In the previous section I showed you how to set up your computer for a data science project in Python. In this tutorial, I’ll tell you about the basic data types and operators in Python.

Artistic rendering of Python Code
Artistic rendering of Python Code
Image by Johnson Martin from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

I. Data Types

Python has about twelve different data types and in this tutorial I’ll introduce you to five of them: string, integer, float, boolean, and list. I’ll also…


An abstract photo of melting clocks.
An abstract photo of melting clocks.
Image by Gerd Altmann from Pixabay

Data in a Day is a series of tutorials designed to guide you through a quick, easy, and end-to-end data science project with Python in one day. Completing the series will teach you how to set up your computer, introduce you to the data science work flow, and show you some of the most popular tools.

One tip that I have often heard regarding learning data science is to start doing projects as soon as possible. I thought that it would be really useful if there was an option that allowed a beginner to properly set up and execute a project in a short period of time.

The following series of tutorials is divided into different sections that should take a couple hours each.

Part I: Setup (2 hours)

  1. General Setup for Data Science Projects with Python
  2. Virtual Environments I: Installing Pyenv with Homebrew
  3. Virtual Environments II: Creating a Virtual Environment with Pyenv and Installing Data…

In Pandas II, we began to clean up the Metal Bands by Nation data set. We eliminated an unneeded column and filled some missing values. Now, we are going to examine the other columns, assess their data types and value, and take action as needed.

Image by Nick115 from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

  1. General Setup for Data Science Projects with Python
  2. Virtual Environments I: Installing Pyenv with Homebrew
  3. Virtual Environments II: Creating a Virtual Environment with Pyenv and Installing…

In Pandas I tutorial, we used Jupyter Notebooks and Pandas to begin working the Metal Bands by Nation spreadsheet. We used a few different Pandas methods to do an initial inspection. In this tutorial, we will try to clean up and improve the data frame for analysis.

Image by Sharon Ang from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

  1. General Setup for Data Science Projects with Python
  2. Virtual Environments I: Installing Pyenv with Homebrew
  3. Virtual Environments II: Creating a Virtual Environment with Pyenv and Installing…

In the last tutorial, I introduced you to Jupyter Notebooks. Now that you have Jupyter notebooks installed, I can show you how to use them to work with data. I will also introduce you to Pandas, a popular library used for working with spreadsheets.

Image by 995645 from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

  1. General Setup for Data Science Projects with Python
  2. Virtual Environments I: Installing Pyenv with Homebrew
  3. Virtual Environments II: Creating a Virtual Environment with Pyenv and Installing…

In Virtual Environments II and Juypter Notebook I I we created a virtual environment for your project and set up Jupyter Notebooks. Now that you have a nice little environment set up on your local machine inside the folder for your project, it’s time to create your GitHub repository. In this tutorial, we will cover the basics of setting up your workflow with GitHub.

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

I. Why Should I Use GitHub?

GitHub is a powerful tool for collaboration. Using GitHub, you can show case your work to share with others. …


In the Virtual Environments II, I showed you how to create a directory and activate a virtual environment for your Python data science project. I also showed you how to install some libraries: Ipykernel, Jupyter Notebook, Pandas and Matplotlib. In this tutorial, we’re going to discuss Ipykernel and how to get started with Jupyter Notebooks.

Image by Reimund Bertrams from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

I. Creating a Kernelspec to Start Using Jupyter Notebooks


In Virtual Environments I, I explained how to install package managers, the latest release of Python, and Pyenv and Virtualenv. Now that those items are installed, we can set up a virtual environment for a Python project.

Python data science packages, Jupyter, Pandas, Seaborn, Matplotlib
Python data science packages, Jupyter, Pandas, Seaborn, Matplotlib
Python Data Science Packages

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

I. Create a Unique Virtual Environment for a Data Science Project with Pyenv-Virtualenv

It’s a good idea to create a new virtual environment for each of your project. So we will start by making a directory for our project.


Before you begin your first data science project using Python, it’s helpful to make sure that your computer is properly set up for the task. I broke this process down into manageable steps to help you get get started.

Image by Renate Becker from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

  1. General Setup for Data Science Projects with Python
  2. Virtual Environments I: Installing Pyenv with Homebrew
  3. Virtual Environments II: Creating a Virtual Environment with Pyenv and Installing…


If you’ve used Python before, you may have encountered a situation known as “dependency hell”. Some packages depend on other packages to work. Sometimes, the package that another package depends on must be a certain version. Later on, you might need to use a different version of that package because there is yet another package that depends on that package being another version. So the best way to avoid dependency hell and create professional projects is to use virtual environments.

a lego toy office worker frustrated in front of a tiny computer
a lego toy office worker frustrated in front of a tiny computer
Image by www_slon_pics from Pixabay

This blog is part of a series of tutorials called Data in Day. Follow these tutorials to create your first end-to-end data science project in just one day. This is a fun easy project that will teach you the basics of setting up your computer for a data science project and introduce you to some of the most popular tools available. It is a great way to get acquainted with the data science workflow.

A virtual environment is like a little capsule for your project that contains all of the proper packages, in the proper version, in one spot. This…

Christine Egan

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