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Entering the Field of Data Science: 2018 Industry Spotlight

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In recent years, more and more schools have been adding data science programs to their curriculum. With such a popular field growing to even greater prominence, we want to highlight this field and how an education in it can impact your life. We were also lucky enough to speak with Raj Bandyopadhyay, Director of Data Science Education at  Springboard, and Michael Li, founder and CEO of The Data Incubator, to get their insights about programs!

What is data science?

You may have heard the phrase used before, but what exactly is it? Essentially, it’s the use of data to gather knowledge and insights, and its implementations are myriad.

“At its core,’ says Michael, it’s “a practice that requires skill in both quantitative problem-solving and computer science.”

“Most students are drawn by public hype,” says Raj, “then discover all the tangible applications, from self-driving cars to voice recognition, language translation and AI, and much more. Many of our students are professionals in other fields, and see data science as a knowledge and impact multiplier; i.e. something that will help them have a really huge impact in their field or solve problems they care about.”

Want to read even more about the industry, how you can become a data scientist, or what work is being done in the field? Check out the most-read articles from Data Science Weekly!

How has data science grown?

In one word: significantly. According to PayScale, the average annual income of an entry-level data scientist is $85,553, with the standard range starting at $57,000 and going up to $119,000. And with demand for data scientists increasing, these numbers are only expected to go up. In fact, according to a recent survey by Glassdoor of its US-based members, respondents ranked data science third in work-life balance, first in pay, and third in number of job openings.

What does the future of the industry look like?

“Data science has a long runway in front of it,” says Raj. Which makes sense, as more and more, various industries are finding applications for data science — which is why he says that Springboard makes sure their mentors hail from a diverse array of industries.

So what’s one example of work that’s adopted this field? Environmental sustainability, says Raj, is “a rapidly expanding space where the applications for data science are endless. Scientists can use data analysis to understand the impact of climate change, dig into machine learning and statistical analysis to optimize energy usage of buildings, and so much more.”

How can you get into data science?

The first steps, as with any field that’s new to you, are doing your research on the subject and taking a course. This way, you can first see if it’s right for you and then really build your skills.

Because this is such a wide-reaching field, there are many opportunities for people with all sorts of backgrounds. This is something Michael sees in effect at The Data Incubator: “Students don’t need to necessarily major in mathematics or computer science,” he says. “[We’ve] had graduates who have been very successful as data scientists with degrees in organizational behavior and operations research.”

So while it is challenging work — requiring technical and interpersonal skills, extreme attention to detail, and communication skills — it’s not limited to people with a certain education or experience.

And if you’re considering making a career switch, Michael has recommendations for how to get a head start: “Anyone interested in pursuing data science as a career,” he says, “should have a solid understanding of the fundamentals of data science — how to use probability and statistics to analyze data, and how to write and understand a coding language like Python or R, or both.” These are all skills you can pick up in a data science course. To get started, check out Career Karma’s list of the best data science courses!

Raj also has four pieces of advice for aspiring data scientists:

Data Science

  1. Work on a portfolio: The one thing that impresses potential employers most is having a portfolio of real-world projects to showcase your skills. A good portfolio is far more important than most degrees or a fancy resume. Your portfolio should reflect a breadth of skills in data science, from data collection and wrangling to actual analysis and algorithms. It should be well-organized and documented to demonstrate your communication skills.
  2. Start from a position of strength: How do you decide what projects to put in your portfolio? We recommend building upon your existing domain knowledge and skills. For example, if you’re currently a civil engineer, what are some interesting problems in your domain you could apply data science to? Perhaps you could improve the structural integrity of buildings, or predict the impact of natural disasters. Using your existing domain knowledge to define and solve a problem will help you focus your energy, and result in an impressive project for your portfolio.
  3. Use your network: Talk to more experienced data scientists in your network — take them out to coffee, get them on the phone, and tell them about your project. Ask them to recommend specific tools, methods, approaches to solve this problem, and then relentlessly study and practice those.
  4. Find a mentor: Accept mentorship from an expert data scientist who is where you want to be in your career in a few years. They will help accelerate your learning curve and help you understand what the day-to-day of a data scientist really looks like.

And finally, what can students expect when taking a course through Springboard and The Data Incubator?

In keeping with both the current and future environment for data science, here’s what Raj and Michael say Springboard and The Data Incubator are doing to prepare their students:

Raj, Springboard:

  1. Robust, employer-vetted curriculum: Whether in our Intro to Data Science course or the job-guaranteed data science bootcamp, our curriculum is based on employer research. We work with employers and hiring managers to identify the critical skills that students need to succeed in both the data science roles as well as the interview process, and make sure they’re thoroughly covered in the curriculum. We have a strong emphasis on not just the technical aspects of data science, but also the vital communication skills that a data scientist needs for their day-to-day work.
  2. Capstone project: Our students get real-world experience they need through their final capstone. They work with their mentor to define and solve a real-world data science problem. Since the capstone project involves a realistic problem with real-world data, the students have to go through all the steps required to work with and analyze the data. Throughout the project, students keep their hypothetical client or boss in mind. To graduate, they present their project to fellow students and mentors during the weekly office hours, sharpening their communication skills and perfecting their process.
  3. Mentorship: Our mentors are all actively practicing data scientists in industry, and are the best of the best: we only accept 12 percent of mentor applicants. They bring real-world experience to mentoring, and take a keen, personal interest in ensuring their students projects are well prepared. Mentors meet with their students regularly to ensure projects are on track and up to par. It’s not enough for a student to carry out a technical analysis — they have to learn how to communicate their work in order to fulfill the requirements of the program.

Michael, The Data Incubator:

  1. Variety of course levels: At The Data Incubator we train students at all levels; whether it’s one of our introductory online courses or our full-time Fellowship program, we strive to provide students with the most up to date curriculum available — built with feedback from our industry training partners.
  2. Learning by doing: Hands-on experience with the latest data science tools and techniques being used in industry is crucial for finding success as a professional data scientist, which is why it’s a cornerstone of our curriculum.
  3. Career Assistance: In our Fellowship program we also provide job search training to coach Fellows on how to search for and apply to data science roles, as well as prepare for technical interviews, and connect them with our hiring partners via our online resume book.

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