A Data Science Central Community

In this data science article, emphasis is placed on *science*, not just on data. State-of-the art material is presented in simple English, from multiple perspectives: applications, theoretical research asking more questions than it answers, scientific computing, machine learning, and algorithms. I attempt here to lay the foundations of a new statistical technology, hoping that it will plant the seeds for further research on a topic with a broad range of potential…

Added by Vincent Granville on February 23, 2019 at 11:00am — No Comments

Many of the following statistical tests are rarely discussed in textbooks or in college classes, much less in data camps. Yet they help answer a lot of different and interesting questions. I used most of them without even computing the underlying distribution under the null hypothesis, but instead, using simulations to check whether my assumptions were plausible or not. In short, my approach to statistical testing is is model-free, data-driven. Some are easy to implement even in Excel. Some…

ContinueAdded by Vincent Granville on February 13, 2019 at 7:00pm — No Comments

For background to this post, please see Learn Machine Learning Coding Basics in a weekend. Here,we present the glossary that we use for the coding and the mindmap attached to these classes and upcoming book. About 80 terms are included in the glossary, covering Ensembles, Regression, Classification,…

ContinueAdded by Vincent Granville on February 12, 2019 at 12:31pm — No Comments

**Logistic regression (LR)** models estimate the probability of a binary response, based on one or more predictor variables. Unlike linear regression models, the dependent variables are categorical. LR has become very popular, perhaps because of the wide availability of the procedure in software. Although LR is a good choice for many situations, it doesn't work well for *all* situations. For example:

- In propensity score…

Added by Vincent Granville on February 7, 2019 at 3:23pm — No Comments

This is another interesting problem, off-the-beaten-path. It ends up with a formula to compute the integral of a function, based on its derivatives solely.

For simplicity, I'll start with some notations used in the context of matrix theory, familiar to everyone: T(*f*) = *g*, where *f* and *g* are vectors, and T a square matrix. The notation T(*f*) represents the product between the matrix T, and the vector *f*. Now, imagine that the…

Added by Vincent Granville on February 3, 2019 at 5:30pm — 1 Comment

- TensorFlow 1.x vs 2.x. – summary of changes
- The Next Big Thing in AI/ML is…
- How exactly do you determine causation?
- Rule of thumb: Which AI / ML algorithms to apply
- Statistics for Data Science in One Picture
- On Being a 50 Year Old Data Scientist
- Variance, Attractors and Behavior of Chaotic Statistical Systems

- Bayesian statistics (1)
- analytics (1)
- churn (1)
- crowd sourcing (1)
- data mining (1)
- email campaigns (1)
- fico (1)
- graph (1)
- lifetime value (1)
- rosacea (1)
- statistical litigation (1)
- user retention (1)

**2020**

- January (2)

**2019**

- December (4)
- November (5)
- October (4)
- September (2)
- August (5)
- July (2)
- June (2)
- May (4)
- April (3)
- March (3)
- February (5)
- January (2)

**2018**

- December (2)
- November (1)
- September (5)
- August (10)
- July (3)
- June (7)
- May (11)
- April (8)
- March (9)
- February (9)
- January (11)

**2017**

- December (6)
- November (8)
- October (9)
- September (5)
- August (8)
- July (3)
- June (6)
- May (4)
- April (10)
- March (4)
- February (6)
- January (5)

**2016**

**2015**

**2014**

**2013**

- December (6)
- November (6)
- October (4)
- September (4)
- August (7)
- July (8)
- June (4)
- May (8)
- April (9)
- March (11)
- February (9)
- January (6)

**2012**

- December (2)
- November (12)
- October (17)
- September (10)
- August (15)
- July (13)
- June (12)
- May (10)
- April (8)
- March (20)
- February (19)
- January (11)

**2011**

- December (19)
- November (15)
- October (11)
- September (16)
- August (7)
- July (4)
- June (8)
- May (11)
- April (9)
- March (6)
- February (7)
- January (7)

**2010**

- December (9)
- November (12)
- October (14)
- September (16)
- August (6)
- July (6)
- June (1)
- May (4)
- April (4)
- March (3)
- February (5)
- January (10)

**2009**

- December (11)
- November (9)
- October (6)
- September (1)
- July (1)
- June (1)
- May (2)
- April (1)
- March (1)
- February (2)
- January (2)

**2008**

© 2020 AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions