Back to Introduction to Probability and Data

4.7

3,152 ratings

•

703 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.

Mar 31, 2018

The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.

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By Akshay J

•Jan 06, 2019

I have a major in mathematics and this is by far one of the best courses I have ever taken on introductory statistics. Instructor explains all the concepts clearly with tons of examples. The labs are very well formed you will never be lost with them. The final project turns out to be fun and informative! Overall, it was a great experience. I recommend it to anyone wanting to get into data science field and/or improve their basic knowledge of statistics and R programming.

By Alan S

•Dec 20, 2018

This is not a course in how to learn R. It is a basic statistics course. The statistical content, including the lectures and book, are very good. The structure of the first four weeks of the course is very good. I recommend students follow the syllabus (videos, etc.) in the order shown--one week I tried the homework without viewing the videos first and it made it more difficult than if I had seen them.

I was very disappointed with the final project, because it required much more R expertise than was explained in the rest of the course. The "example project" was useful, but not sufficient. The specific requirements for the final project (e.g., two of the three subjects must involve and analyze three variables vice two), were not clear or easy to find. An additional week or lectures in the final week covering R tasks such as how to make an HTML document and project expectations would be helpful.

By Anastasia

•Feb 05, 2017

Lecture videos were fantastic! Instructor was amazing. I have a big problem with the final project in Week 5. The entire courses was focused on statistics, yet the final project was focused on R. I wish I had prior R knowledge before starting this course, yet on the front page it says it's a beginner specialization. I would recommend learning R and ggplot before this course. It will make the course a lot less frustrating!

By Susan M

•Feb 20, 2019

It's so so bad. Idk if this is old or what.

Unfortunately my answer here got deleted I took forever writing. So I will just be frank.

God it sucked so bad

Even my bf who is an economist who uses R regularly had to take a half hour of googling just to set up R for the quiz to be usable. Before he came over I was just having a horrible time full of crying and remembering the trauma of computers eating my homework as a kid. Worst class experience of my life. And you call it a beginner course? With that little guidance? Seriously there is so so little about setting up R. I still feel so angry my time and energy was wasted like that. And I was so excited for this course- I'd been gearing up for weeks and was very very committed to finishing.

Steer clear of this course. Btw my friends tell my that R is so old and rarely used anyway so it is dumb to prioritize it over Python or even modeling in Excel

Zero stars

By Alexander S

•Jun 23, 2018

I took this course primarily for the purposes of learning R and reviewing statistics. While the course content was well organised and succinctly presented in the videos, questions on quizzes and labs could at times be phrased in a confusing manner (even for a native English speaker), and the labs did not prepare one for the finesse with R required for the peer-graded final project. For the final project, I would recommend establishing more specific conditions (e.g., the number of visualisations expected, suggestions as to variables to explore in the dataset, and the points to consider in narrative sections), especially for an entry-level course. As a university professor myself, I have found that it is more pedagogically effective to offer precise guidelines for lower-level courses, and reserve open-ended projects for higher-level courses/seminars.

By Korawat T

•Dec 26, 2016

This course will teach you a little bit of statistics and leave you confused with R. The first four weeks are very straightforward. They give R code for everything. However, this is not good because it does not prepare you well for the last project in which you have to do the whole project using R. This is almost like learning how to cook without knowing how to use a knife.

By Guy T

•Mar 14, 2019

I've not studied at this level for a while so the first couple of weeks were intense. The pace didn't let up but the quality of the presentation material was excellent. I didn't feel quite prepared enough for the project and it took much longer than the estimate to complete but well worth it as an exercise.

By Rafael A S R

•Sep 18, 2016

Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher

By Breno B S

•Sep 16, 2018

I would give it 5 stars if it was truly for zero beginners. I myself didn't have much problem understanding the content, but I can imagine that people with no background in statistics would have a very hard time. Some very important concepts are just glossed over. Another problem is that much more attention is given to the mathematics behind the stats, as opposed to how to conduct the tests themselves. I'm finishing the 2nd course two (inferential statistics) and I have the same feeling there. We spend video after video learning the nuts and bolts of the math behind, but at times we are only given the code in R. Sometimes the code is not even given!! The same with ggplots. The real-life applicability of the knowledge here is to make sure you are able to use the software to run the analysis. It's important to understand the logic behind, surely, but no one will, professionally, do the calculations by hand. I left this and will leave the second course (Inferential statistics) with the feeling that I've learnt much more the maths than how to actually use R.

One final thought: at times, in stats, the most difficult thing is to decide which test to implement. There are possibilities, but which one? Why? How to I check for Skeweness in R (the number, not the histogram). What is considered too much skewness? What is too large a bias in bootstrapping? These are just examples of precious, directly applicable information that's left out

By David K

•Mar 08, 2019

I liked:

+ The detailed Learning Objectives.

+ Good examples in the lectures.

+ The quizzes are great for testing and refreshing one's memory.

+ Overall the course seems very well-focused on the most important foundational items and hammers them in.

I'd appreciate improvement in:

+ Providing more clarity on how much R is expected to be learned for the final project, or lowering the level of R skill expected for the final project.

+ Providing a more relaxed time estimate on the final project. I spent 10+ hours on it, in addition to ~10 hours learning more R on DataCamp.

+ Getting feedback on my work from a professional, not just from fellow students. (I would be willing to pay for that.)

By Cheryl L X

•Feb 15, 2019

Be sure you want to learn R before you embark on this course. As a beginner, it was a challenge, but after a few rounds of revisiting the content, it all started to make sense. I would recommend you do the exercises on R Studio. I did mine on Datacamp and had to refamiliarise myself with the RStudio platform for the final assignment, which was slightly painful as more things had to be set up (and time may not be on your side by then). You can use the commands learnt in the course for the final assessment but many classmates seemed to go above and beyond. Online resources are truly indispensable and I'm amazed that I can make decent educated guesses as to what certain lines of code do, in order to improve the chart!

By 舒穎 鄭

•Nov 26, 2018

The lecturer is very nice and teaching well, the basic knowledge is easy to learn. Examples are vivid and easy to understand as well. The biggest problem is the final project. The things we learn can not support the ability to finish the project. One way to improve it is to give more tips or teach more usage of R. Overall I learnt many useful stuffs and I recommend it!

By katie v

•Apr 11, 2019

I wish they went over how to use R for beginners in the beginning of the course. I feel like the final project was stressful and piecemealed together from google searches on the web. I think they should give us a list of all the codes in the beginning of the course that we will use throughout the entire course.

By Raluca B

•Jun 13, 2019

It is an excellent introduction to probability and data, but I think it would be improved by adding one week to the course dedicated to solely data analysis in R, as a precursor to the final project. There was too big of a gap in terms of R practice (for those new to R) between what was explained during the course and the final project. I would have found it super useful to have one more week in which to discuss how to treat missing data, how to clean data in R (even if just a simple cleaning, like getting rid of the NAs), followed by steps/do's and don'ts when analyzing data, different types of graphs in R appropriate for numerical and ordinal data, that sort of thing. That would have made it almost perfect.

By Grant P

•Nov 17, 2018

Very well organized.

By EthanKwon

•Dec 04, 2018

Thanks to this course, I was able to develop my knowledge of statistics and get basic skills in R programming.

By Lien C

•Dec 19, 2018

Dr. Mine Çetinkaya-Rundel is an amazing teacher! I have never learnt and enjoyed so much statistics!

Syllabus is well constructed and organised, plenty of learning materials (with moderate difficulty). This first course is aimed at beginners (no requirement of prior statistics knowledge). Although I use statistics regularly, I don't really understand them well so I find this course extremely helpful!

Thank you so much for putting together this course of the whole specialisation!

By Gopi K D

•Dec 20, 2018

Very Nice

By Samuel O T A

•Dec 10, 2018

It's a great course, I had acquired new abilities like using R language programming for applied statistics, as well as knowledge about probability and statistics in topics like: sampling, measures of center and spread, data visualization, inference, probability distribution and much more.

By YASHKUMAR R T

•Dec 11, 2018

One of the best course in Statistics.

By Lindsay C

•Dec 13, 2018

This course was a good introduction to basic probability, experimental design, and data structure. The labs and final assignments made great use of R Studio and markdown. This course is best suited for someone who is proficient in R already.

By Akash R

•Dec 26, 2018

It was a highly interesting course in which we learnt topics at an easy and understandable pace. The understanding of the project was consolidated further using examples. Lastly, the peer reviewed project had us apply all our understanding on real world data set which is greatly important in the long run.

By 倪仲望

•Jan 01, 2019

Have a simple understanding of data analysis, as well as a simple use of R language tools.

By Sharan S

•Jan 01, 2019

Excellent course for starters.

By Andrea P

•Jan 28, 2019

Very nice and well explained material. Suggested!

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