Tag Archives: academic

Data Science Papers – Summer 2019 edition

Looking for a few academic data science papers to study? Here are a few I have found interesting. The are not all from the past 12 months, but I am including them anyhow.

Help for Academic Programs in Data Science

Brandon Rohrer (along with others) created an excellent resource for academic programs, Industry recommendations for academic data science programs. The resource is authored by a number of industry data scientists and university faculty. It is collection of useful information for college data science programs. Here are some of the topics:

Plus, the site is growing, and new information is frequently being added. If your college/university is launching a data science program, this resource is a must read.

Papers for Teaching Undergraduate Data Science

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.

Deep Learning Research Paper Lists for Summer 2017

The last links are not official academic papers, but they are quite good resources on deep learning.

5 Data Science Research Papers to read in Summer 2017

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.

12 Useful Tips for Machine Learning

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.

  1. LEARNING = REPRESENTATION + EVALUATION + OPTIMIZATION
  2. IT’S GENERALIZATION THAT COUNTS
  3. DATA ALONE IS NOT ENOUGH
  4. OVERFITTING HAS MANY FACES
  5. INTUITION FAILS IN HIGH DIMENSIONS
  6. THEORETICAL GUARANTEES ARE NOT WHAT THEY SEEM
  7. FEATURE ENGINEERING IS THE KEY
  8. MORE DATA BEATS A CLEVERER ALGORITHM
  9. LEARN MANY MODELS, NOT JUST ONE
  10. SIMPLICITY DOES NOT IMPLY ACCURACY
  11. REPRESENTABLE DOES NOT IMPLY LEARNABLE
  12. 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.