Latest Courses
SQL/ETL Developer - T-SQL/Stored Procedures/ETL/SSISCheck course
ServiceNow IT Service Management (CIS-ITSM) Rome Delta TestsCheck course
React JS - Build 5 Projects With (Redux, React Router, MUI)Check course
Ansible MasteryCheck course
Game Development for beginners with PythonCheck course
Advanced Web Animations with GSAP [ JavaScript SVG CSS ]Check course
R for Data Science: Your First Step as a Data ScientistCheck course
ARM 64-bit Assembly Language with Raspberry PiCheck course
SQL Server Master Series - Beginner CourseCheck course
Build A TodoList with Laravel and ReactCheck course
SQL/ETL Developer - T-SQL/Stored Procedures/ETL/SSISCheck course
ServiceNow IT Service Management (CIS-ITSM) Rome Delta TestsCheck course
React JS - Build 5 Projects With (Redux, React Router, MUI)Check course
Ansible MasteryCheck course
Game Development for beginners with PythonCheck course
Reproducible Research

Reproducible Research

FREE

Add your review
Add to wishlistAdded to wishlistRemoved from wishlist 0
Add to compare
8.7/10 (Our Score)
Product is rated as #88 in category Data Science

This course focuses on the concepts and tools behind reporting modern data analyses in a reproducible manner. Reproducible research is the idea that data analyses, and more generally, scientific claims, are published with their data and software code so that others may verify the findings and build upon them. The need for reproducibility is increasing dramatically as data analyses become more complex, involving larger datasets and more sophisticated computations. Reproducibility allows for people to focus on the actual content of a data analysis, rather than on superficial details reported in a written summary. In addition, reproducibility makes an analysis more useful to others because the data and code that actually conducted the analysis are available. This course will focus on literate statistical analysis tools which allow one to publish data analyses in a single document that allows others to easily execute the same analysis to obtain the same results. The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life–long learning, to foster independent and original research, and to bring the benefits of discovery to the world.

Instructor Details

Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and a Co-Editor of the Simply Statistics blog. He received his Ph.D. in Statistics from the University of California, Los Angeles and is a prominent researcher in the areas of air pollution and health risk assessment and statistical methods for environmental data. He is the recipient of the 2016 Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to health statistics. He created the course Statistical Programming at Johns Hopkins as a way to introduce students to the computational tools for data analysis. Dr. Peng is also a national leader in the area of methods and standards for reproducible research and is the Reproducible Research editor for the journal Biostatistics. His research is highly interdisciplinary and his work has been published in major substantive and statistical journals, including the Journal of the American Medical Association and the Journal of the Royal Statistical Society. Dr. Peng is the author of more than a dozen software packages implementing statistical methods for environmental studies, methods for reproducible research, and data distribution tools. He has also given workshops, tutorials, and short courses in statistical computing and data analysis.

Specification: Reproducible Research

Duration

11 hours

Year

2015

Certificate

Yes

Quizzes

Yes

53 reviews for Reproducible Research

4.0 out of 5
36
8
5
2
2
Write a review
Show all Most Helpful Highest Rating Lowest Rating
  1. Israel D D G

    Good material

    Helpful(0) Unhelpful(0)You have already voted this
  2. Moshe P

    The course seems to be based on lectures recorded at different times. Some points discussed are repetitive. the quality of content is good though. I believe the whole material may have to be updated and, potentially, re–recorded.

    Helpful(0) Unhelpful(0)You have already voted this
  3. Nino P

    To be a data scientist you must use RMarkDown. Here you learn how to use it. A must do course for data scientists and highly valuable.

    Helpful(0) Unhelpful(0)You have already voted this
  4. Rok B

    Not the most important course in the series, but I give it 3 stars. Positives, I’m impressed with RMarkdown. It is a handy tool to make reproducible research. I also think the final assignment was very interesting. You can train cleaning data. Negatives, lectures from weeks 3 and 4. They are poorly recorded and have little to no value for the course

    Helpful(0) Unhelpful(0)You have already voted this
  5. Avolyn F

    I was really passionate about the subject matter, but, although I have experience in R, apparently not enough to complete the assignment. Would have liked a little more warning that this would be needed, I was more interested in the topic of Reproducible Research, which while I agree is easier done via code of some kind, shouldn’t be a topic specific to R, should be applicable to Python, SQL, whatever. Might have time to revisit this, but will probably need to take a few more R classes to even be able to complete, likely won’t get around to it, but the first 2 weeks were worth the cost of paying for a certificate, I guess.

    Helpful(0) Unhelpful(0)You have already voted this
  6. gerson d o

    GREAT course!!!!!!!!!!!!!!!!!!

    Helpful(0) Unhelpful(0)You have already voted this
  7. Jean–Philippe M

    Lack of practical cases. The two cases are not really interesting and lack of details.

    Helpful(0) Unhelpful(0)You have already voted this
  8. Jean Philippe M

    Lack of practical cases. The two cases are not really interesting and lack of details.

    Helpful(0) Unhelpful(0)You have already voted this
  9. Stefan H

    Very repetitive in context of earlier introduction to the topic and also throughout the weeks. Generally it doesn’t feel there is much of a take–away and not sure it deserves its own course.

    Helpful(0) Unhelpful(0)You have already voted this
  10. Edouard A

    Interesting projects

    Helpful(0) Unhelpful(0)You have already voted this
  11. Amy B P

    Very good course!

    Helpful(0) Unhelpful(0)You have already voted this
  12. Santi M

    good course

    Helpful(0) Unhelpful(0)You have already voted this
  13. Lakshay S

    Pathetic It was . Not at all Interesting !!

    Helpful(0) Unhelpful(0)You have already voted this
  14. Joe D

    The two projects were interesting and built on skills learned in the previous four courses in this specialization that focused on using the R language. The video lectures were largely repetitions of the course text, which is fine, some people prefer videos, others prefer texts. (I read the text and was fine with skipping around the videos and/or playing them at 2x speed.) Perhaps the most useful skills learned in this course were during the projects where we did some data cleaning and analysis, then wrote up our results in an R markdown file (.Rmd) and published to Rpubs. Overall enjoyable experience. The most useful “hack” was learning how to preserve markdown files in the Rstudio settings so that when you push your .Rmd s to github, you get a nice readable markdown file.

    Helpful(0) Unhelpful(0)You have already voted this
  15. Martin G

    Interesting content. However, it can get somewhat repetitive.

    Helpful(0) Unhelpful(0)You have already voted this
  16. Ishwarya M

    Without taking this course wouldn’t have fully understood the importance of reproducible research in data science. Thank you so much. I recommend this course for all data scientists.

    Helpful(1) Unhelpful(0)You have already voted this
  17. Thiago

    course material and projects help a lot in learning and tips on how to better document research and projects

    Helpful(0) Unhelpful(0)You have already voted this
  18. Jessica R

    Very useful in bringing together skills learned in the earlier courses of the Data Science specialization: R programming, R Markdown, knit, RPubs.

    Helpful(0) Unhelpful(0)You have already voted this
  19. Connor B

    Course Project 2 was fun and learned a lot

    Helpful(0) Unhelpful(0)You have already voted this
  20. Jorge B S

    I have found this course very useful in order to learn the keys of reproducible research. Moreover, both course projects are useful for putting other skills of this specialisation into practice. I recommend it!

    Helpful(0) Unhelpful(0)You have already voted this
  21. Robert J C

    It’s good to learn R Markdown.

    Helpful(0) Unhelpful(0)You have already voted this
  22. Bla~ Z

    Very nice project at the end of the course!

    Helpful(0) Unhelpful(0)You have already voted this
  23. Tomasz S

    Extremely important course.

    Helpful(0) Unhelpful(0)You have already voted this
  24. Rizwan M

    good

    Helpful(0) Unhelpful(0)You have already voted this
  25. Muhammad Z H

    learning alot

    Helpful(0) Unhelpful(0)You have already voted this
  26. Abu R B K

    Good but quite hard

    Helpful(0) Unhelpful(0)You have already voted this
  27. Mehrdad P

    Course nicely highlighted the importance of reproducible research and the use of markdown and knitr packages.

    Helpful(0) Unhelpful(0)You have already voted this
  28. Angela C

    Fantastic course!

    Helpful(0) Unhelpful(0)You have already voted this
  29. Courtney R

    I really appreciated the topics covered in this course. Is a wonderful follow–up to the Exploratory Data course.

    Helpful(0) Unhelpful(0)You have already voted this
  30. Nilrey J D C

    A very good course to know why it is important to have a reproducible research.

    Helpful(0) Unhelpful(0)You have already voted this
  31. Pitak P

    Thank you

    Helpful(0) Unhelpful(0)You have already voted this
  32. Ratanaporn

    Congratulations

    Helpful(0) Unhelpful(0)You have already voted this
  33. Luiz E B J

    This is a good course tht open our minds and eyes to the relevance of Reproducible Research.

    Helpful(0) Unhelpful(0)You have already voted this
  34. Esteban R F

    The projects in this course were a real challenge, which demanded to tackle those problems with a mind willing to go to the hedge and discover new horizons. The result was that I ended up the course with real skills for processing data in a reproducible process.

    Helpful(0) Unhelpful(0)You have already voted this
  35. Fabien N

    I really liked the assignments projects !

    Helpful(0) Unhelpful(0)You have already voted this
  36. Manuel M M

    Nice course. you learn quite a lot of things although it could be a little bit more complex

    Helpful(0) Unhelpful(0)You have already voted this
  37. Johnnery A

    Excellent!

    Helpful(0) Unhelpful(0)You have already voted this
  38. Shubham S

    Thank you instructors, for making me realize the importance of reproducible research.

    Helpful(0) Unhelpful(0)You have already voted this
  39. Marcela Q

    A little repetitive and basic but useful!!!!

    Helpful(0) Unhelpful(0)You have already voted this
  40. Amit S

    Very Good Content

    Helpful(0) Unhelpful(0)You have already voted this
  41. Eduardo S B

    The course is nice. However, I think the last assignment is simply too much.

    Helpful(0) Unhelpful(0)You have already voted this
  42. Trevor G

    I thought this was a very helpful class. Brought together the first 4 classes really nicely.

    Helpful(0) Unhelpful(0)You have already voted this
  43. Anton K

    The material is shallow. Projects are way too time demanding. Everybody knows that data cleaning is a routine and long process. That is precisely why nobody likes it. And if there’s only one way to clean the data – by hand and only after reading a lot of related database documentation – this kills all the fun of studying and makes the overall picture of consepts relations unclear.

    Helpful(0) Unhelpful(0)You have already voted this
  44. Onedio S S J

    Excelent!

    Helpful(0) Unhelpful(0)You have already voted this
  45. Gary T

    Very critical course!!

    Helpful(0) Unhelpful(0)You have already voted this
  46. Jordan I

    Great course that provides a structure for analysis and how to challenge the analysis. I found the assignments hard. The lack of information about the data for the assignment represented a real–life situation.

    Helpful(0) Unhelpful(0)You have already voted this
  47. Mathew K

    A pretty good coverage on the need for reproducibility and the best practices to make it happen.

    Helpful(0) Unhelpful(0)You have already voted this
  48. Amanyiraho R

    Very interesting and tackles a very important issue that Data scientists always miss–out, reproducibility of your project

    Helpful(0) Unhelpful(0)You have already voted this
  49. ONG P S

    Very practical and knowledge learned can be applied into my works as auditors. This can benefit any fields involving using data.

    Helpful(0) Unhelpful(0)You have already voted this
  50. Willie C

    Lecture videos were very repetitive. Course projects were repetitive, too. Important information, but didn’t need to be stretched out over a full “four–week” course.

    Helpful(0) Unhelpful(0)You have already voted this
  51. Rob S

    very interesting, but a pity about the errors that occur due to incompatible software

    Helpful(0) Unhelpful(0)You have already voted this
  52. Vishwamitra M

    Highly recommended for beginners to learn the basics of Data Science, Re producibility and how to write a good report around the analysis done by you as a data analyst.

    Helpful(0) Unhelpful(0)You have already voted this
  53. David W

    Very interesting content

    Helpful(0) Unhelpful(0)You have already voted this

    Add a review

    Your email address will not be published. Required fields are marked *

    This site uses Akismet to reduce spam. Learn how your comment data is processed.

    Price tracking

    Java Code Geeks
    Logo
    Register New Account
    Reset Password
    Compare items
    • Total (0)
    Compare