I published a post about the current status of "Data Scientist" in Japan, as a periodic follow-up analysis since two years ago. Its trend still remains, but it's beyond my anticipation at that time.

Indeed growing trend of "Artificial Intelligence" in Japan is steeper than that in English, and "Data Scientist" is now getting to be…

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.

1. Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)
2. Multiple Regression (Linear Models)
3. General Linear Models (GLM: Logistic Regression, Poisson Regression)
4. Random Forest
5. Xgboost (eXtreme Gradient Boosted Trees)
6. Deep Learning
7. Bayesian Modeling with…

Actually I've known about MXnet for weeks as one of the most popular library / packages in Kaggler, but just recently I heard bug fix has been almost done and some friends say the latest version looks stable, so at last I installed it.

MXnet: https://github.com/dmlc/mxnet

I think that the most important feature…

I wrote a blog post inspired by Jamie Goode's book "Wine Science: The Application of Science in Winemaking".

In this book, Goode argued that reductionistic approach cannot explain relationship between chemical ingredients and taste of wine. Indeed, we know not all high (alcohol) wines are excellent, although in general high wines are believed to be good. Usually taste of wine is affected by a complicated balance of many components such as sweetness, acid, tannin,…