This time of year, if you look for it, we have a sneaky feeling you’ll find that data actually is all around us — no, seriously. From your Spotify Wrapped digest to Google’s Year in Search and our very own Code Review, the data in our lives tells a compelling story about the year we’re leaving behind.
End-of-year wrap-ups are fun ways that companies across industries are leveraging data to entertain and inform people. If, like us, you get giddy about stats like the number of minutes our learners spent learning this year (507,530,006 minutes), then there’s a pretty good chance you’ll enjoy learning about data science.
Data science is a multidisciplinary field that involves extracting insights and knowledge from data. It combines expertise from statistics, computer science, and domain-specific knowledge to analyze and interpret complex datasets. Data science is all about transforming copious amounts of information into actionable and interpretable insights.
As JR Waggoner, Data Analytics Manager at Codecademy, puts it: “Data engineering is sometimes very much like traditional software development. Other times, it’s the Wild West,” he says. “You’re taking this list of metrics or data points that the team has compiled and converting them into what we know from the data.”
Data scientists use various techniques, like machine learning and statistical modeling, to uncover patterns, trends, and valuable information that can inform decision-making and solve problems across diverse industries. A common thread among data scientists is a robust grasp of statistics, coding skills, and strong communication skills. Here are some data science courses, paths, and programming languages that’ll teach you techniques like the ones we used to build Code Review.
Basic data literacy
Data is really just a jumble of information until you have the knowledge to contextualize it and draw conclusions. Data literacy is a crucial skill that helps you make sense of all the data floating around — like understanding what it’s trying to tell you or whether the info is trustworthy.
Knowing how to collect data, assess its quality, use statistical thinking, and manage bias will enable you to work with data confidently and responsibly. Data literacy makes you stand out in any field where you have to make informed decisions and back up your arguments with evidence.
Learn the skills:
SQL
Our data pipeline that captures user interactions like code submissions is pretty sophisticated. For Code Review, our engineers used SQL to access information stored in our data warehouse. SQL is a programming language specifically designed for managing and manipulating data within relational databases. You don’t need to be a programmer to use SQL; its syntax is designed to be straightforward and readable, so it’s an accessible and beginner-friendly option for anyone who wants to interact with data.
Learn the skills:
Telling a story with data
Once we’d gathered all the data for Code Review, then came the fun part: figuring out what sort of stories and trends we could uncover. Communicating your data science findings in a visually pleasing and understandable way is an important part of any data scientist’s work. There are a number of data visualization tools and programming languages that you can use to create reports and dashboards.
Learn the skills:
We hope that Code Review inspires you to add some data science skills to your tech stack. If you want to work towards a career in data science, we have focused career paths that’ll help you quickly land a job you want in a variety of data science specializations.
This blog was originally published in December 2023 and has been updated to include additional courses.
