# Topic: Statistics

This page features most recent and most popular posts on Statistics.

### Latest posts on Statistics

- 10 Principles of Practical Statistical Reasoning - Nov 3, 2020Practical Statistical Reasoning is a term that covers the nature and objective of applied statistics/data science, principles common to all applications, and practical steps/questions for better conclusions. The following principles have helped me become more efficient with my analyses and clearer in my conclusions.
- The Best Free Data Science eBooks: 2020 Update - Sep 30, 2020The author has updated their list of best free data science books for 2020. Read on to see what books you should grab.
- Causal Inference: The Free eBook - Sep 25, 2020Here's another free eBook for those looking to up their skills. If you are seeking a resource that exhaustively treats the topic of causal inference, this book has you covered.
- What is Simpson’s Paradox and How to Automatically Detect it - Sep 18, 2020Looking at data one way can tell one story, but sometimes looking at it another way will tell the opposite story. Understanding this paradox and why it happens is essential, and new tools are available to help automatically detect this tricky issue in your datasets.
- Statistics with Julia: The Free eBook - Sep 14, 2020This free eBook is a draft copy of the upcoming Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Interested in learning Julia for data science? This might be the best intro out there.

### Most popular (badge-winning) recent posts on Statistics

- The Best Free Data Science eBooks: 2020 Update
**[Silver Blog]**The author has updated their list of best free data science books for 2020. Read on to see what books you should grab. - Statistics with Julia: The Free eBook
**[Silver Blog]**This free eBook is a draft copy of the upcoming Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence. Interested in learning Julia for data science? This might be the best intro out there. - Modern Data Science Skills: 8 Categories, Core Skills, and Hot Skills
**[Gold Blog]**We analyze the results of the Data Science Skills poll, including 8 categories of skills, 13 core skills that over 50% of respondents have, the emerging/hot skills that data scientists want to learn, and what is the top skill that Data Scientists want to learn. - These Data Science Skills will be your Superpower
**[Gold Blog]**Learning data science means learning the hard skills of statistics, programming, and machine learning. To complete your training, a broader set of soft skills will round out your capabilities as an effective and successful professional Data Scientist. - 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. - A Complete Guide To Survival Analysis In Python, part 1
**[Silver Blog]**This three-part series covers a review with step-by-step explanations and code for how to perform statistical survival analysis used to investigate the time some event takes to occur, such as patient survival during the COVID-19 pandemic, the time to failure of engineering products, or even the time to closing a sale after an initial customer contact. - 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.