Specialization via Coursera and Johns Hopkins University
$392 for 6-8 months
3-7 hours a week of effort
6 courses + capstone project
This specialization will teach you to understand, analyze, and interpret data from next-generation sequencing experiments. You will learn common tools of genomic data science, including Python, R, Bioconductor, and Galaxy. These courses can serve as a stand-alone introduction to genomic data science or can compliment to a primary degree or postdoc in biology, molecular biology, or genetics. The Specialization concludes with a Capstone project that allows you to apply the skills you've learned throughout the courses.
18th Nov, 2019
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.
Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University.
We will learn computational methods -- algorithms and data structures -- for analyzing DNA sequencing data. We will learn a little about DNA, genomics, and how DNA sequencing is used. We will use Python to implement key algorithms and data structures and to analyze real genomes and DNA sequencing datasets.
I have completed this specialization in the first cohort. There are six courses and a 2.5 month capstone. Some of the four-week classes are very basic, and easy to pass at the 70% level. Some of the advanced classes are generally more difficult to fo…
I have completed this specialization in the first cohort. There are six courses and a 2.5 month capstone. Some of the four-week classes are very basic, and easy to pass at the 70% level. Some of the advanced classes are generally more difficult to follow through, the capstone has tough/time-consuming subtasks (but is also designed to be a "realistic" assignment were you work with multi-gigabyte .fastq files).
The first class in the program is a general introduction to next-gen sequencing (NGS). The other classes are, from a programming-language perspective: three python classes (intro to Python, intro to the python-based Galaxy software-as-a-service platform, bioinformatics algorithms); one command-line class (simple bash programming); two R classes (Bioconductor, biostatistical analysis) . The capstone is given by the guys who teach the R classes. Thus, in applying the knowledge gained in these two classes most participants preferred to work with R at the end. You need to be pretty familiar with python and more proficient with R ( but not an expert in either language) to complete the entire 7-course specialization.
During the capstone project you'll work with R and many command line tools developed for RNA-seq analysis. The capstone was by far the most demanding course to take. You really re-apply a lot of diverse stuff from the prerequisite classes, except for the low-level python programming.
The bioinformatics-algorithms classes (focused on building indexes for NGS alignment softare) were very popular and are highly liked and ranked. Same for the command-line-tools class.
However, the heterogeneity of the class contents shows. You could take the classes in any order. For the entire specialization ,I'd say "The sum of its parts is NOT more than the sum of its parts".
Forum participation was quite low during the more advanced classes. The instructors themselves have participated with mixed enthusiasm in the forums. Some were very eager to answer questions, maybe its a new experience for them. Some were responsive but did not take much action on improving the course content. Some were seen very rarely. Maybe the instructors were disappointed in actual MOOC participation rates, and/or have moved on to other projects, up or out. Maybe they cannot spend much time looking closely, when the classes are being run routinely, periodically, all the time, and the questions keep repeating.
You have to be really passionate for the subject to complete this.
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