Topic: Statistics
This page features most recent and most popular posts on Statistics.
Latest posts on Statistics
- Data Science Curriculum for Professionals - Mar 25, 2021If you are looking to expand or transition your current professional career that is buried in spreadsheet analysis into one powered by data science, then you are in for an exciting but complex journey with much to explore and master. To begin your adventure, following this complete road map to guide you from a gnome in the forest of spreadsheets to an AI wizard known far and wide throughout the kingdom.
- Rejection Sampling with Python - Mar 24, 2021Read this article on rejection sampling with examples using the Normal and Cauchy Distributions.
- KDnuggets™ News 21:n11, Mar 17: Is Data Scientist still a satisfying job? How To Overcome The Fear of Math and Learn Math For Data Science - Mar 17, 2021Must Know for Data Scientists and Data Analysts: Causal Design Patterns; Know your data much faster with the new Sweetviz Python library; The Inferential Statistics Data Scientists Should Know; Natural Language Processing Pipelines, Explained
- Must Know for Data Scientists and Data Analysts: Causal Design Patterns - Mar 12, 2021Industry 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.
- The Inferential Statistics Data Scientists Should Know - Mar 11, 2021The foundations of Data Science and machine learning algorithms are in mathematics and statistics. To be the best Data Scientists you can be, your skills in statistical understanding should be well-established. The more you appreciate statistics, the better you will understand how machine learning performs its apparent magic.
Most popular (badge-winning) recent posts on Statistics
- How To Overcome The Fear of Math and Learn Math For Data Science [Gold 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.
- 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.
Interpretable Machine Learning: The Free eBook