Here’s an interesting talk by Eunice Jun on a language she’s working on.


From the video description:

Current statistical tools place the burden of valid, reproducible statistical analyses on the user. Users must have deep knowledge of statistics to not only identify their research questions, hypotheses, and domain assumptions but also select valid statistical tests for their hypotheses. As quantitative data become increasingly available in all disciplines, data analysis will continue to become a common task for people who may not have statistical expertise. Tea, a high-level declarative language for automating statistical test selection and execution, abstracts the details of analyses from users, empowering them to perform valid analyses by expressing their goals and domain knowledge. In this talk, I will discuss the design and implementation of Tea, lessons learned through the process, and other ongoing work in this vein.

Worried about a shark attack when you go to the beach? Then you need to watch this video.

From causation and correlation, to relative and absolute risk, Jennifer Rogers explains how to figure out if the stats we are presented in newspapers are accurate.

Jennifer Rogers holds the position of Director of Statistical Consultancy Services at the University of Oxford having previously worked as a Post-Doctoral Research Fellow in the Department of Statistics funded by the National Institute of Health Research. She has a special interest in the development and application of novel statistical methodologies, particularly in medicine. Her main area of expertise is the analysis of recurrent events and her research has recently focused on developing and implementing appropriate methodology for the analysis of repeat hospitalisations in patients with heart failure but her research has many other applications in medicine such as epilepsy and cancer, but also in retail and engineering. She works alongside other statisticians, clinicians, computer scientists, industry experts and regulators.

Learn the essentials of statistics in this complete (and free!) course from

This course introduces the various methods used to collect, organize, summarize, interpret and reach conclusions about data. An emphasis is placed on demonstrating that statistics is more than mathematical calculations. By using examples gathered from real life, students learn to use statistical methods as analytical tools to develop generalizations and meaningful conclusions in their field of study.

Bloomberg takes a look at the unique role of data science in professional basketball.

From the description:

With her PhD in math, Ivana Seric had expected to wind up with a career in academia—but thanks to the growing use of statistical analysis in the NBA, she took a job with the Philadelphia 76ers instead. As a data scientist, she helps the team’s coaches devise smarter strategies to win.

Here’s a great exploration of Bayes’ Theorem and how to use it in real world problems.

Bayes’ theorem is a way to figure out conditional probability. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events. For example, your probability of getting a parking space is connected to the time of day you park, where you park, and what conventions are going on at any time. Bayes’ theorem is slightly more nuanced. In a nutshell, it gives you the actual probability of an event given information about tests.

Gradient Descent is the workhorse behind much of Machine Learning. When you fit a machine learning method to a training dataset, you’re almost certainly using Gradient Descent.

The process can optimize parameters in a wide variety of settings. Since it’s so fundamental to Machine Learning, Josh Starmer of StatQuest decided to make a “step-by-step” video that shows exactly how it works.

Heads up: there is some singing.