# Topic: Statistics

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

### Latest posts on Statistics

- Paradoxes in Data Science - Sep 17, 2021Have a look into some of the main paradoxes associate with Data Science and it’s statistical foundations.
- KDnuggets™ News 21:n34, Sep 8: Do You Read Excel Files with Python? There is a 1000x Faster Way; Hypothesis Testing Explained - Sep 8, 2021Do You Read Excel Files with Python? There is a 1000x Faster Way; Hypothesis Testing Explained; Data Science Cheat Sheet 2.0; 6 Cool Python Libraries That I Came Across Recently; Best Resources to Learn Natural Language Processing in 2021
- Antifragility and Machine Learning - Sep 6, 2021Our intuition for most products, processes, and even some models might be that they either will get worse over time, or if they fail, they will experience an cascade of more failure. But, what if we could intentionally design systems and models to only get better, even as the world around them gets worse?
- Hypothesis Testing Explained - Sep 3, 2021This brief overview of the concept of Hypothesis Testing covers its classification in parametric and non-parametric tests, and when to use the most popular ones, including means, correlation, and distribution, in the case of one sample and two samples.
- What is Noise? - Aug 25, 2021We might have a reasonable sense for what "noise" is as some statically random phenomena that occurs in Nature. But, how can this same characteristic be defined--and understood--within the context of making judgements, such as in human behavior, corporate decision-making, medicine, the law, and AI systems?

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

- Hypothesis Testing Explained
**[Gold Blog]**This brief overview of the concept of Hypothesis Testing covers its classification in parametric and non-parametric tests, and when to use the most popular ones, including means, correlation, and distribution, in the case of one sample and two samples. - Learning Data Science and Machine Learning: First Steps After The Roadmap
**[Silver Blog]**Just getting into learning data science may seem as daunting as (if not more than) trying to land your first job in the field. With so many options and resources online and in traditional academia to consider, these pre-requisites and pre-work are recommended before diving deep into data science and AI/ML. - A Brief Introduction to the Concept of Data
**[Silver Blog]**Every aspiring data scientist must know the concept of data and the kind of analysis they can run. This article introduces the concept of data (quantitative and qualitative) and the types of analysis. - 11 Important Probability Distributions Explained
**[Gold Blog]**There are many distribution functions considered in statistics and machine learning, which can seem daunting to understand at first. Many are actually closely related, and with these intuitive explanations of the most important probability distributions, you can begin to appreciate the observations of data these distributions communicate. - A Guide On How To Become A Data Scientist (Step By Step Approach)
**[Platinum Blog]**Becoming a Data Scientists is an exciting path, but you cannot learn data science within one year or six months—instead, it’s a lifetime process that you have to follow with proper dedication and hard work. To guide your journey, the skills outlined here are the first you must acquire to become a data scientist. - Top 3 Statistical Paradoxes in Data Science
**[Silver Blog]**Observation bias and sub-group differences generate statistical paradoxes. - Must Know for Data Scientists and Data Analysts: Causal Design Patterns
**[Silver Blog]**Industry is a prime setting for observational causal inference, but many companies are blind to causal measurement beyond A/B tests. This formula-free primer illustrates analysis design patterns for measuring causal effects from observational data. - How To Overcome The Fear of Math and Learn Math For Data Science
**[Platinum Blog]**Many aspiring Data Scientists, especially when self-learning, fail to learn the necessary math foundations. These recommendations for learning approaches along with references to valuable resources can help you overcome a personal sense of not being "the math type" or belief that you "always failed in math." - Want to Be a Data Scientist? Don’t Start With Machine Learning
**[Gold Blog]**Machine learning may appear like the go-to topic to start learning for the aspiring data scientist. But. thinking these techniques are the key aspects of the role is the biggest misconception. So much more goes into becoming a successful data scientist, and machine learning is only one component of broader skills around processing, managing, and understanding the science behind the data. - 15 Free Data Science, Machine Learning & Statistics eBooks for 2021
**[Platinum Blog]**We present a curated list of 15 free eBooks compiled in a single location to close out the year. - Monte Carlo integration in Python
**[Gold Blog]**A famous Casino-inspired trick for data science, statistics, and all of science. How to do it in Python? - 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.