PYTHON

One of the best things about using Python is its infinity of open-source libraries. There is a library for basically anything. If you have read some of my previous blogs, you may have noticed that I’m a big fan of low-code libraries. That’s not because I’m lazy to type code but because I prefer investing my time working on a project with what really matters. If a library can solve a problem, why not save your precious time and give it a try? Today, I will introduce you to 5 libraries that you probably have never heard about but you…

PYTHON

Everybody has heard of the sentence a picture is worth a thousand words. The reason why this sentence is so well known is that it’s true. There are things that only our eyes can catch. That’s why data visualization is an essential step for any project or report. A person who is not familiarized with the dataset can easily give us insights into it. However, creating data visualizations can be time-consuming, and sometimes, you want to take a quick look at the dataset. Luckily, we have QuickDA, a library that can solve this issue.

I have written about how you…

PYTHON

Whether you are a data analyst or data scientist, you have heard that professionals spend 80% of their time doing data cleaning and only 20% modeling. That is true that data cleaning and exploratory data analysis take multiple steps to be performed, and remember all the code requires a lot of practice. A simple data visualization, for example, requires at least three lines of code to be minimally presentable. However, what if I tell you that there is a way to reduce the time spent on EDA drastically? That’s when QuickDA can be handy.

PYTHON

Data visualization is a crucial step in any exploratory data analysis or report. It’s usually easy to read, and it can give us insight into the dataset in one look. There are dozens of great tools for business intelligence, such as Tableau, Google Data Studio, and PowerBI that allow us to create graphs easily. A data analyst or data scientist will often create visualizations on a Jupyter Notebook using Python. Luckily, there are dozens of great Python libraries that create great graphs. However, the million-dollar question is: which one is the best?

Whether you are a student or a professional…

SQL

Nowadays, SQL has become one of the top skills for professionals. Every day, more companies start using relational databases. For this reason, professionals who want to grow in their careers understood that learning SQL is a must. Even professionals using SQL for a few years (like myself) could get confused with all the SQL commands and know that they need to practice. The other scenario problem is that multiple relational database management systems and many essential commands are not the same.

Here’s the problem. There are hundreds of online courses that can teach how to use SQL. However, most of…

NATURAL LANGUAGE PROCESSING

When working with Natural Language Processing, you will inevitably find misspelled words. Even the best writers in the world will make typos. This article, for example, will be read at least three times before I push the button Publish. Still, it will have mistakes. It doesn’t matter if you are working with a dataset with product reviews or tweets; typos will be there. The problem is that this might affect the accuracy of NLP models because some important words can be missed if it’s misspelled.

Luckily, TextBlob can fix this, and I will show you how you can apply it…

MACHINE LEARNING

Let’s say you need to work on a regression machine learning project. You analyze your data, do some data cleaning, create a few dummy variables, and now it’s time to run a machine learning regression model. What are the top ten models that come to your mind? Most of you probably don’t even know that there are ten regression models out there. Don’t worry if you don’t know because, by the end of this article, you will be able to run not only ten machine learning regression models but over 40!

A few weeks ago, I wrote the How to…

Cuando inicias un nuevo proyecto de Machine Learning supervisado, uno de los primeros pasos es analizar los datos que tenemos, entender lo que estamos tratando de lograr, y qué algoritmos de machine learning podrían ayudarnos a lograr nuestros objetivos. Si bien la biblioteca scikit-learn nos facilita la vida al hacer posible la ejecución de modelos con unas pocas líneas de código, también puede llevar mucho tiempo cuando necesitas probar varios modelos. …