Everyone wants to be in the field of Data Science and Analytics as it’s challenging, fascinating as well as rewarding. You have to be familiar in the core areas of Data Science, which hinges on the concepts of probability, statistics, machine learning algorithms, visualization, etc. and as a data scientist, these are an essential part of your data science journey, which is why you have to learn them…
There are so many blogs, so many videos, so many crash courses available; it’s difficult to know where to start. To assist our readers, we have assembled a list of seven amazing articles touching each angle of Machine Learning with probability, statistics for data science aspirants, as well as those already in the discipline and wish to be in touch with the basics. We hope that these articles can further guide you in the right direction.
Swapnil Vijay – Editorial Associate / Data Scientist at Scintel Technologies
Statistics for people in a hurry
By Cassie Kozyrkov – 8 min read
Ever wished someone would just tell you what the point of statistics is and what the jargon means in plain English? Let me try to grant that wish for you! I’ll zoom through all the biggest ideas in statistics in 8 minutes! Or just 1 minute, if you stick to the large font bits.
The 5 Basic Statistics Concepts Data Scientists Need to Know
By George Seif – 9 min read
Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data.
Probability concepts explained: Maximum likelihood estimation
By Jonny Brooks-Bartlett – 8 min read
In this post I’ll explain what the maximum likelihood method for parameter estimation is and go through a simple example to demonstrate the method. Some of the content requires knowledge of fundamental probability concepts such as the definition of joint probability and independence of events.
Estimating Probabilities with Bayesian Modeling in Python
By Will Koehrsen – 12 min read
In this article, we’ll explore the problem of estimating probabilities from data in a Bayesian framework, along the way learning about probability distributions, Bayesian Inference, and basic probabilistic programming with PyMC3.
The Actual Difference Between Statistics and Machine Learning
By Matthew Stewart, PhD Researcher – 15 min read
No, they are not the same. If machine learning is just glorified statistics, then architecture is just glorified sand-castle construction.
Machine Learning Algorithms In Layman’s Terms (Part 1, Part 2)
By Audrey Lorberfeld – 14 min read
As a recent graduate of the Flatiron School’s Data Science Bootcamp, I’ve been inundated with advice on how to ace technical interviews. A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person.
Statistics is the Grammar of Data Science (Part 1, Part 2, Part 3)
By Semi Koen – 4 min read.
Statistics refresher to kick start your Data Science journey
We also thank all the great new writers who joined us recently, Yao Yang, Matt Hergott, Ben Mann, Claire Genoux, Perry Johnson, Nikolay Oskolkov, Vikram Devatha, Jorge Castañón, Ph.D., Adam Dick, George Čevora, Jacob Crabb, Nick Evers, Alexander Shropshire, Brandon Lin, Rashmi Margani, Yann Feunteun, Shubham Tiwari, John Murray, Salim Chemlal, Martin Millmore, Giacomo Vianello and many others. We invite you to take a look at their profiles and check out their work.