Am I a Good Fit for Data Science?

In the last blog post, we talked about the differences between data scientists, data engineers, and data analysts. This week we’ll be looking generically at the industry of data science and one of the questions we hear most often: am I a good fit? Specifically, people are wondering if they have the right skills and how they will apply this either to a current or future career.

Skill Set

Let’s start with the skill set you should have coming into a Data Science program. Of course you’ll learn all kinds of new skills in a 16-week data science focused course, but what about natural talent and skills you’ve already nurtured? Let’s look at 4 skills that help ensure our students are successful.

  1. You enjoy math – Simply put, data science is math so you need to have a love for numbers. This doesn’t necessarily mean that you got an A in Calculus in high school, but you definitely weren’t the one saying, “I hate math” either. Maybe you love Sudoku puzzles. Maybe you’re fascinated by baseball statistics and how it may (or may not) predict the success of one team over the other. This curiosity makes you a great candidate.
  2. You’re analytical – You like to get to the root cause of something and aren’t afraid to dig around to get the facts. If someone to ask you for a friendly wager on the probability of something happening, you’re the type of person that would only play if you’d had ample time and knowledge to properly predict the outcome. You’re not a guesser because you know that’s not the most accurate method of prediction.
  3. You look for the related impact – Some people are happy to study theoretical physic with no real concept of how their work will ever be applied in the real world. Data scientists want to see the impact of their work. If you’re going to spend all your time crunching numbers and writing code to crunch numbers, you want to know why. What do these numbers mean? And, more importantly, what will change because of them?
  4. You are a lifelong learner – Data science is in its infancy so what’s being taught today will be considered rudimentary five years from now. In this field, you will have the opportunity to continue to learn new programming languages, new methods, and new technologies. The learning process will never end. To be successful, you should be someone who has already demonstrated an interest in learning for the sake of learning.

Industries

Data science can be applied to a limitless number of industries, but here we provide a sampling to show how some are already using it.

  1. Environmental science – Global Fishing Watch is using data to monitor the world’s largest fishing operations. By relying on the GPS data of more than 70,000 boats, they’re working to protect critical marine habitats. On the flip side, the most sophisticated of the fishing companies are likely also using data science to make their catches as profitable and efficient as possible.
  2. Logistics – This $8 trillion industry is responsible for moving goods around the world. With money like that being spent, it isn’t surprising that they are finally turning to data to make better decisions. Accurately predicting demand is one of the most important ways data science can help. Given the importance of this field, it’s no surprise that Amazon is continuously looking at different delivery mechanisms – from operating their own fleet of planes to authorizing and supporting Amazon Prime branded delivery services.
  3. Retail – It’s fitting that we follow-up a discussion of Amazon with retail stores since that’s one of the primary industries it is disrupting. If you compare a store like Nordstrom today with a few years ago, you’ll notice that their retail footprint is changing dramatically and this isn’t by accident. This is data science at work. Nordstrom calculates everything from how much money they make per store to how much money comes from each square foot of a specific store to how much money comes from all online sources. How valuable is this data? Very – as evidenced by their recent decision to stop publicly reporting this data. They need the data and they will act upon it, but they definitely don’t want Wall Street to do the analysis for them.
  4. Healthcare – While insurance companies are all moving towards more data-driven decisions, data science is also making a big impact on global health initiatives. As a example, the Center for Disease Control has used real-time mapping data to track the spread of Ebola across Africa and around the world. This allows them to pinpoint the best places to send aid. To learn more about the potential massive global impact of pooling healthcare data, check out John Wilbank’s TED talk.

Data science can truly be applied to any industry. If you’re an analytical person who is good at math and up for the challenge of continuous learning, you have the potential to discover the data that can have meaningful impact.

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