A Professional’s Tutorial to Python Making Fictional Data

This tutorial will help learners showcase data science skills

For an overview of these tutorials, click here (or click the image).

Welcome to ninth, in a series of tutorials that teach beginner Python specifically for aspiring data scientists. For an overview of these tutorials, click here.

Introduction

I want you to make fictional data. I really want this for you. The reason I want this for you is because, if you’ve been following along with this sequence of tutorials, making fictional data will showcase what you’ve learned ways that will support your journey towards data science.

Bonus, having made a new set of fictional data you will have a set of data that nobody else has (unless you share it). When you use your own personally created data for future projects you’ll be making, producing, and distributing something new and unique. It’ll help you and your professional portfolio stand out from the pack.

Image Credit: Author’s illustration created in Canva.com.

You might be skeptical.

I won’t question your skepticism.

Instead I will explain that while real-world data provides the raw materials for most of our analytics and machine learning projects, fictional data has emerged as its own niche. Sometimes fictional data gets the moniker “synthetic data.”

Fictional or synthetic data is a valuable tool for both novices and professionals alike.

How Making Fictional Data Demonstrates Data Science Skills

Creating fictional data is not about conjuring completely random numbers and facts fully and wholly out of thin air. Instead, this exercise is about using imagination, statistical knowledge, and technical prowess in highly technical and creative ways.

You can demonstrate many core data science competencies through the act of generating this type of data including for example:

1. Statistical Savvy: Crafting fictional data requires a grasp of statistical distributions, means, medians, modes, and more. It’s not just about generating…