Summary

You should now have a basic understanding of how data science came to be, what tools and techniques are used in the field, specializations in data science, and some strategies for managing data science projects. We saw how the ideas behind data science have been around for decades, but data science didn't take off until the 2010s. It was in the 2000s and 2010s that the deluge of data from the internet coupled with high-powered computers enabled us to carry out useful analysis on large datasets.

We've also seen some of the skills we'll need to learn to do data science, many of which we will tackle throughout this book. Among those skills are Python and general programming skills, software development skills, statistics and mathematics for data science, business knowledge and communication skills, cloud tools, machine learning, and GUIs.

We've seen some specializations in data science as well, like machine learning and data engineering. Lastly, we looked at some data science project management strategies that can help organize a team data science project.

Now that we know a bit about data science, we can learn about the lingua franca of data science: Python.