Do you want to learn how to harvest health science data from the Internet? Or learn to understand the world through data analysis? Start by learning R Statistics!
Skilled professionals who can process and analyze data are in great demand today. In this course you will explore concepts in statistics to make sense out of data. You will learn the practical skills necessary to find, import, analyze and visualize data. We will take a look under the hood of statistics and equip you with broad tools for understanding statistical inference and statistical methods. You will also perform some really complicated calculations and visualizations, following in the footsteps of Karolinska Institute’s researchers.
Statistical programming is an essential skill in our golden age of data abundance. Health science has become a field of big data, just like so many other fields of study. New techniques make it possible and affordable to generate massive data sets in biology. Researchers and clinicians can measure the activity for each of 30000 genes of a patient. They can read the complete genome sequence of a patient. Thanks to another trend of the decade, open access publishing, the results of such large scale health science are very often published for you to read free of charge. You can even access the raw data from open databases such as the gene expression database of the NCBI, National Center for Biotechnology Information.
We will dive into this data together. Learn how to use R, a powerful open source statistical programming language, and see why it has become the tool of choice in many industries in this introductory R statistics course.
MOOCs stand for Massive Open Online Courses. These arefree online courses from universities around the world (eg. StanfordHarvardMIT) offered to anyone with an internet connection.
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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.
Explore statistics with R is a 5-week introductory level course offered by the Karolinska Instetutet through the edX platform, covering the basics of R, statistics and using R for statistical analysis. The course covers 3 main topic areas in 4 weeks--R basics, getting and manipulating data in R and statistical tests in
Explore statistics with R is a 5-week introductory level course offered by the Karolinska Instetutet through the edX platform, covering the basics of R, statistics and using R for statistical analysis. The course covers 3 main topic areas in 4 weeks--R basics, getting and manipulating data in R and statistical tests in R. The 5th week consists of a final graded assignment where you follow along with research project conducted in R. Each week consists of a few short lecture videos followed by a series of graded quiz questions. The class awards a certificate if you achieve a total score at last 60% on the quizzes and the final graded assignment.
Explore stats in R offers some quality content, but it is too short constitute a complete intro to R or stats. The professor speaks clearly, explains concepts well and seems genuinely excited to be teaching the course. I noticed he seemed active on the course's discussion boards, which is nice to see. The quizzes were too few and a bit too easy: they generally tested conceptual knowledge and did not require the student to do much in R besides copy, paste and run code. As a course focused on statistical operations, it didn't teach basic programming concepts in R like control flow and functions. This course could benefit from having a bit more content each week and beefing up the homework exercises to force students to do a little bit more in R on their own. Extending the course by a couple of weeks would also give the professor time to cover some neglected topics like programming basics in R and more on data visualization. With expanded content this could be a great course, so hopefully they'll make some tweaks and additions and offer it again.
Alun Ap Rhisiartcompleted this course, spending 3 hours a week on it and found the course difficulty to be medium.
This was a very well presented course. I have programming experience in a number of languages, but not R. This course was a great, and very approachable, introduction to statistical analysis and R. You don't need to know much statistics, but it does help.
Brandt Pencecompleted this course, spending 1 hours a week on it and found the course difficulty to be very easy.
This course, offered by the Karolinska Institute, is a gentle introduction to the R language. I have to admit that I only watched a few of the videos. These are very high quality compared to the Johns Hopkins Data Science courses, but this course suffers from the same issues as many of the other introductory R courses
This course, offered by the Karolinska Institute, is a gentle introduction to the R language. I have to admit that I only watched a few of the videos. These are very high quality compared to the Johns Hopkins Data Science courses, but this course suffers from the same issues as many of the other introductory R courses out there, namely, that there is very little true challenge in these courses and that most of the exercises are simply copying code and interpreting the output. Despite the criticisms of R Programming and the other JHU courses, those courses do force students to learn to write code in order to solve the problems. I finished this course with a 92% in a bit over 2 hours of total work, and I imagine anyone who has any experience with R could do likewise. A few of the questions were a bit tricky (but didn't require any programming), and they only give you two attempts, so it might still be difficult to get 100% in the course without careful study, but passing this one (pass line 56%) should be no problem.
Overall, two stars. This might be useful as a gentle introduction to the R language, but you won't learn to code well by taking this course.