We currently offer 3 Specializations on Coursera: Data Science, Executive Data Science, and Genomic Data Science. These programs each have unique aspects:

Data Science - This is the first, most comprehensive (9 courses), largest (2 million+ enrollers, 1,000+ completers), and most science-driven data science Specialization on Coursera.

Executive Data Science - This is the only data science Specialization specifically designed for managers of data scientists. All the courses are designed to fit into a busy managers schedule (1 week courses) and all are on demand. We are also designing a cool interactive capstone experience with Zillow for this one.

Genomic Data Science - This is the only program covering Genomic Data Science on the Coursera platform. This is a major area of growth with interest in personalized medicine increasing by the day. The course covers the tools needed to understand and analyze data from next generation sequencing.

Data Science - covers the spectrum of data science problems from Git/Github, to R, to specific tools/packages for data cleaning, inference, and machine learning. This course is largely R based since R is the most widely used language for data science in the wild.

Executive Data Science - covers a crash course in the basics of what data science is, how to build a data science team, and how to manage that team to success.

Genomic Data Science - covers an introduction to genomic technologies, python, Galaxy, R, the command line, Bioconductor, and statistics for genomics. This course is designed to get a person "up to speed" on doing genomic data science.

Parts of these courses are incorporated into programs in the Biostatistics (http://www.jhsph.edu/departments/biostatistics/), computer science (https://www.cs.jhu.edu/), biology (http://www.bio.jhu.edu/) and computational biology (https://ccb.jhu.edu/) programs at JHU. But these specializations were designed specifically for the MOOC platform to be available and fit into the schedules of people taking courses online.

We are really excited about making classes available to the world and hope that they will be useful for people getting into a new field, transitioning careers, or looking for a job.

Our specialization is unique, not just as a data science specialization, but as a series of STEM MOOCs in general, in a few different ways. First, it has an enormous amount of production for a series of MOOCs, and is the result of a development team working for the past two years. For example, although our courses have lecture videos with very high production quality, the production of these lecture videos represents a very small component of our overall investment of time and resources (unlike most MOOCs, for which this is essentially the sole focus). Instead, our MOOCs are built upon the creation of an interactive textbook applying the principles of active learning. As soon as learners encounter a tricky concept, we ask them to stop and think about it before transitioning. We peppered the text with hundreds of exercises; some of these build learning, others are opportunities for learners to implement the bioinformatics algorithms that they encounter, and others allow them to apply these algorithms to real biological datasets. Each page of the interactive text is linked to its own discussion forum, and students have made thousands of posts over the last two years. Furthermore, an important part of the process of developing this interactive text was responding to student concerns. To do this, we mined through 8500 discussion forum posts and have made widescale changes to every single page of the interactive text, as well as creating FAQs and additional remedial learning modules to help address the most common errors encountered by learners. Pavel and I outlined our vision for what 21st century textbooks in STEM fields should look like in a recent Communications of the ACM Viewpoints article:http://m.cacm.acm.org/magazines/2015/10/192385-life-after-moocs/fulltext

Second, bioinformatics is inherently interdisciplinary, being at the intersection of computer science, biology, mathematics, and data science. As such, it attracts learners who arrive with varying strengths. It means that we have had to think about how to adapt the content for learners with these strengths. For example, our courses are currently divided into two main tracks: a "biologist track" and a "hacker track". All learners read the course interactive text, watch the course videos, and take quizzes. However, learners on the hacker track implement the algorithms that they encounter in the text; learners following the biologist track do not need to program but do need to learn how to apply existing software resources in bioinformatics. Accordingly, we also have a series of "Bioinformatics Application Challenges" for these learners in which they can learn how to apply some of this existing software while following a narrative that is tangential to what they have learned in the main text. For example, in the main text, learners see how researchers sequence genomes by solving a 300-year-old mathematical puzzle. The hacker track learners write their own algorithms (in the language of their choosing) to assemble genomes on their own; the biologist track learners have an Application Challenge walking them through how to use the popular SPAdes assembler to analyze the quality of an assembly for Staphylococcus.

The greatest central theme of our Bioinformatics Specialization is the importance of being able to formulate a biological challenge as a precise computational problem. This is a skill that is often lacking in life science education. For example, when looking for the location in a bacterium's DNA where the bacterium starts replicating this DNA, we are essentially looking for a "hidden message" saying "start replication here!" But this problem makes no sense to a computer scientist, who needs to be told exactly what to look for. The interplay between learning new biological facts and using these facts to formulate increasingly robust computational problems is constant throughout our courses.

In terms of specific course content, each chapter of content addresses a central questions to modern biology such as "How Do We Assemble Genomes?", "Why Do We Still Not Have an HIV Vaccine?", and "How Do We Find Disease-Causing Mutations?" We see how approaches from a variety of technical topics such as graph theory, machine learning, and standard data science methods such as clustering algorithms can be applied to solve each central question. From a biological perspective, we have a heavy focus on biological sequence analysis and the methods needed to address it.

The course content covered in the Bioinformatics Specialization is identical to some of the coursework taken by students in the renowned Bioinformatics Ph.D. program at UC San Diego. Furthermore, the print companion of the course (Bioinformatics Algorithms: An Active Learning Approach) has already been adopted in about twenty universities, some of which offer flipped classes based on the book. This is another way in which we feel that our courses are unique, as the majority of online courses do not currently have the rigor of a course that one would take at a leading offline institution.

We are very proud of partnering with Illumina (the leader in Genome Sequencing) to design a really interesting Capstone project (launching in the spring) based on their BaseSpace cloud platform, and Illumina is interested in interviewing students who excel in our Specialization. More generally, we think that our Specialization has excellent potential to be adopted by many university programs in bioinformatics around the world, and that it will be a great resource for biologists, computer scientists, and data scientists alike to add an important set of knowledge to their skillset in the rapidly growing biotech market. Learners in the latter two groups may have never even realized how relevant many classic approaches in CS and data science really are to modern biology, and the enormous demand for people who can bring these skillsets to biology, and we hope that our Specialization can help bridge this divide.