"Please suggest a link for budding data scientists"
AnalyticBridge
A Data Science Central Community
Takashi J. OZAKI's Page
Latest Activity
Prasanth NSR liked Takashi J. OZAKI's blog post 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know
DoonePlace liked Takashi J. OZAKI's blog post 12 Statistical and Machine Learning Methods that Every Data Scientist Should KnowPriya commented on Takashi J. OZAKI's blog post 12 Statistical and Machine Learning Methods that Every Data Scientist Should KnowEljay Canoy commented on Takashi J. OZAKI's blog post In Japan, "Artificial Intelligence" comes to be a super star while "Data Scientist" is fading away"I added "Deep Learning" and "Machine Learning" in the mix and found that Deep Learning has gained most popularity, followed by Machine Learning and then Artificial Intelligence. It's also interesting to see people's…"
Takashi J. OZAKI posted a blog postIn Japan, "Artificial Intelligence" comes to be a super star while "Data Scientist" is fading away
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…See More
Takashi J. OZAKI's blog post was featured12 Statistical and Machine Learning Methods that Every Data Scientist Should Know
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 ForestXgboost (eXtreme Gradient Boosted Trees)Deep LearningBayesian Modeling with MCMCword2vecK-means ClusteringGraph Theory & Network Analysis(A1) Latent Dirichlet Allocation & Topic Modeling(A2)…See More
Takashi J. OZAKI posted a blog post12 Statistical and Machine Learning Methods that Every Data Scientist Should Know
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 ForestXgboost (eXtreme Gradient Boosted Trees)Deep LearningBayesian Modeling with MCMCword2vecK-means ClusteringGraph Theory & Network Analysis(A1) Latent Dirichlet Allocation & Topic Modeling(A2)…See More
Takashi J. OZAKI's blog post was featuredOverview and simple trial of Convolutional Neural Network with MXnet
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/mxnetI…See More
Takashi J. OZAKI's blog post was featuredMultivariate modeling vs. univariate modeling along human intuition: predicting taste of wine
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, density or others that are given…See More
Takashi J. OZAKI's blog post was featuredR and Stan: introduction to Bayesian modeling
I wrote a series of blog posts on Bayesian modeling with R and Stan.Bayesian modeling with R and Stan (1): OverviewBayesian modeling with R and Stan (2): Installation and an easy exampleBayesian modeling…See More
Takashi J. OZAKI's blog post was featuredEven without any "golden feature", multivariate modeling can work
A/B testing is widely used for online marketing, management of Internet ads or any other usual analytics. In general, people use it in order to look for "golden features (metrics)" that are vital points for growth hacking. To validate A/B testing, statistical hypothesis tests such as t-test are used and people are trying to find any metric with a significant effect across conditions. If you successfully find a metric with a significant difference between design A and B of a click button, you'll…See More
Takashi J. OZAKI posted a blog postOverfitting or generalized? Comparison of ML classifiers - a series of articles
In my own blog I wrote a series of articles about how major machine learning classifiers work, with some visualization of their decision boundaries on various datasets.Machine learning for package users with R (0): PrologueMachine learning for package users with R (1): Decision Tree…See More
Takashi J. OZAKI's blog post was featuredOverfitting or generalized? Comparison of ML classifiers - a series of articles
In my own blog I wrote a series of articles about how major machine learning classifiers work, with some visualization of their decision boundaries on various datasets.Machine learning for package users with R (0): PrologueMachine learning for package users with R (1): Decision Tree…See More
Takashi J. OZAKI's blog post was featuredDecision tree vs. linearly separable or non-separable pattern
As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns.1. Simple (non-overlapped) XOR patternIt worked well. Its decision boundary was drawn almost perfectly parallel to the assumed true…See More
Takashi J. OZAKI posted a blog postDecision tree vs. linearly separable or non-separable pattern
As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns.1. Simple (non-overlapped) XOR patternIt worked well. Its decision boundary was drawn almost perfectly parallel to the assumed true…See More
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,…
ContinueComment Wall (1 comment)
- At 8:16am on May 16, 2013, Takashi J. OZAKI said…
Hi Paul (id: paulagyiri),
I'd be happy if you contact me via LinkedIn (http://jp.linkedin.com/in/tjozaki).
Thanks,
-TJO
Follow Us
On Data Science Central
On DataViz
On Hadoop
© 2018 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC
Powered by
Badges | Report an Issue | Privacy Policy | Terms of Service
