This page features most recent and most popular posts on Statistics.
Latest posts on Statistics
- Find the Best-Matching Distribution for Your Data Effortlessly - Oct 22, 2021How to find the best-matching statistical distributions for your data points — in an automated and easy way. And, then how to extend the utility further.
- How to calculate confidence intervals for performance metrics in Machine Learning using an automatic bootstrap method - Oct 15, 2021Are your model performance measurements very precise due to a “large” test set, or very uncertain due to a “small” or imbalanced test set?
- How to do “Limitless” Math in Python - Oct 7, 2021How to perform arbitrary-precision computation and much more math (and fast too) than what is possible with the built-in math library in Python.
- How to Determine the Best Fitting Data Distribution Using Python - Sep 30, 2021Approaches to data sampling, modeling, and analysis can vary based on the distribution of your data, and so determining the best fit theoretical distribution can be an essential step in your data exploration process.
- Advanced Statistical Concepts in Data Science - Sep 30, 2021The article contains some of the most commonly used advanced statistical concepts along with their Python implementation.
Most popular (badge-winning) recent posts on Statistics
- How to Find Weaknesses in your Machine Learning Models [Gold Blog]FreaAI: a new method from researchers at IBM.
- 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.