If you are in in high school and love numbers and math, you have a lot of career trajectories available to you. From a STEM career in computer programming or mechanical engineering to teaching, your math skills can open so many doors in so many industries. There isn’t an industry where you will not be able to apply these skills in one shape or another.
But what if it isn’t just using math, but statistics and the numbers themselves? Don’t worry, there are a huge array of jobs here as well. The key is to find the perfect fit and area for you to apply your love of statistics and data. For some people, finding an industry you are passionate about can put you in the best place to succeed in your career.
Overview of Careers in Data
We can’t go through all of the numerous careers working with data, but we did want to point out a few different career paths – they typically leverage many of the same skills but rely on slightly different skill-sets and experience.
Here are a few quick definitions to give you a basic idea, but many people find that being exposed to different areas in statistics and data can help them narrow down where they want to focus.
- Statistician – if there is a problem, their job is to help figure out the best way to gather accurate and relevant data – they would define the method for data collection and gather the data. Think of this as more of the data research.
- Data Analysts – The data analyst’s main goal is to curate and use the data provided by the statistician to help drive decision making.
- Data Scientist – Similar to Data Analysts, the goal of the Data Scientist is to make sense of data - Data Scientists, however, are usually working more with ‘Big Data,’ machine learning, and computer science.
- Business Analyst – This one often causes confusion because it sounds like it would be closely related. However, while there are certainly some related elements, a Business Analyst is much more focused on understanding business processes, how changes will impact a business, and communicating all of this with stakeholders. They do use data to inform their understanding of any change requirements, but their job is much less focused on the actual data gathering or analysis and more on the business processes themselves.
What is Data Analysis?
Numbers and statistics are great…but what do they tell us? We can collect and organize all the data we want, but if we can’t appropriately draw meaningful conclusions that drive action, they aren’t really all that helpful.
This is the crux of Data Analysis – we need to take data captured in the past and use it to draw conclusions that can help inform our decisions and give us insight and answers.
Consider this example: You are a baseball coach and want to determine which batter should be at the ‘top of the order.’ The prime candidate would be the one better at getting on base.
A statistician would figure out how to observe and put their skills into quantifiable form. They may look at two players over the span of a season and figure out, for example, what percentage of the time does each player get on base?
Let’s say their data show the players On Base Percentage (OBP)*:
Player A: .300 OBP (30% of the time they come to the plate they get on base).
Player B: .400 OBP (40% of the time they come to the plate they get on base).
By this measure, we can see that Player B has a much better chance of getting on base – by looking at this statistic and drawing a conclusion, we just participated in Data Analysis. This is an incredibly oversimplified exercise, but this type of conclusion is at the heart of what analyzing data is all about; figuring out what the numbers show us.
*On Base Percentage (OBP) = (hits + walks + hit by pitch) / (at-bats + walks + hit by pitch + sacrifice flies)
If that example is right in your wheelhouse because you love sports and data, it is an exciting time for you! Sports Analytics has become an indispensable part of how professional, and even college teams, are now built and run. Largely brought into the public consciousness through the 2011 movie Moneyball with Brad Pitt (and based on a 2003 book by Michael Lewis with the same title), this field continues to grow as more teams see the potential unlocked by using data to improve and build a sustainable team.
Teams now analyze nearly every single aspect of every game and player performance. Taken by themselves, some of these statistics might not seem all that helpful – most casual viewers will not wonder exactly how many feet away from the hoop an NBA player is on every shot or how frequently an NHL ‘dump in’ results in offensive control of the puck. This is where our analytic skills come in.
Once they compile these huge amounts of data, they need to make sense of the data and make sure it is useful to them. An NBA team may find a certain player is not effective outside a certain range for jump-shots. Or an NHL team may realize they maintain much more control when they ‘carry’ the puck across the blue line into the offensive zone than ‘chip and chase.’
This is a great example of how people with a love for statistics and math can follow their passions to pursue a career that is fulfilling, successful and exciting!
Moneyball: Sports Analytics Summer Program at JKCP
If the sections above interest you, this summer camp opportunity may be great for you! Our weeklong Moneyball: Sports Analytics camp at Villanova University, can help you turn statistics into important discoveries and find, often unexpected, conclusions. The camp was designed for talented high school students who love statistics/data and sports.
As it covers Statistics 101 to make your background is rock-solid, it exclusively uses data and examples from the sports world. This is a great way to learn about a specific career option, or to learn more about statistics in a setting and format that you will likely not find offered at your high school.