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
via Four data themes to watch from Strata + Hadoop World 2012 – Strata.
- In-memory data storage
- SQL and SQL-like tools matter
- The 80% rule for data preparation
- Asking the right question
A few weeks back one of my children flushed an unknown item (toy or something) down one of the toilets. Well, it caused some problems, and the toilet was not working properly. So, I did what any reasonable person in 2012 would do. I googled for plumbers in my area and called one of them. I happened to call Mr. Rooter Plumbing Service. They came the next day and fixed the toilet.
Great Ryan, What does this have to do with data science?
Well, the following week, I started seeing this add.
I no longer needed a plumber! The process that determines what adds I see is definitely a data product. The problem is the solution is too slow. By the time the add company has figured out what I need, I no longer need it.
This just reminded me of why data science needs to focus strongly on real-time or near real-time solutions. Our world moves quickly and the data products need to respond accordingly. Are you aware of some good work being done for real-time data science? I know it is out there.
In the end, I would recommend Mr. Rooter, but I would wait for the coupon first.
Yesterday, Coursera announced that students will soon be able to earn college credits for some of the courses. See the blog post with the college credit announcement.
In case you missed my slides I posted this weekend, the end of the presentation mentions a new project I am starting to create. It is called Data Science 201. The goal is simple:
To be the best place on the web for learning about all topics in data science
Blogging is great, but there is more data science resources on the internet than I could ever read/evaluate and blog about. That is why I am creating Data Science 201.
Don’t worry, I will keep this blog going.
These are some slides from a talk I gave at South Dakota Code Camp. I also could have titled the presentation, “How to Learn Data Science.”
Also, the slides contain lots of links to other resources. Enjoy!
Strata Conference/Hadoop World NYC 2012 videos are now available on Youtube. All the videos are not there, but most of the keynotes and some extra interviews are available.