Topic: Statistics
This page features most recent and most popular posts on Statistics.
Latest posts on Statistics
- R squared Does Not Measure Predictive Capacity or Statistical Adequacy - Jul 31, 2020The fact that R-squared shouldn't be used for deciding if you have an adequate model is counter-intuitive and is rarely explained clearly. This demonstration overviews how R-squared goodness-of-fit works in regression analysis and correlations, while showing why it is not a measure of statistical adequacy, so should not suggest anything about future predictive performance.
- A Complete Guide To Survival Analysis In Python, part 3 - Jul 30, 2020Concluding this three-part series covering a step-by-step review of statistical survival analysis, we look at a detailed example implementing the Kaplan-Meier fitter based on different groups, a Log-Rank test, and Cox Regression, all with examples and shared code.
Essential Resources to Learn Bayesian Statistics - Jul 28, 2020If you are interesting in becoming better at statistics and machine learning, then some time should be invested in diving deeper into Bayesian Statistics. While the topic is more advanced, applying these fundamentals to your work will advance your understanding and success as an ML expert.- Demystifying Statistical Significance - Jul 17, 2020With more professionals from a wide range of less technical fields diving into statistical analysis and data modeling, these experimental techniques can seem daunting. To help with these hurdles, this article clarifies some misconceptions around p-values, hypothesis testing, and statistical significance.
- Before Probability Distributions - Jul 16, 2020Why do we use probability distributions, and why do they matter?
Most popular (badge-winning) recent posts on Statistics
- Essential Resources to Learn Bayesian Statistics [Silver Blog]If you are interesting in becoming better at statistics and machine learning, then some time should be invested in diving deeper into Bayesian Statistics. While the topic is more advanced, applying these fundamentals to your work will advance your understanding and success as an ML expert.
- 4 Free Math Courses to do and Level up your Data Science Skills [Silver Blog]Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.
- Overview of data distributions [Silver Blog]With so many types of data distributions to consider in data science, how do you choose the right one to model your data? This guide will overview the most important distributions you should be familiar with in your work.
- If you had to start statistics all over again, where would you start? [Gold Blog]If you are just diving into learning statistics, then where do you begin? Find insight from those who have tread in these waters before, and see what they might have done differently along their personal journeys in statistics.
- A Concise Course in Statistical Inference: The Free eBook [Silver Blog]Check out this freely available book, All of Statistics: A Concise Course in Statistical Inference, and learn the probability and statistics needed for success in data science.
- Should Data Scientists Model COVID19 and other Biological Events [Silver Blog]Biostatisticians use statistical techniques that your current everyday data scientists have probably never heard of. This is a great example where lack of domain knowledge exposes you as someone that does not know what they are doing and are merely hopping on a trend.
- 5 Statistical Traps Data Scientists Should Avoid [Gold Blog]Here are five statistical fallacies — data traps — which data scientists should be aware of and definitely avoid.
- How to Become a (Good) Data Scientist – Beginner Guide [Platinum Blog]A guide covering the things you should learn to become a data scientist, including the basics of business intelligence, statistics, programming, and machine learning.
- 6 bits of advice for Data Scientists [Silver Blog]As a data scientist, you can get lost in your daily dives into the data. Consider this advice to be certain to follow in your work for being diligent and more impactful for your organization.
- Which Data Science Skills are core and which are hot/emerging ones? [Gold Blog]We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.
- Statistical Modelling vs Machine Learning [Silver Blog]At times it may seem Machine Learning can be done these days without a sound statistical background but those people are not really understanding the different nuances. Code written to make it easier does not negate the need for an in-depth understanding of the problem.
- 5 Useful Statistics Data Scientists Need to Know [Gold Blog]A data scientist should know how to effectively use statistics to gain insights from data. Here are five useful and practical statistical concepts that every data scientist must know.
- Top 10 Statistics Mistakes Made by Data Scientists [Silver Blog]The following are some of the most common statistics mistakes made by data scientists. Check this list often to make sure you are not making any of these while applying statistics to data science.
- How to correctly select a sample from a huge dataset in machine learning [Silver Blog]We explain how choosing a small, representative dataset from a large population can improve model training reliability.
- The Essential Data Science Venn Diagram [Gold Blog]A deeper examination of the interdisciplinary interplay involved in data science, focusing on automation, validity and intuition.
- Introduction to Statistics for Data Science [Gold Blog]This tutorial helps explain the central limit theorem, covering populations and samples, sampling distribution, intuition, and contains a useful video so you can continue your learning.
- The 5 Basic Statistics Concepts Data Scientists Need to Know [Silver Blog]Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!
- Machine Learning Cheat Sheets [Platinum Blog]Check out this collection of machine learning concept cheat sheets based on Stanord CS 229 material, including supervised and unsupervised learning, neural networks, tips & tricks, probability & stats, and algebra & calculus.
- Essential Math for Data Science: ‘Why’ and ‘How’ [Platinum Blog]It always pays to know the machinery under the hood (even at a high level) than being just the guy behind the wheel with no knowledge about the car.
- Basic Statistics in Python: Descriptive Statistics [Gold Blog]This article covers defining statistics, descriptive statistics, measures of central tendency, and measures of spread. This article assumes no prior knowledge of statistics, but does require at least a general knowledge of Python.
- Causation in a Nutshell [Gold Blog]Every move we make, every breath we take, and every heartbeat is an effect that is caused. Even apparent randomness may just be something we cannot explain.
