New featured content for data scientists:

Data Science in Python: Pandas Cheat Sheet -- This cheat sheet, along with explanations, was first published on DataCamp. Click on the picture to zoom in. To view other cheat sheets (Python, R, Machine Learning, Probability, Visualizations, Deel Learning, Data Science, and so on) click here. To read the article,…

The main focus of this article is on computing the point that minimizes the sum of the "distances" to n points in a d-dimensional space, called centroid or center, in the presence of outliers. 

This long article has several sections.

Content

1. A related physics problem

2. Algorithm to find the centroid

• Source code to generate points and compute centroid, using Monte…

Here is our updated selection of featured articles and resources posted over the weekend:

• Building a Deep Learning Model for Process Optimisation 
• Improving Prediction of Office Room Occupancy Through Random Sampling…

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…

This article, written by Kass RE, Caffo BS, Davidian M, Meng X-L, Yu B, and Reid N, contains the following rules:

• Statistical Methods Should Enable Data to Answer Scientific Questions
• Signals Always Come with Noise
• Plan Ahead, Really Ahead
• Worry about Data Quality
• Statistical Analysis Is More Than a Set of Computations
• Keep it Simple
• Provide Assessments of Variability
• Check Your Assumptions
• When Possible,…

This post is the fourth part of the multi-part series on how to build a search engine –

• How to build a search engine – Part 1: Installing the tools and getting the technology stack ready
• How to build a search engine – Part 2: Configuring elasticsearch
• How to build a search engine –…

Randomness is all around us. Its existence sends fear into the hearts of predictive analytics specialists everywhere -- if a process is truly random, then it is not predictable, in the analytic sense of that term.  Randomness refers to the absence of patterns, order, coherence, and predictability in a system. 

Unfortunately, we…

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…