Data science is about putting theory into practice. Build up your hands-on skills in Statistics, Python, R, SQL, Tableau, Excel, Machine Learning, Deep Learning, and Time Series Analysis with our step-by-step, easy to understand tutorials and implement what you’ve learned in your future data science and analytics projects.
If you’ve been researching or learning data science for a while, you must have stumbled upon linear algebra here and there. Linear algebra is an essential part of coding and thus: of data science and machine learning. But even then, you may be compelled to ask a question… Why is...
Introduction to Databases and an Example of a Data Table When you become a successful SQL programmer, working with multiple data tables will be a walk in the park. One thing you’ll notice when studying programming languages is that the concepts you encounter are interrelated. This means by...
"A hero is someone who voluntarily walks into the unknown" - Tom Hanks. Why did we start a post about the Jupyter dashboard with a Tom Hanks quote? Well, we want you to be a Python hero (which you will be). And if you are a complete beginner, you have to open Jupyter and "walk into the...
We can classify data in two ways: based on its type and on its levels of measurement. If you want to figure out how to do it based on its type, that’s something we covered in this tutorial. We've also made a video on the topic. You can watch it below or scroll down if you prefer...
There are two errors that often rear their head when you are learning about hypothesis testing – false positive and false negative, technically referred to as type I error and type II error respectively. At first, I was not a huge fan of the concepts, I couldn’t fathom how they could...
