This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
by Peter Bruce, President & Founder, Statistics.com Nearly everyone is now familiar with the IHME Covid-19 forecasts (also called the “Murray” model after the lead… Read More »Covid-19: Epidemiological Models vs. Statistical Models
by Mike Thurber, Principal Scientist, Elder Research The COVID-19 pandemic has created a need for clear and actionable analytics like never before. The world can’t… Read More »Is the Spread of the COVID-19 Coronavirus Being Slowed?
Process mining is changing the way enterprises operate and manage their processes. It tells the process ‘as-is’ and ensures that enterprises and process owners have… Read More »Evolution Of Process Mining: A Look At The History
Randomised algorithms are built on statistical features played by random numbers. Quicksort is a good example to illustrate this algorithm. For instance, in a class of taller… Read More »Randomised algorithms
There are a few key differences between the Binomial, Poisson and Hypergeometric Distributions. These distributions are used in data science anywhere there are dichotomous variables (like yes/no, pass/fail). This… Read More »Difference between Binomial, Poisson and Hypergeometric Distribution in One Picture
One of the best-known books on statistics is now free Larry Wasserman’s All of Statistics is free to download from Springer I like this book… Read More »Free book on Statistics: Larry Wasserman’s All of Statistics
Data mining includes statistics and elements of statistical analysis. Some people describe the two as interconnected, others as them being on a continuum. This one picture shows… Read More »Data Mining vs Statistics in One Picture
Divide-and-Conquer Algorithm Divide-and-conquer algorithms are made up of three steps: divide, conquer, and combine. In the divide stage, we divide the data into smaller, more manageable fragments. In… Read More »Divide and Conquer Algorithm
Marketing, at least in the IT sector, has been replaced by memes. Every so often a Gartner slide deck goes viral, and the next thing… Read More »Why Data Is Not The Next Oil
This blog post talks about the concepts of data structures and algorithms and introduces the why of integrating them. A few topics in data engineering… Read More »Data Structures & Algorithms
