This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.
This is the first course in the Genomic Data Science Specialization.
In this Module, you can expect to study topics of "Just enough molecular biology", "The genome", "Writing a DNA sequence", "Central dogma", "Transcription", "Translation", and "DNA structure and modifications".
In this module, you'll learn about polymerase chain reaction, next generation sequencing, and applications of sequencing.
The lectures for this module cover a few basic topics in computing technology. We'll go over the foundations of computer science, algorithms, memory and data structures, efficiency, software engineering, and computational biology software.
Data Science Technology
In this module on Data Science Technology, we'll be covering quite a lot of information about how to handle the data produced during the sequencing process. We'll cover reproducibility, analysis, statistics, question types, the central dogma of inference, analysis code, testing, prediction, variation, experimental design, confounding, power, sample size, correlation, causation, and degrees of freedom.
Brandt Pence completed this course, spending 1 hours a week on it and found the course difficulty to be very easy.
This is the first course in the new (at the time of this writing) Genomic Data Science specialization, offered by Johns Hopkins through Coursera. This course is similar to the Data Scientist’s Toolbox course which leads off the Data Science specialization...
This is the first course in the new (at the time of this writing) Genomic Data Science specialization, offered by Johns Hopkins through Coursera. This course is similar to the Data Scientist’s Toolbox course which leads off the Data Science specialization in that it is scheduled for 4 weeks but really only takes a few hours to complete.
Indeed, this course is even less useful than the Data Scientist’s Toolbox, which at least had some practical aspects (installing and learning Git, for example). Here, the meat of the course is video lectures with titles such as “Just Enough Cell Biology”, “Why Care About Statistics?”, and “What is Computational Biology Software?”. The level of detail in these will certainly not impress anyone with training in biology or computer science, and most individuals (or at least those who post in the forums) come from one of those backgrounds, with some majority from the latter.
The quizzes (there are four) are very easy. Without referring to my notes, I needed a second attempt to score 10/10 for only one of them, and that was due to a silly error on my part. The final project asks you to read a Science paper (published not-coincidentally by the course directors) and to take a quiz covering information from that paper. The paper is dense but not particularly difficult if you have a biology background (as I do), and you are given 3 attempts at the quiz here as well, which should allow those with weak biology backgrounds a reasonable chance to score highly.
Overall, one star. This course was essentially useless and should not cost $49. I expect that, with sufficient complaint volume, the course will drop to $29 similar to the intro course in the Data Science sequence. I finished this course Tuesday morning after starting it Monday night and spending most of that night working on lectures and quizzes for R Programming, and I signed up for and started the next specialization course on the Galaxy platform immediately.
Adelyne Chan completed this course, spending 3 hours a week on it and found the course difficulty to be easy.
I have a background in biology and therefore found this course relatively easy, I mainly took it for completeness as I also intend to take the other courses in this series. Jeff Leek has shown through the Data Science specialisation that he is an effective MOOC instructor and this course is no different, well organised and sets a good foundation for what I hope are other equally wonderful courses in this specialisation (I am not signed up for the specialisation, but intend to take all courses in the series) which I am very much looking forward to!