Categories statistics

28 posts

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What’s a negligible effect in an election?

One possible rule-of-thumb for researchers One of the quantitative methodology papers which has most shaped my thinking in recent years is Carlisle Rainey’s Arguing for a Negligible Effect. Like all great papers, it makes a simple point well: statistical significance is not the same as substantive significance, and to assess […]

A gentle guide to Tidy statistics in R (part 1)

While data analysis in R can seem intimidating, we will explore how to use it effectively and clearly! Introduction Jesse Maegan posed the following question on Twitter, and started a great discussion with #RStats users at all points on the R spectrum. OK #rstats Twitter, let’s talk. If you’ve tried learning […]

A gentle guide to Tidy statistics in R (part 2)

While data analysis in R can seem intimidating, we will explore how to use it effectively and clearly! Part 1 starts you on the journey of running your statistics in R code. Introduction After a great discussion started by Jesse Maegan (@kiersi) on Twitter, I decided to post a workthrough of some […]

Generalized Linear Models

It has been long time since I wrote the first machine learning for everyone article. From now on, I will try to publish articles more frequently. Quick Note: Unfortunately, Medium does not support mathematical type setting (Latex etc.), so I put mathematical formulas as images to articles and I have […]

Self Driven Data Science — Issue #34

Self Driven Data Science — Issue #34 Here’s this weeks lineup of data-driven articles, stories, and resources delivered faithfully to your inbox for you to consume. Enjoy! Data Science Use Cases In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. The entire process […]

16 Useful Advices for Aspiring Data Scientists

Why is data science sexy? It has something to do with so many new applications and entire new industries come into being from the judicious use of copious amounts of data. Examples include speech recognition, object recognition in computer vision, robots and self-driving cars, bioinformatics, neuroscience, the discovery of exoplanets […]

12 Useful Things to Know about Machine Learning

Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other […]

Using Linear Regression to Model Point Spreads in College Basketball

The surest strategy to win a college basketball game is to score more points than the other team. Or, as John Madden more artfully stated: You can’t win a game if you don’t score any points. So when it comes to modeling win likelihood, a project in my Advanced Modeling Techniques course […]

Data science you need to know — A/B testing

This is part 2 of a 5-part series of posts aiming to quickly introduce some core concepts in data science and data analysis, with a specific focus on areas that I feel are overlooked or treated briefly in other materials. This post covers A/B testing, and will suit data scientists […]

I’ve simulated the bitcoin price for the whole 2018. You won’t believe the result!

Disclaimer: This is just for fun, my opinion and not investment advice. Do your due diligence, don’t do stupid things and don’t hold more USD than you can afford to lose. Disclaimer 2: Some of you have pointed out that there is no guarantee that the future returns will be like […]