Monday, May 8, 2017

Readings in Data Science models

Ex-libris


As I've previously mentioned, I recently started a 6 part series on LinkedIn called "ex-libris" of a Data Scientist. 

Part I was on "data and databases": "ex-libris" of a Data Scientist - Part i

I just posted part II, "models": "ex-libris" of a Data Scientist - Part II

It does cover machine learning, but before going there, I cover Metrics, Operations Research, Econometrics and Time Series and Statistics. Even more fundamentally, I start with Math.

Yes, if you slept through linear algebra or calculus (or analysis as it is called in certain parts of the world), check the list out. Book suggestions and links to videos and other resources.


Also, a reminder: Python specific books will show up in part IV.



Francois Dion
@f_dion

Monday, April 24, 2017

Meet Eliza #AI



I will be presenting and directing a discussion on artificial intelligence, from various angles including the arts, Tuesday April 25th at Wake Forest in Winston Salem, NC.

Details here:
http://www.pyptug.org/2017/04/pyptug-monthly-meeting-meet-eliza-april.html

Francois Dion
@f_dion

Thursday, April 13, 2017

Readings in data and databases

Recent readings (can you guess/decipher some of them?)

I've been fairly quiet on this particular blog this year. Beside a lot of data science work, I've done presentations at meetups and conferences, including a recent tutorial on "Getting to know your data at scale" at the IEEE SouthEastCon 2017. Notebooks will be posted on github soon.

But, in the meantime...

Ex-libris

Something else I've been doing is publishing a few articles here and there. Just recently, I started a 6 part series on LinkedIn called "ex-libris" of a Data Scientist. I think many readers of this blog will appreciate this series, and particularly this first installment on "data and databases":

"ex-libris" of a Data Scientist - Part i

It covers a good variety of books on the subject, some pretty much must read for whatever corner of the computer science world you live in. Also of interest will be the Postgres, Hadoop and graph database pointers and a list of over 20 curated must read papers in the field.

Python specific books will show up in part IV.


Francois Dion
@f_dion