subject
Intro

Coursera: Data Analysis and Statistical Inference

 with  Mine Çetinkaya-Rundel
Class Central Course Rank
#2 in Subjects > Data Science
#1 in Subjects > Data Science > Data Analysis

The Coursera course, Data Analysis and Statistical Inference has been revised and is now offered as part of Coursera Specialization “Statistics with R”.
 
This Specialization consists of 4 courses and a capstone project. The courses can be taken separately:
  • Introduction to Probability and Data (began in April 2016)
  • Inferential Statistics (begins in May 2016)
  • Linear Regression and Modeling (begins in June 2016)
  • Bayesian Statistics (begins in July 2016) A completely new course, with additional faculty!
  • Statistics Capstone Project (August 2016) (for learners who have passed the 4 previous courses, and earned certificate)
You may enroll in a single course, or all of them, but each requires the knowledge and techniques from the previous courses. The assignments in these courses have suggested but not required deadlines, so you can work at your own schedule. Please check the Specialization page for other answers to your questions, and peek at the first course.
 
We hope to see you in our new courses.
 
The Statistics with R team.

___________________________________________________
The goals of this course are as follows:
  1. Recognize the importance of data collection, identify limitations in data collection methods, and determine how they affect the scope of inference.
  2. Use statistical software (R) to summarize data numerically and visually, and to perform data analysis.
  3. Have a conceptual understanding of the unified nature of statistical inference.
  4. Apply estimation and testing methods (confidence intervals and hypothesis tests) to analyze single variables and the relationship between two variables in order to understand natural phenomena and make data-based decisions.
  5. Model and investigate relationships between two or more variables within a regression framework.
  6. Interpret results correctly, effectively, and in context without relying on statistical jargon.
  7. Critique data-based claims and evaluate data-based decisions.
  8. Complete a research project that employs simple statistical inference and modeling techniques.

Syllabus

Week 1: Unit 1 - Introduction to data
  • Part 1 – Designing studies
  • Part 2 – Exploratory data analysis
  • Part 3 – Introduction to inference via simulation
Week 2: Unit 2 - Probability and distributions
  • Part 1 – Defining probability
  • Part 2 – Conditional probability
  • Part 3 – Normal distribution
  • Part 4 – Binomial distribution
Week 3: Unit 3 - Foundations for inference
  • Part 1 – Variability in estimates and the Central Limit Theorem
  • Part 2 – Confidence intervals
  • Part 3 – Hypothesis tests
Week 4: Finish up Unit 3 + Midterm
  • Part 4 – Inference for other estimators
  • Part 5 - Decision errors, significance, and confidence
Week 5: Unit 4 - Inference for numerical variables
  • Part 1 – t-inference
  • Part 2 – Power
  • Part 3 – Comparing three or more means (ANOVA)
  • Part 4 – Simulation based inference for means
Week 6: Unit 5 - Inference for categorical variables
  • Part 1 – Single proportion
  • Part 2 – Comparing two proportions
  • Part 3 – Inference for proportions via simulation
  • Part 4 – Comparing three or more proportions (Chi-square)
Week 7: Unit 6 - Introduction to linear regression
  • Part 1 – Relationship between two numerical variables
  • Part 2 – Linear regression with a single predictor
  • Part 3 – Outliers in linear regression
  • Part 4 – Inference for linear regression
Week 8: Unit 7 - Multiple linear regression
  • Part 1 – Regression with multiple predictors
  • Part 2 – Inference for multiple linear regression
  • Part 3 – Model selection
  • Part 4 – Model diagnostics
Week 9: Review / catch-up week
  • Bayesian vs. frequentist inference
Week 10: Final exam
40 Student
reviews
Cost Free Online Course (Audit)
Pace Finished
Subject Data Analysis
Institution Duke University
Provider Coursera
Language English
Hours 8-10 hours a week
Calendar
Sign up for free? Learn how
+ Add to My Courses
Dr. Mine Cetinkaya-Rundel
Class Central presents
An interview with
Dr. Mine Cetinkaya-Rundel
Statistics Professor Mine Cetinkaya-Rundel discusses how teaching a Statistics MOOC changed her on campus class at Duke University. Read
Learn Data Analysis udacity.com

Learn to become a Data Analyst. Job offer guaranteed or get a full refund.

Advertisement
Become a Data Scientist datacamp.com

Learn Python & R at your own pace. Start now for free!

Advertisement
FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

40 reviews for Coursera's Data Analysis and Statistical Inference

Write a review
22 out of 22 people found the following review useful
3 years ago
profile picture
Life is Study completed this course.
Duke’s Data Analysis and Statistical Inference on Coursera is an introduction to statistics with an optional computational component using the R programming language. The course runs about 8 weeks and covers a considerable amount of ground in that time. It starts with the basics of data and data collection methods but Read More
Duke’s Data Analysis and Statistical Inference on Coursera is an introduction to statistics with an optional computational component using the R programming language. The course runs about 8 weeks and covers a considerable amount of ground in that time. It starts with the basics of data and data collection methods but quickly moves on to cover probability, the normal distribution, the binomial distribution, hypothesis testing, confidence intervals, Z and T statistics, ANOVA and Chi squared tests and linear regression. The course is a bit of a whirlwind tour that packs a lot into each lecture. The PDF slides that go along with the videos are a great resource to review the information dumped in each lecture. Many students complained that the course requires more time than the original estimated amount of around 6-8 hours per week. The course was later updated with an estimate of 8-10 hours per week, which is on the conservative side. If you come in with some prior knowledge of stats and R you can get through in 3-5 hours per week.

The professor is engaging and does a good job going through the material while providing adequate face time. The slides are very informative and the video quality is excellent. There are periodic in-lecture quizzes that help test your understanding of the material as you go along. I felt that the frequency of in-lecture quizzes was just about right in this course.
Was this review helpful to you? YES | NO
9 out of 9 people found the following review useful
3 years ago
profile picture
Bart completed this course.
Great, great course. A well balanced mixture of theory, examples, labs and to learn software and projects to test your skills in the 'real' world. The teacher explains the concepts very clearly. The course layout and order of topics is excellent. The difficulty is does not change overall. The free and open textbook is Read More
Great, great course. A well balanced mixture of theory, examples, labs and to learn software and projects to test your skills in the 'real' world.

The teacher explains the concepts very clearly. The course layout and order of topics is excellent. The difficulty is does not change overall. The free and open textbook is the best I've come across. It is packed with footnotes to datasets, and has more than enough examples and exercises to get you through the midterm and the exam.

The teacher is very active on the forums and even organised a Google hangout session you can join.

The focus of this course is definitely not on mathematical proofs, probability theory or working through problems analytically, but geared towards practical approaches using given formula's or R to get results on every day problems.

Integrated into the course is the datacamp environment that helps you to learn the software R by playing with data. The nature of these task mirror the theory discussed that week. There are some glitches in this new environment (during the first run of this course), and the tasks are not really challenging.

The quizzes are quite good. Many of the questions test your insight rather than ask you to do tedious algebra.

The peer reviewed research projects are time consuming, great fun and an excellent way to get your hands wet with real data, R and doing some real data analysis.

One of the best courses I've taken.
Was this review helpful to you? YES | NO
7 out of 7 people found the following review useful
3 years ago
Prose Simian completed this course, spending 4 hours a week on it and found the course difficulty to be easy.
I've had plenty (cough) of time to forget the basic statistical inference I did at high school. This was a great refresher (if a little repetitive - hypothesis tests vary mainly in relatively minor-seeming details). With a little prior exposure, it's not too time consuming. But at the same time it never degenerated - i Read More
I've had plenty (cough) of time to forget the basic statistical inference I did at high school. This was a great refresher (if a little repetitive - hypothesis tests vary mainly in relatively minor-seeming details). With a little prior exposure, it's not too time consuming. But at the same time it never degenerated - in the way of much stat inf pedagogy - into a pure exercise in plugging numbers into formulae; some questions were demanding enough to require significant headscratching and envelope scribbling

And judging by the quality of the materials (video, detailed learning objectives plus references to an open textbook - more or less optional given the copious lecture material and summary "learning objectives") & reactions in the forums, it's a great introduction for people without previous exposure to the topic, with the added bonus of a gentle introduction to R (via the excellent datacamp website should you prefer).

Thanks to Dr Mine, there is now no excuse for statistical illiteracy.

No.

Ex.

Cuse.

(Update: DASI has two tracks - with and without a project and R labs. The project is supposed to be necessary for a distinction, but I just discovered that with a high enough grade on the exams/quizzes, it isn't :)
Was this review helpful to you? YES | NO
3 out of 3 people found the following review useful
3 years ago
profile picture
Anonymous completed this course.
Extremely time consuming. I work in a market research firm, have some familiarity, and perhaps because of that some misconceptions. The course is very very well organised by the instructor, much of the material follows each other, so no time wasted like some other online courses. Believe me you'll need every extra minu Read More
Extremely time consuming. I work in a market research firm, have some familiarity, and perhaps because of that some misconceptions. The course is very very well organised by the instructor, much of the material follows each other, so no time wasted like some other online courses. Believe me you'll need every extra minute. Her videos are upbeat as well, never a dull moment. One thing make sure your math skills are up to par. My math skills need brushing up, but I'm used to data and programming so that took the edge off. I would say make sure you have the rest of your life organised while doing the course. Definitely worth every aching minute though.
Was this review helpful to you? YES | NO
3 out of 3 people found the following review useful
3 years ago
Maxime Zabiégo completed this course, spending 10 hours a week on it and found the course difficulty to be medium.
One of the greatest courses I've taken so far. A great teacher, very much involved in exchanges with her students. A large variety of teaching approaches and tools. Lots of practice, through short tests, R-programming labs, and an in-depth project. A very lively forum, with lots of help to cope with difficulties. The c Read More
One of the greatest courses I've taken so far. A great teacher, very much involved in exchanges with her students. A large variety of teaching approaches and tools. Lots of practice, through short tests, R-programming labs, and an in-depth project. A very lively forum, with lots of help to cope with difficulties. The course is not too difficult, but the variety of the proposed material requires that students get involved quite substantially. A very nice book available for free, with plenty of practice exercises.
Was this review helpful to you? YES | NO
2 out of 4 people found the following review useful
2 years ago
profile picture
Anonymous is taking this course right now.
i have completed 5 weeks so far. and now i am dropping out of this course. its painfully slow and easy. I was looking to gain more knowledge on stats before i stats my ms program in analytics. I dont want to take away the credits from duke uni or Dr Mine Rundel. She has done great work . I went through her book os and Read More
i have completed 5 weeks so far. and now i am dropping out of this course. its painfully slow and easy. I was looking to gain more knowledge on stats before i stats my ms program in analytics. I dont want to take away the credits from duke uni or Dr Mine Rundel. She has done great work . I went through her book os and read pdfs. this way i saved time. but now i am dropping out. ( I am from engineering background )
Was this review helpful to you? YES | NO
4 out of 4 people found the following review useful
3 years ago
profile picture
Anonymous completed this course.
Time consuming, but this course is well worth the money on signature track! This will give you a very solid understanding of statistic, which is the basic of so many other fields: experimental research, lean, and machine learning just to name a field.

Unmissable if you want to broaden your knowledge on how to do things with scientific rigor.
Was this review helpful to you? YES | NO
2 out of 2 people found the following review useful
3 years ago
profile picture
Anonymous completed this course.
This course has a fairly high standard for passing (80%). Though this can be frustrating, it ultimately drives you to buckle down and learn the material. Those who cannot devote a decent amount of time to the course will feel lost if they have no former introduction to the topic. If you can devote the time, it is rewarding and very well taught. Highly recommended.
Was this review helpful to you? YES | NO
2 out of 3 people found the following review useful
3 years ago
profile picture
Anonymous is taking this course right now.
This is definitely the best Coursera course I have ever done (and probably best MOOC). Very high quality materials, presented and explained extremely well and with a lot of real world practical examples which most stats courses do not have. It is a high workload and a high pass mark but you will get real, useful knowledge
Was this review helpful to you? YES | NO
2 out of 3 people found the following review useful
2 years ago
Aliaksandr Bely completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
Very time consuming course still doesn't contain any proofs. All the time spent just to make you memorise quite simple concepts but not to understand why do they work.

Don't wast your time, go through book and pdf slides and you will end up with the same knowledge many times faster.
Was this review helpful to you? YES | NO
2 out of 2 people found the following review useful
3 years ago
profile picture
Anonymous completed this course.
Thisi is one of the best course I ever took online or at university.

The content is challenging but the professor explains it so well that it is a pleasure to come back for more every week.

Its not easy, you have to work a lot to succeed but if you do, you will be very happy with this class.
Was this review helpful to you? YES | NO
1 out of 1 people found the following review useful
2 years ago
profile picture
Anonymous completed this course.
One of the very best courses - instructor was engaging, course material was challenging but extremely interesting. I find myself referring to this material repeatedly in my data science work.
Was this review helpful to you? YES | NO
5 months ago
profile picture
Anonymous completed this course.
I took this course a while back before it changed format. From the other reviews, I see that that it is still a great course. I had no background in statistical inference but lots in programming before taking the course so the R programming was fairly trivial and I could concentrate on learning data analysis and statis Read More
I took this course a while back before it changed format. From the other reviews, I see that that it is still a great course. I had no background in statistical inference but lots in programming before taking the course so the R programming was fairly trivial and I could concentrate on learning data analysis and statistical inference. The material was presented methodically and with many worked examples. This is not a 'fast-paced' course. If you need to cram material, this isn't the right course for that. This course teaches you the language of the scientific method - how to articulate a hypotheses that can be disproven. Like any language, the art of speaking it takes time and practice.
Was this review helpful to you? YES | NO
2 years ago
George Soilis completed this course, spending 20 hours a week on it and found the course difficulty to be medium.
Took the course,finished it but didnt pass,as igot a really bad grade in the midterm.The course is probably the best introductory-intermediate course available.Together with the book (Open Intro) makes a perfect start for anyone who wants to get serious with statistics.Time consuming,you really have to allow for lots o Read More
Took the course,finished it but didnt pass,as igot a really bad grade in the midterm.The course is probably the best introductory-intermediate course available.Together with the book (Open Intro) makes a perfect start for anyone who wants to get serious with statistics.Time consuming,you really have to allow for lots of work and lots of reasoning,as exercises tend to be with lengthy descriptions,and the quizzes too. In all a pleasant experience (it would have been better if i d passed :):))...I seriously reccomend it,and i am waiting for the next iteration ,in order to gain the certificate,which i consider a valuable addition to anyone's skill kit.
Was this review helpful to you? YES | NO
2 years ago
Adelyne Chan completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
A wonderful course on data analysis and statistical inference as well as the use of R (and R Studio, although there is the option for students to complete the assignments online using a DataCamp platform). I found the exercises and especially the project an extremely interesting endeavour, and the provision of an open- Read More
A wonderful course on data analysis and statistical inference as well as the use of R (and R Studio, although there is the option for students to complete the assignments online using a DataCamp platform). I found the exercises and especially the project an extremely interesting endeavour, and the provision of an open-courseware textbook is a very nice gesture by the instructor.

For other learners, particularly those who are not particularly after a certificate and don't want to wait till the next offering of the course, it is also possible to cover the same course material through the DataCamp platform at your own leisure!
Was this review helpful to you? YES | NO
2 years ago
Caio Taniguchi completed this course, spending 12 hours a week on it and found the course difficulty to be medium.
One of the best MOOCs out there, not really much to say besides that. Very time consuming if you choose to join the track with programming assignments. However, they are totally worth your time. The recommended exercises are also useful, and the final project was interesting as well. It's just the perfect MOOC (for me, at least).
Was this review helpful to you? YES | NO
3 years ago
Stacev completed this course.
Very good practical oriented course. All concepts clearly explained. Many useful examples from real world are demonstrated in detail. Flow is very smooth from basic to advanced topics.
Was this review helpful to you? YES | NO
9 months ago
Chandana Sapparapu completed this course.
Dr.Mine is one of the best professors I've known. She can communicate arcane concepts very effectively and her passion is seen in every lecture.
Was this review helpful to you? YES | NO
0 out of 1 people found the following review useful
2 years ago
Sankalp Yadav is taking this course right now.
Was this review helpful to you? YES | NO
0 out of 1 people found the following review useful
3 years ago
Vlad Podgurschi completed this course, spending 14 hours a week on it and found the course difficulty to be hard.
Was this review helpful to you? YES | NO
a year ago
Sajan Skandakumar completed this course.
Was this review helpful to you? YES | NO
2 years ago
profile picture
Rafael Prados completed this course.
Was this review helpful to you? YES | NO
2 years ago
Abbhinav Srivastava is taking this course right now.
Was this review helpful to you? YES | NO
2 years ago
Masato Yonekawa dropped this course.
Was this review helpful to you? YES | NO
7 months ago
Visitantz completed this course.
Was this review helpful to you? YES | NO
5 months ago
profile picture
César Alba completed this course.
Was this review helpful to you? YES | NO
2 years ago
profile picture
Fabian Hoffmann dropped this course.
Was this review helpful to you? YES | NO
2 years ago
profile picture
Zoltan Sánchez completed this course.
Was this review helpful to you? YES | NO
2 years ago
Cristina partially completed this course.
Was this review helpful to you? YES | NO
2 years ago
Ben M. completed this course.
Was this review helpful to you? YES | NO
2 years ago
Colin Khein completed this course.
Was this review helpful to you? YES | NO
7 months ago
Ashlynn Pai completed this course.
Was this review helpful to you? YES | NO
2 years ago
Met Bay completed this course, spending 8 hours a week on it and found the course difficulty to be medium.
Was this review helpful to you? YES | NO
2 years ago
profile picture
Robert Moran-birch is taking this course right now.
Was this review helpful to you? YES | NO
2 years ago
Gaetano Pagani completed this course.
Was this review helpful to you? YES | NO
2 years ago
Lace Lofranco is taking this course right now.
Was this review helpful to you? YES | NO
2 years ago
Mark Henry Butler completed this course.
Was this review helpful to you? YES | NO
2 years ago
Matteo Ferrara completed this course.
Was this review helpful to you? YES | NO