AcademicWe are a faculty team aiming to bring Applied Data Science with Python skills to everyone through a Coursera specialization this fall AMA!
Aug 24th 2016 by UMichiganAI • 13 Questions • 106 Points
My short bio: There’s a big team behind this University of Michigan Coursera specialization and we want to share with you what we’re doing to bring applied data science and python skills to everyone! From pedagogy and technology through to curricular design and content please feel free to ask us anything! Want to know why we think python is great for data science? Or what it takes to put a MOOC together?
- Christopher Brooks is faculty in the University of Michigan School of Information, and does research in learning analytics and educational technologies, such as predictive models of student success.
- Kevyn Collins-Thompson is faculty in the University of Michigan School of Information and does research in information retrieval and text analysis.
- Daniel Romero is faculty in the University of Michigan School of Information and does research in networks and complex systems.
- V.G.Vinod Vydiswaran is faculty in the University of Michigan Medical School and the School of Information and does research in text mining and natural language processing, such as mining health information from patient records and social media. In addition to the faculty, we are joined by our coordinators * Stephanie Haley and course tutorial assistant Filip Jankovic!
Here’s the course we are planning to teach: coursera.org/specializations/data-science-python
My Proof: http://i.imgur.com/DXaA0F2.jpg
Why did you pick python as the language you're using to teach with?
There are a couple of reasons. First Python is wonderful specifically for data science - lots of great libraries for machine learning (scikit-learn), natural language processing (nltk), network analysis (networkx) and basic visualizations (matplotlib). The data analysis and cleaning ability of python is great - I (Chris) am regularly writing up pandas manipulations to clean and transform research data.
Python also is a comprehensive programming language, so if you're a software developer you've got a full toolkit including multiprocessing and cloud computing libraries and not just a specialized stats language.
But we also took a look at what exists out there for free educational data science material - there are lots of great resources in R, but I think the python world was a little underrepresented, so we figured we would share our workflows (though I think all of us use a variety of tools when solving data science problems!).
What value does your specialization offer the job seeker? I'm curious if you had that demographic in mind while designing the course.
EDIT: for example, some specializations have industry partnerships, or large / capstone projects to put on your CV.
All here: We are very interested in this demographic, and talked about how to support these learners at some length in course planning. This course is more introductory, so it depends on the kind of job you are seeking, and what other background (current employment, previous academic background, etc.) you might have. For instance, if you're a programmer who is looking to shift positions away from (say) front end development to business intelligence, we hope this specialization is for you. That's of course just one example of a job seeker!
We also hope to support students who are thinking of going into graduate school, and want some solid skills to put on their application process.
And, while we don't have an omnibus capstone, instead each of the courses ends in a larger project assignment. My experience in talking with learners who had done data science MOOCs was, even if they paid for the specialization, they tended not to do the separate capstone project. So we wanted to try larger projects on a per course basis to see if this would help create a compelling portfolio for learners!
In the end, I think the best bet for a job seeker is to differentiate themselves by applying their skills to a novel project that is wholly their own!
Would completing programming for everybody specialization (which I currently take) provide enough knowledge for me to take this specialization or should I wait to finish first year of my (in CS) University?
Chris here: Yes, I think it would provide enough of a background, especially if you are planning to go into a technology field and consider yourself to be a keen. Some of the later courses get more intense and be more challenge as they require some basic statistics knowledge, but I think this is generally achievable by any CS student in either the late part of their first or second year of undergraduate study. I think this specialization would help you experience techniques that you might not normally get to experience until you are a senior undergraduate.
Currently Coursera shows that the first course runs from 9/26 to 10/9, so that is 2 weeks? There are a lot of topics in the syllabus - will they all be covered in just 2 weeks? Also, how long are the other 4 courses of the specialization?
Hi: No, each of the courses runs for four weeks, not sure why it is showing up as only two weeks on Coursera!