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.
In our previous tutorial, we became familiar with the ARMA model. But did you know that we can expand the ARMA model to handle non-stationary data? Well, that’s exactly what we’re going to cover in this post - the intuition behind the ARIMA model, the notation that goes with...
What do we use the Moving Average model for? In time-series, we sometimes observe similarities between past errors and present values. That’s because certain unpredictable events happen, and they need to be accounted for. In other words, by knowing how far off our estimation yesterday was,...
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...
In this tutorial, we will use a database called “Sales” to illustrate the concept of relational schemas a little better and introduce you to the SQL primary key. The data will be stored in 4 tables – “Sales”, “Customers”, “Items”, and...
Functions Containing a Few Arguments Exercise: Create a function that determines the type of a triangle based on the lengths of its sides. If you have followed our exercises, you may have seen a variation of this one – to create an IF statement that checks the same thing. This exercise can...
