With genomics sparks a revolution in medical discoveries, it becomes imperative to be able to better understand the genome, and be able to leverage the data and information from genomic datasets. Genomic Data Science is the field that applies statistics and data science to the genome. This Specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most common tools used in genomic data science including how to use the command line, along with a variety of software implementation tools like Python, R, and Bioconductor. This Specialization is designed to serve as both a standalone introduction to genomic data science or as a perfect compliment to a primary degree or postdoc in biology, molecular biology, or genetics, for scientists in these fields seeking to gain familiarity in data science and statistical tools to better interact with the data in their everyday work. To audit Genomic Data Science courses for free, visit https://www.coursera.org/jhu, click the course, click Enroll, and select Audit. Please note that you will not receive a Certificate of Completion if you choose to Audit.
very basic level for people with basic knowledge of molecular biology and bioinformatics. Yet, it is adequate level of information necessary to start the next courses in this specialization.
A very good course for its length and the amount of time it requires. It improved my python skills and knowledge of Genomics. I'm more engaged in my pursuits than before taking the course.
Very good course this is. Just a little advice, please make the quiz questions more clear and more specific because one shouldn't waste time to understand a question which is very easy to implement.
Very interesting and detailed course.Most I liked the tasks: they are very detaild. Thanks to them I've leraned a lot of new things.
It's very helpful in learning genomic data science. I think it's a little difficult in the beginning. The instructor have some accent, which made me even harder to get into it.
The professor is really enthusiasm, so I was really impreesed by him. And his teaching is brief, and I can learn key points through the lectures. Great course!
Having a Computer Science background, the course helped me understand the foundational knowledge of Biology and Statistics. The explanations were easy to understand and digest.
You better know a lot about Python if you plan to pass this course!! Items covered in lecture are elementary. Items covered in exams/quiz's are Graduate Level!!!
Very well explained, a lot of the gaps of the previous courses got cleared up. This course should be an example on how to teach a subject. Thanks!!!
Leart a lot. Recommend all others this course. Lecturer also cool and explain every bit of course contents.
I think it is a good class overall but the pace is fast and the resources that can help you is lacking. This will be good for someone who already kind of understands bioconductor.
Pretty good but a little superficial and outdated.
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Great introduction to Genomics. The instructors really laid out the practical terms and gave great resources. Weeks 3 and 4 ramp up in difficulty, so some outside research in statistics is helpful.
This course provides a gentle introduction to the concepts and terminology associated with genomics. As someone who never took a biology course in high school or university, this course is perfect.
Relatively nice introduction course, contents are maybe rather limited, yet as an instructive course, it does provide a clear overview which correlates well with the required answers for the quizzes!
It is a great introduction course if you really don't know anything about the field. But if you think you have got the basics down, that means this course will be too easy for you.
This is my first course in Genomic Technologies (and one I completed overall). I was taking this course bit lightly for being the introductory/basic course but I have learnt a number of new things: Introduction to some stats terms I overlooked/haven't learnt before like p-test and confounders, the incredible example of data-counterfeiting in the academia and how PCR and next-gen sequencing technologies work. Although I couldn't learn in depth, which is obvious since its first of the 8 course specialization, so I am anxious to take the next course - this specialization is looking very exciting.
Have tried a few other courses around introduction to genomic technologies but this is the best structured course I have taken. Loved the course project as it gives a good insight on how to read scientific papers and what is expected from a genomic scientist. The only point would be to improve the statistics content(week 4) and make it more beginner friendly especially when it comes to p-values.
A good basic introduction. Might have been nice to have had recommendations for further reading for those who were interested.
I have done the data sciences specialization from JHU and that was 4 star on 5. Compared to that, this specialization is pretty bad.
Not worth doing this specialization. Instructors are clueless on the plan
I found the course to be very clear and informative. I believe the molecular biology aspect was easy enough for someone without a background in biology or biochemistry to understand. Also, the computer science and statistical aspects were also explained in a clear way.
Very good introductory course. It's a lot of material to cover for a newbie, but if you have some background, you will find you progress pretty quickly. Enjoyable, engaging, fun and useful.
It was disappointing to see that time wasn't taken to fix and improve lectures where mistakes were made in the teachings. Although warning dialogues were displayed and the updated class materials were made available, the audio is still misleading. The transcripts also need to be cleaned up ("batch effects" transcribed as "batrifacts", etc.). It was also surprising to see the huge gaps in expected prior knowledge across subjects. For example, a multiple-choice question in one of the assignments required understanding the role of ribosomes in protein synthesis, but ribosomes were not covered in the lectures. This is high-school biology, so it was less of a shock compared to the questions around topics like Family Wise Error Rate, which was covered superficially at best in the lectures, with a teacher who was very difficult to follow (awkward phrasing, chaotic structure of courses, stuttering, lack of vocalization, would quickly discuss complex or advanced mathematical topics without explaining the basics, but spend a long time to wordy explanations of simple subjects that did not require dumbing down). The assignment questions on Family Wise Error Rate however, required a very good understanding, not just conceptually, but actually knowing how to apply the formulas and calculate values which was not taught at any point during the lecture. All in all the course has potential but it needs some cleanup, better visual supports for complex topics, more structured scripts/outlines for teachers not as comfortable speaking in front of the camera, and a better baseline for the expected level of prior knowledge and expertise from students (lots of time spent drilling topics from high-school biology, not nearly enough time dedicated to explaining college-level mathematics/statistics concepts and teaching how to practically apply statistical concepts, formulas, etc.).
It seemed a little too easy, especially for the timeline given. Seemed like there should have been more information, or have gone more in-depth
Good intro for people who don't have any background, a little too simple for people who already work in this criteria or have knowledge about
Great way of delivering information.
The initial lectures on introduction to genomics are very engaging and good starting point for everyone who slightly touched that topic before and is interested in both history and recent developments in the area.
The week focusing on software engineering doesn't seem to be a good fit for such training. Most of the content is dedicated to extremely trivial computer science topics hardly related with bioinformatics. Only last several minutes give examples of bioIT software and systems.
The week with statistics is good only if you posses solid knowledge on statistics. Then it serves as a good recap. Otherwise you might be completely lost 5 min into the content, e.g. there are much better explanations of p-value and family-wise error out there in internet than the one provided in the course.
The idea of a project to go through a research paper and answer the questions about it was fantastic! Very good way to cement what you learned.
Overall: 3 stars. I would give 4 if not for trivial and rather useless computer science intro. At few points, the pace should be adjusted (e.g. statistics).
While I wasn't expecting heavy material for an intro course, this course did not reach even a 101 level in my opinion. Take this course if you have never heard of biology or computer science or statistics but avoid if you know even a little about these subjects. Sometimes the quiz questions asked about details not covered in videos/papers, which was pointless and frustrating.
I am a PhD student in genetics, so the background material there was review from undergrad courses. Obviously some people who are not biologists will need this background. I do wish, however, that the statistics intro was more applied and covered a better intro and application to the why!
Good class but incredibly basic. Needs to go a bit more in depth. Feels rushed.
This course if just a quick fly-by overview. You don't learn anything relevant in biologiy, computer science or statistics. Some questions in the quiz were not part of the course which is really annoying. Anyhow, I like the idea of reading a paper as an academic part of this course.
Quite good explanation of molecular biology and statistics. Hope the narrator can lower down the speed, since it is a little tough for a non-native English speaker to catch-up with it (Though the pronunciation is very clear)