This article is for those who fall into one of the following categories: You don’t have a post-secondary degree but you’re interested in data science; You don’t have a STEM-related degree, but you’re interested in data science; You’re working in a field completely unrelated to data science, but you’re interested in data science.; You’re simply interested in data science and want to learn more about it.

I will try to explain CLT so that you don’t get trapped in the same misunderstanding as mine in the above. In the end part, I also discuss the theorem’s importance in one of the core competencies that every data scientist should comprehend, namely hypothesis testing.

The confidence interval is an approach to show what is the vulnerability inside certain statistics. In this article, we will explore the confidence interval and demonstrate how to interpret confidence intervals.

Regression Analysis is about predicting a value or attribute of a variable based on some other variables. And linear regression is when there is only one variable you want to predict based on another single variable.

A Full Guide to Waiting Line Models and Queuing Theory. In this article, I will give a detailed overview of waiting line models. I will discuss when and how to use waiting line models from a business standpoint.

Central Limit Theorem -Complete Guide for Beginners! 1) There is a population. 2) We take many number of random samples of same size from our population. 3) Take mean of those samples. 4) Plot those mean on distribution graph ( Histogram )

A machine learning approach to accelerate engineering design. We will focus on the fundamentals of this method by going through the following aspects: Motivation: why do we need a method to accelerate computer simulations? Solution: how is surrogate modeling helping the situation? Details: how to actually apply surrogate modeling?

The first two figures show what can happen — they are extreme cases, and other cases may incorporate both small continuous changes and big jumps. The third figure below indicates what “the picture becomes clearer” analogy would suggest — that probabilities should change only by moving closer to the ultimate outcome.

Example of Chi-Square Test in Python. We will provide a practical example of how we can run a Chi-Square Test in Python.

Generative and discriminative are classes of machine learning models. In this article, we will look at the difference between generative and discriminative models, how they contrast, and one another.

Error correcting codes: Another unseen but widespread use of linear algebra is in coding theory. The problem is to encode data in such a way that if the encoded data is tampered with a little bit, you can still recover the unencoded data.

Learn decision trees, Gini, pruning and much more from scratch with hands-on in python. In this article, we are going to cover just that. Without any further due, let’s just dive right into it.

Election forecasts are predicting a Biden win with a chance of around 9 in 10, but do these numbers fully capture the uncertainty in the outcome?

In this article, I will dig into what that data looks like and some into of its characteristics, discuss a few of its issues and start a discussion on how to look at it from a time series forecasting perspective.

Cohen’s Kappa is a statistical measure that is used to check if two raters rating the same quantity are reliable and agree with each other.

The 68–95–99 Rule — Normal Distribution Explained in Plain English. It’s a foundational concept in statistics, and the key to understanding a range of natural phenomena

Why you should be plotting learning curves in your next machine learning project. Spoiler: they will help you understand whether your model suffers from high variance or high bias — and I’ll explain what you can do about it

Four Types of Random Sampling Techniques Explained with Visuals. The secret to minimizing biased data!

Statistics for Data Science — Practical Tips, Misconceptions, Curriculum and Learning Plan. Answering important questions by transforming data into insights with Statistics

In this article, check out how to compare multiple projects with the GitHub Stats tool.