One of the most important and biggest steps in almost any Data Science project is the data preparation phase, this includes loading and cleaning our data. When we begin to clean our data we will most like encounter two different types of data. The first of these data types is…

A subquery in SQL is a query that is nested inside another query. This simply means that we will have an additional SELECT statement, contained inside parentheses, surrounded by another complete SQL statement. Many times in order to retrieve certain information that we want we will have to perform some…

We are generating huge amounts of data on a daily basis simply by using our phones, playing that YouTube video, or by simply paying for a meal. The companies that are providing these services to us collect this data internally and use it to help them make data driven decisions…

In Python we use a for loop to iterate over a sequence, generally this sequence is a list, dictionary, tuple, set, or string. Traditionally it is used when we have a block of code which we want to repeat a certain number of times. For loops are extremely valuable to…

Confusion Matrices are a useful tool for evaluating binary classification models, it can even be extended to multiclass classification problems but we will stick to binary cases for this article. The matrix is a way for us to see our model’s predicted classes vs the actual outcomes. It’s called a…

Today we will be continuing our series on visualization tools, specifically focusing on the Seaborn library. As a reminder, we went over the basics of the Matplotlib library in our previous article, a link will be provided at the bottom of the page for to view it. …

This is the fifth part in my series teaching the basics of SQL. As a reminder in parts 1–4 we have gone over a few of the most common keywords that are used to query a database. We will be utilizing many of these keywords that we have gone over…

We have seen the importance of the pandas library and its impact on data manipulation and visualization for Data Scientists. We can also use pandas to explore our dataframes by grouping them by certain variables and then performing summary statistics on them. In this blog we will go over aggregating…

Pandas is an immensely useful Python package used mainly for data manipulation but also can be used for data visualization. As we have talked about in a previous article, “A Pandas Primer”, Pandas is built upon two essential packages, Numpy and Matplotlib. Numpy allows for the easy and quick manipulation…