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Data Scientist | Machine Learning Engineer | Based in NYC | Writer |


Discover three cool libraries and why you should try them right now

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A few days ago, I published a blog on 5 Python Libraries That You Don’t Know About, But Should. That blog had dozens of thousands of views in less than a week. Thus, I decided to write a part two with other cool libraries that you should try out.

Python is one of the most popular languages in computation. One of the main reasons is that it contains hundreds of open source libraries that can make our life easier. People focus on more popular (and more reliable) libraries. However, other less-known libraries can save hours of your precious time. That…


Discover 5 Python libraries that you should start using right now

Image by Christina Morillo. Source: Pexels

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…


Learn how to use QuickDA to create data visualizations with one line of code

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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…


Learn how to do a use QuickDA for your next project and save hours of work

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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.


Matplotlib, Seaborn, Plotly Express, and Altair. Which of these is the best library for data visualization?

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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…


Learn how to find and start using a complex database to practice SQL

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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…


Learn how to use TextBlob to fix misspellings and improve the model for your Natural Language Processing project

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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…


Learn how to run over 40 machine learning models using Lazy Predict for regression projects

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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…

Aprende a ejecutar varios modelos de aprendizaje automático mediante predicción diferida.

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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. …


In this blog, I walk you through my transition from Marketing to Data Science and why you should consider doing the same

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One of the questions that I get the most as a data scientist with a background in marketing and business intelligence is why I switched careers. That is interesting because I have never decided to move away from marketing and start a move to another field. It was an evolution of my job that brought me to data science. In fact, I have a Bachelor of Science in marketing, which is a type of marketing focused on data analysis and business intelligence. Thus, this transition was going to happen at some moment in my career.

Since my day one working…

Ismael Araujo

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