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Takashi J. OZAKI's Page
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Profile Information
- Short Bio:
- Ph. D. Data Scientist
*Expertise*
Data Science: mainly for digital marketing
Statistics: Multivariate modeling, including Bayesian modeling
Machine Learning: including Deep Learning
*Proficiency*
Programming language: R, Stan, Python, SQL, C, Matlab
Natural language: Japanese, English (business level)
*CV*
Jan 2016 - present: Data Scientist, Google
Jul 2013 - Dec 2015: Data Scientist, Recruit Communications Co., Ltd.
Jun 2012 - Jun 2013: Data Scientist, CyberAgent, Inc.
Apr 2006 - May 2012: Academic Researcher in Cognitive Neuroscience, in some universities or national research institutes
Mar 2006: Ph. D. in Frontier Sciences, The University of Tokyo
- My Website or LinkedIn Profile (URL):
- http://tjo-en.hatenablog.com/
- Field of Expertise:
- Business Analytics, Predictive Modeling, Data Mining, Econometrics, Web Analytics, Statistical Consulting, Artificial Intelligence
- Years of Experience in Analytical Role:
- 4 yr
- Professional Status:
- Technical
- Interests:
- Networking
- What is your Favorite Data Mining or Analytical Website?
- http://www.kdnuggets.com/
- Your Company:
- Industry:
- Technology
- Your Job Title:
- Data Scientist
- How did you find out about AnalyticBridge?
- I saw an article by Dr. Vincent Granville via Google search.
Takashi J. OZAKI's Blog
In Japan, "Artificial Intelligence" comes to be a super star while "Data Scientist" is fading away
Posted on January 13, 2017 at 6:30am 1 Comment 0 Likes
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…
Continue 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know
Posted on January 8, 2017 at 6:30am 1 Comment 2 Likes
Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.
- Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)
- Multiple Regression (Linear Models)
- General Linear Models (GLM: Logistic Regression, Poisson Regression)
- Random Forest
- Xgboost (eXtreme Gradient Boosted Trees)
- Deep Learning
- Bayesian Modeling with…
Overview and simple trial of Convolutional Neural Network with MXnet
Posted on March 30, 2016 at 8:30am 0 Comments 0 Likes
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…
Continue Multivariate modeling vs. univariate modeling along human intuition: predicting taste of wine
Posted on November 26, 2015 at 8:43am 0 Comments 0 Likes
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,…
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Hi Paul (id: paulagyiri),
I'd be happy if you contact me via LinkedIn (http://jp.linkedin.com/in/tjozaki).
Thanks,
-TJO