If you work at a university and are considering starting an undergraduate program in data science, then today’s post is for you.
If you know of any other papers, please leave a comment below.
The last links are not official academic papers, but they are quite good resources on deep learning.
In the past, the blog has included 7 Important Data Science Papers and 5 More Data Science Papers. Here is another list if you are looking for something to read over the summer.
Pedro Domingos of the Department of Computer Science and Engineering at the University of Washington provides a very useful paper with tips for machine learning. The paper is title, A Few Useful Things to Know about Machine Learning [pdf].
Below are the 12 useful tips.
- LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION
- IT’S GENERALIZATION THAT COUNTS
- DATA ALONE IS NOT ENOUGH
- OVERFITTING HAS MANY FACES
- INTUITION FAILS IN HIGH DIMENSIONS
- THEORETICAL GUARANTEES ARE NOT WHAT THEY SEEM
- FEATURE ENGINEERING IS THE KEY
- MORE DATA BEATS A CLEVERER ALGORITHM
- LEARN MANY MODELS, NOT JUST ONE
- SIMPLICITY DOES NOT IMPLY ACCURACY
- REPRESENTABLE DOES NOT IMPLY LEARNABLE
- CORRELATION DOES NOT IMPLY CAUSATION
For details and a good explanation of each, see the paper A Few Useful Things to Know about Machine Learning [pdf].
Also,later this year, Pedro Domingos will be teaching a machine learning course via Coursera. Sign up if you are interested.
Although Tobias Mayer may be known as the first data scientist, he did not coin the term data science. According to Wikipedia, the first use of the term data science was in 2001.
Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics was published in the April 2001 edition of the International Statistics Review. The author was William S. Cleveland, currently a Professor of Statistics at Purdue University.
The paper proposes a new field of study named data science. It then goes on to list and explain 6 technical focus areas for a university data science department.
- Multidisciplinary Investigations
- Models and Methods for Data
- Computing with Data
- Tool Evaluation
For the most part, the paper is still relevant. I did find a couple of good quotes from the paper that deserve comment.
The primary agents for change should be university departments themselves.
That did not happen. The driving agents for change in the data science field have been some of the newer technology/web companies such as LinkedIn, Twitter, and Facebook (none of which even existed in 2001).
…knowledge among computer scientists about how to think of and approach the analysis of data is limited, just as the knowledge of computing environments by statisticians is limited. A merger of the knowledge bases would produce a powerful force for innovation.
I think this statement still applies today. The world is just starting to realize the benefits of merging knowledge from computer science and statistics. There is much more work to do. Fortunately, businesses and universities are working to address the merger.
Have you seen the paper before? What are your thoughts on it?
Springer has just release a new data science journal named EPJ Data Science. The journal is open access which means that articles are freely available online. That catch is that people whom submit articles must pay a fee for publication. Sometimes the fee will be covered by the author’s university or company. Anyhow, if you are interested in data science research, this journal is probably worth following.
Are you interested in academic journals?
Does this excite you?