Le Monde puzzle [#1075]
A Le Monde mathematical puzzle from after the competition:
R > Python： a Concrete Example
I like both Python and R, and teach them both, but for data science R is the clear choice. When asked why, I always note (a) written by statisticians for statisticians, (b) built-in matrix type and matrix manipulations, (c) great graphics, both base and CRAN, (d) excellent parallelization facilities, etc. I also like to say that R is “more CS-ish than Python,� just to provoke my fellow computer scientists. 
Top KDnuggets tweets, Nov 14-20： 10 Free Must-See Courses for Machine Learning and Data Science; Great list of
WPI： Post-Doctoral Fellow [Worcester, MA]
At: WPILocation: Worcester, MA
Web: www.wpi.eduPosition: Post-Doctoral Fellow
Document worth reading： “The Algorithm Selection Competition Series 2015-17”
The algorithm selection problem is to choose the most suitable algorithm for solving a given problem instance and thus, it leverages the complementarity between different approaches that is present in many areas of AI. We report on the state of the art in algorithm selection, as defined by the Algorithm Selection Competition series 2015 to 2017. The results of these competitions show how the state of the art improved over the years. Although performance in some cases is very promising, there is still room for improvement in other cases. Finally, we provide insights into why some scenarios are hard, and pose challenges to the community on how to advance the current state of the art. The Algorithm Selection Competition Series 2015-17
A Bayesian take on ballot order effects
Dale Lehman sends along a paper, “The ballot order effect is huge: Evidence from Texas,” by Darren Grant, which begins:
Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices
By Derrick Mwiti, Data Analyst
Driving Success through Business Insight, One Customer at a Time
Dataiku Customer Success Managers are experienced liaisons between business leaders and data teams. Melissa Capece, Customer Success Manager, says that “everyone has a mixed background somewhere between technology and business.” There’s a diverse group of skills on the team, which are leveraged to suit each client’s needs. Ulysses David, Customer Success Manager, adds how this diversity is a strength for the team and our customers,“We have people from various backgrounds that moved into it, from business, tech, sales, and consulting. It’s like being a data scientist, it requires a little bit of everything.”
R Packages worth a look
Piece-Wise Exponential Additive Mixed Modeling Tools (pammtools)Functions that facilitate fitting piece-wise exponential (additive mixed) models (Bender and Scheipl (2018) <doi: 10.1177/1471082X17748083>). Thi …