What are the resources needed to become a Data Scientist? How does one become successful in this new career path?
We will answer these questions by looking at ourselves, our research on careers in data science, and conversations we’ve had with people who have successfully built careers in data science. They say that when you find yourself asking “how” or “what” three times, you should start taking notes. Let’s begin:
How many years of education do I need to become a Data Scientist?
We’ll leave aside MBA programs and online courses (more on that later) and focus only on degrees that could be related to data science or statistics. If we define Data Science as the “application of statistical programming and machine learning techniques to create actionable insights “, you could make the case that any degree related to math, statistics, or computer science could be helpful.
Let’s look at how many students are graduating with degrees in data science each year compared to other majors:
Data source: The Data Incubator – Class of 2016 Report
STEM (Science, Technology, Engineering and Mathematics) fields lead the way in terms of the sheer volume of graduates which isn’t too surprising. What is perhaps more interesting is seeing Business Management & Administration being so close behind Data Science.
This suggests that having a background in business management may not have an adverse effect on getting hired as a data scientist. Note however that this chart does not show what percentage of graduates from a degree program goes on to work as Data Scientists.
How much money can you make as a Data Scientist?
If you happen to have a PhD in Machine Learning or Statistics from Stanford, then you probably don’t need us to tell you that it pays well 🙂 In all seriousness, the answer is that there is no simple answer here. There are many companies that pay data scientists more than doctors and lawyers. On the flip side though, there are also many more job postings for doctors and lawyers compared to data scientists so keep your expectations reasonable!
We’ve talked with a lot of people about how they got their first job as data scientists. Let’s take a look at what some of them had to say:
“I majored in Design and Technology at Stanford where I got a computer science degree, but with the emphasis on design. After college I went to law school for one year before dropping out and deciding to apply to data science boot camps.”
“I was an engineer at my first job which gave me exposure to technical aspects of data science early on. At my current company, most people come from either research or industry background.”
“I knew very little about coding when I started doing data science. Now that I understand the concepts behind programming, it allows me to effectively learn new tools more quickly.”
We can see from their stories that prior technical knowledge definitely helps speed up your learning process especially if you want to eventually work with technologies like Spark or Hive. Beyond technical knowledge, people seem to value ambition, curiosity, and the ability to communicate clearly, and have an open-minded attitude towards learning new technologies.
How can I get started in Data Science?
If you are reading this article chances are that you have at least some programming experience so we’ll focus on what advice we would give to those without any prior exposure to data science. Below is our rough suggestion for how someone could go about getting started:
1) Start taking online courses in Python or R (find here) (update: it’s also possible to use Julia which you can learn more about here. If interested we recommend starting with the free Coursera course taught by the inventors of Julia). For an idea of what’s possible check out this blog post by Github engineer Mike Couter marsh about how he used the Coursera course in R programming to transition into a data science role.
2) Start practicing/reading up on machine learning (find here) (update: Google also offers an intro course to Machine Learning which can be found here). For ideas on free datasets to practice with, consider checking out Kaggle. This website contains thousands of datasets that people can download and practice their machine learning skills.
Although there are no guarantees, many companies hiring for data scientist roles will look favorably upon candidates who have competed in Kaggle competitions. We recommend taking at least 1-3 introductory courses before becoming familiar with any of these tools.
Conclusion:
If you are interested in getting started with Data Science we recommend checking out the resources above and giving it a shot. It’s important to us as a company that everyone who wants to work as a data scientist has the opportunity to do so. This directive applies equally whether you’ve been working as a data scientist for years or have just begun your journey. Please feel free to also ask questions below if anything is unclear!
