Monthly Archives: September 2012
Who is taking this class?
We have students from the following departments: Business/Marketing , Statistics, Earth and Environmental Engineering, Biomedical Informatics, Industrial Engineering/Operations Research, Actuarial Science, Quantitative Methods in the Social Sciences, Psychology, Economics, Physics, Politics, Mechanical Engineering, Sociology, History, Journalism (Some departments have more than others. If I missed anyone, sorry! You’re welcome too.) We have students who are Undergrads, […]
Tonight’s Lab Schedule with Jared
Jared will hold two Lab Sections tonight. There is no difference between them in content. Just come to whichever one fits your schedule: 6:10-7:25 503 Hamilton 7:40-8:55 503 Hamilton Bring a laptop to follow along with him or take notes on paper. Either way. What’s the difference between the Monday night labs and the problem […]
How am I supposed to get experience if I need experience to get experience?
I pulled this excerpt out of my post about defining the scope of the course, because I think maybe I buried the lead: There are a shortage of people who can do Data Science well. When I talk to people in positions recruiting, they don’t want recent grads because the recent grads don’t have enough […]
Big Data in My Blood
Dear Students, Check out this story in this week’s NYT Big Data in Your Blood I want to use it to explore a couple things and ideas I was struggling with before the class started this semester, and that I wasn’t sure how to communicate with you about on our first day together: Semantics again […]
Computational Skills Boot Camp
For students registered for the course, the computational skills boot camp will be held Friday, September 28 - Saturday, September 29. It is optional but encouraged. You can come for one or both of the days. The schedule will be something like: Friday a.m.: introduction to Python (which is really about how to design and […]
Data Scientist Profiles
An example of a data scientist profile of one of the students in our class
What were you thinking when you made us do those data scientist profiles?
I had four primary reasons for going through that exercise:
Reason 1: Cultivating self-awareness
Reason 2: Illustrate importance of standardization in visualization
I wanted to demonstrate standardizing visualizations of individuals as a mix of characteristics. (You should think about how you might do it, and then also ask yourself whether you think a standardized visualization has any value.) In this particular case
(a) standardizing the x-axis: I used the main buckets that I thought were approximately some of the skills one needs as a data scientist. I’m not tied to these
Questions from Students about the Nature of the Class
I received the following questions from students (1) How is this class different from the Machine Learning and Data Mining class? Good question. I can understand why you ask this given the appearance of many of the same books on the syllabus. So let me give you some insight into how I developed this course. […]
