Across industries, data scientists are bringing buzzwords to life: artificial intelligence, machine learning, big data, data visualization. Behind the buzzwords are software skills like Python, R, SQL, and Java. The list goes on and can sound intimidating, but getting started is more approachable than ever and the value of foundational, entry-level data science skills is taking off.
“Today with data science, for a lot of it you don’t have to have a PhD anymore. You don’t have to spend years and years studying something. The runway is a lot shorter this year for data science,” said Joseph Santarcangelo, PhD, IBM data scientist, and instructor for several edX data science courses and programs, from Python to deep learning.
“Even some years ago, if you wanted to perform any kind of data science task, you had to spend a lot of time understanding the concepts, learning the programming languages, but now all you really have to know is Python and have a basic understanding of what’s going on and it’s pretty remarkable where you can go.”
Read on for more excerpts from our interview with Santarcangelo, where he shares insights from working in and teaching data science, along with advice for those considering a data science career path or adding data science skills to another discipline.
What Does it Take to Get Started in Data Science?
Most of what I do is stuff I learned in the beginning. I’ll spend a long time learning a framework. A lot of it’s just going into Python, understanding the language, understanding what does what — and those are the first few things I learned.
The first step is the largest. You’re going to make the biggest jump. You’ll get 70% of the way there in your first few steps. A year of studying data science will get you very far.
The hardest part of anything starting it and Python is the first big step to data science.