Introduction In the previous blog post, we looked at controlled experiments and saw what techniques we can use to properly analyze them. Unfortunately though, we don’t always have the option to use a controlled experiment. There are times when data has already been collected and we still want to properly analyze it… …or other times when a controlled experiment is unfeasible or unethical (e.g. unregulated refusing of treatments for patients). Of course, there’s still a
From beginner's guides to advanced tricks, find it all here.
In the previous blog post, we looked at the terms causation and correlation and developed a deeper understanding of what exactly each of those terms means and what the difference between causation and correlation is. In this post, we’re going to continue from where we left off last time and dive into how we can approach different types of datasets and go about analyzing them correctly. More specifically, in the next two blog posts, we’re
Once and for all – what are correlation and causation? How do you differentiate between correlation vs causation?
In this blog post, we discuss what correlations and causations are, some properties and types of correlations plus what noise is, and of course, you’ll find some examples to guide you along the way!
Good ol’ bar graphs are some of the easiest and simplest forms of data visualizations, but let’s take it beyond what we learned in fourth-grade math and get into some of the more advanced questions when it comes to bar graphs, including when to use bar graphs, how to use them, and how to make them in Python.
Plus, let’s also talk through what the differences between bar graphs vs. histograms vs. box plots are and when to use which type of visualization.
Need help with scatter plots? This article goes over what they are, how to use them as well as 3 application of scatter plots including clusters, correlations and higher dimensional graphs.
This blog post covers what programming libraries are, what to use them for in data science, and how to use the top Python libraries for data science and machine learning.
Data science is becoming a very hotter and hotter topic by the minute, and data scientists are becoming more and more demanded by all sorts of companies. I personally like to think of data scientists as the watermelon of the fruit aisle in the summer. Everyone wants one – but there is a limit to how many there are. (Side note: I love watermelon.)
The term “Data Scientists” is very vague though – what exactly does it mean, how can you do it, and what can you do with it?
In this article, we’re going to discuss API’s and address some of the biggest questions surrounding this mysterious acronym:
What are APIs? & What are they used for?
Who has an API?
What are the advantages and disadvantages of API’s?
What is an API Economy?
How to Apply & Monetize an API
Curious? Awesome. Take a read through the article!