robert W

•Oct 05, 2019

This course is a very good introduction to statistics. The lectures are well paced and engaging. The reading materials augment the lectures nicely by providing more details and workable exercises. There is a new edition of the reference book and the lecture material has not been updated to match the new edition, but the previous editions are available.

By ahmed i a e r

•Sep 18, 2017

Very good course on Statistics with application in the R programming language ,a great intro for anyone who want to understand statistical concepts used in research , data science.

Although the course require no background in R , I would advise to take an introduction in R programming before hand in order to be able to finish the final project easily .

By Ashley T

•Sep 17, 2019

In my opinion, the final assignment could have been more effectively and meaningfully answered if students have had prior knowledge in data wrangling/ cleaning in R. I don't think this information was made known throughout the course. Otherwise, this course provided a good overview on the fundamentals of basic statistical visualisation and analysis!

By Khaleel O

•Dec 19, 2017

The course content, resources and teaching were very good but the course is much more demanding than advertised. The course was advertised for Beginners but in truth it is much more for students of Intermediate Skill and Experience. As a working adult I would have preferred more realistic, expected completion timelines for the tasks.

By James F

•Nov 30, 2017

The course was good but I found the final project to be quite disjoint from the course work. The course included a lot of normal approximation to binomial distribution and calculating probabilities based on binomial draws. The final project seemed very much directed towards looking for correlations in data and mastering ggplot2!

By Ying T

•Jul 11, 2017

The content is pretty good especially with the concepts well-explained by Dr. Mine, in my opinion, which is much better than John Hopkins' series. However, the R programming assignment is not designed for beginners and it's quite often to get stuck at the final project. For this reason, I gave 4 star for this course.

By Sarah G

•Feb 02, 2017

Good professor, good exercises to review what you've learned.

My only complaint - if considered that - is that R was difficult for me to learn and work with, especially when I'll be using SPSS for my own statistical needs moving forward. I'd like for there to be an option within the course to use SPSS instead of R.

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