What 2 or 3 concepts or technologies does your specialization focus on the most?
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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. For example, in the main text, learners see how researchers sequence genomes by solving a 300-year-old mathematical puzzle.
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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.
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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. All learners read the course interactive text, watch the course videos, and take quizzes. For example, our courses are currently divided into two main tracks: a "biologist track" and a "hacker track". It means that we have had to think about how to adapt the content for learners with these strengths. As such, it attracts learners who arrive with varying strengths. Second, bioinformatics is inherently interdisciplinary, being at the intersection of computer science, biology, mathematics, and data science. 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: 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. Furthermore, an important part of the process of developing this interactive text was responding to student concerns. 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. 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. As soon as learners encounter a tricky concept, we ask them to stop and think about it before transitioning.
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Instead, our MOOCs are built upon the creation of an interactive textbook applying the principles of active learning. 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). 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. 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. What distinguishes your data science specialization from the others currently available via Coursera? Instructor Phillip Compeau provided us with the following detailed feedback. ▪ Bioinformatics Capstone: Big Data in Biology ▪ Finding Mutations in DNA and Proteins (Bioinformatics VI) ▪ Genomic Data Science and Clustering (Bioinformatics V) ▪ Deciphering Molecular Evolution (Bioinformatics IV) ▪ Comparing Genes, Proteins, and Genomes (Bioinformatics III)
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▪ Finding Hidden Messages in DNA (Bioinformatics I)
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This specialization is made up of the following courses: UCSD's Bioinformatics Specialization is a first of its kind in the field, and looks like it could be of benefit not only to those coming from the world of biology to data science, but to the reverse as well. Bioinformatics is an interdisciplinary field which uses select tools and techniques from mathematics, computer science, statistics, engineering, and other fields, to analyze biological data from our perspective, we could say that bioinformatics is the intersection of data science and biology.