I recently read, Big Data Education: 3 Steps Universities must take
Here are the 3 steps listed:
- Data Science cannot be an undergraduate degree
- A graduate degree should contain math, stats and computer science
Step 2 seems obvious. Math, stats, and computer science are some of the key areas for data science. I would add communication and presentation skills to the list because people with just math, stats, and CS skills are not known to be naturally good communicators. I agree with step 3. More research needs to be done, but most of the research will need to be interdisiplinary. Universities need to put more effort into interdisiplinary research.
Step 1 confused me a bit. The argument was data science has too many necessary skills and an applied focus area. Of course a person cannot learn everything about data science in an undergraduate degree. Earning a computer science degree does not mean you will know everything about computer science. It just means you know the fundamentals about algorithms, architecture, and operating systems. You know enough about computer science to understand the field and learn more as you go. I think 4 years should be enough time to do the same for data science.
What are your thoughts?
Recently, a great comment was posted on the list of Colleges With Data Science Degrees. Currently, many of the data science related college programs are being built in the Business Department. While it is great that colleges are starting to build data science programs, data science is so much bigger than just business. This was a nice reminder that data science is used in many fields.
I thought the comment by Bernice Rogowitz said it very well. Here is a copy of the comment:
It’s not all about business!
Some of the posts align data science and analytics with business applications. It’s important to keep in mind that scientists and researchers of all stripes are using statistical approaches, optimization, clustering, outlier detection and data cleansing methods, etc., not just analysts in the business world. In fact, some of the most sophisticated models come from outside of business. And, don’t forget the importance of analytics for finding features in non-structured data, such as images, text, 3-D models and simulations, etc. The large scope for data science and analytics was recently explored in a workshop: http://www.radcliffe.harvard.edu/exploratory-seminars/new-multidisciplinary-approach-data-understanding
Once again, California Institute of Technology will be broadcasting Learning From Data online. This is an introductory course on machine learning. All of the videos are broadcast live, and online homework and discussion forums are available. I have not participated in the course, so I would love some comments about how this class compares with the Coursera machine learning course.
Just Announced, Coursera adds 17 new universities. Those universities include Columbia and Brown, as well as a few international universities.
A few notable courses for data science are: a new machine learning course from the University of Washington, Linear Algebra from Brown, and Natural Language Processing by Michael Collins from Columbia.
See the following pages to seed what other courses are now available.
I just recently (yesterday) found out that Columbia University is offering a Data Science course. Dr. Rachel Schutt of the Department of Statistics is teaching the course. She is also blogging some of the course material. Sorry, I could not find any video lectures. However, Cathy O’Neil is sitting in on the course and will be blogging some of the material. You can see more at Cathy’s popular blog titled mathbabe.
Coursera has so many courses, it is difficult to keep track. New ones are starting all the time. Here are 2 more that will be beneficial to people interested in learning more about data science.
- Statistics One – Technically, it started yesterday, but you will not miss out on much if you start today. If you are lacking some skills in statistics, this is probably a great place to start.
- Intro to Computational Finance and Financial Econometrics – If you are interested in data science and finance, or if you want to know if you are interested in data science and finance, it is worth checking out this course.
Happy Studying Again!
This infographic is somewhat related to the previous infographic I posted. The first paragraph under point #1 is worth noting. Schools now have the ability to collect massive amounts of data; they just need to analyze it to acquire useful information.
Presented By: OnlineDegrees.org