Data science is a career field poised for massive growth. According to the U.S. Bureau of Labor Statistics, “Employment of computer and information research scientists is projected to grow 15 percent from 2019 to 2029, much faster than the average for all occupations. Job prospects are expected to be excellent.”1 As most careers are becoming integrated with data application and data research, the next generation of workers will need to acquire data analytic skills in order to be successful and effective communicators in their profession.2

Teaching data science in high school will set students up for success for their future as we see data analytic skills as an essential part to many career paths. Data analytics is going to continue to improve and be used by companies to reflect on what is being effective and also on what changes could be made. Employers are going to expect their employees to be able to have a basic understanding of data analytics and to be able to interpret, analyze, and solve problems using data sets. It’s crucial that high school students are given opportunities in the classroom to practice these skills to prepare them for the workforce that soon lies ahead of them.

Bridget Zingale is HubSpot’s Global Director of Analytics and has answered a few questions related to the importance of teaching data science in high school.

Q&A With Bridget Zingale: HubSpot’s Global Director of Analytics

Q: How do you use data to make decisions in your profession?

A: Data informs almost every decision I make! Data is truly just a collection of stories. A data point typically represents an event or action, and that event or action is often something that happened somewhere to someone, or a reflection of a choice someone made. Of course, the process to access and understand these stories is notoriously cumbersome. Unlike other forms of storytelling, data comes to us in the form of millions of rows of data, or amorphous blobs of JSON. Data science, broadly speaking, is a tool we use to translate data into a language more suitable for decision-making. It allows us to understand all those stories that came before, so we can determine what we want to repeat, what we don’t want to repeat, and allows us to build forward-looking models that show us what else might be possible. I personally use data to build algorithms that surface content our customers might want, to understand how the leads in our database feel about our brand, and adjust our messaging accordingly. I use it to create goals for our Marketing and Sales departments so that we continue to grow as a company. And I use it to create our company-wide Demand Plan, which helps us understand what’s possible in the years to come, (and how to get there).

Q: If you are not specifically a data scientist, do you think it’s still beneficial for people to know how to read and interpret data?

A: Absolutely yes. The volume of data is only going to increase, our ability to access it will continue to improve, and the potential applications are endless. Because of this, I also think expectations for many careers will shift to involve a certain level of data literacy. I think we will end up in a future where the idea that you don’t need to be a writer, but you need to know how to write will expand to include the idea that you don’t need to be a data scientist, but you’ll need to know how to use data to inform your work. This is especially true for jobs in the finance industry, banking, medical fields, technology, and software. But I would also include the creative industries in that assumption! Digital/data-driven art is popping up more and more, art sales have experienced a lucrative spike in art hosted on blockchains (NFTs); music theory is quite similar to data science, as it relies on understanding and building off patterns; the list goes on!

Q: Do you believe the practice of data science is growing and increasingly relevant?

A: Conceptually, and as a need, I think it’s always been hyper-relevant! What’s happening now is a vast improvement in how we can both teach data science, thus expanding the skillset in our society, and apply data science across an increasingly broad range of use cases. My team and I joke that the word ‘analyst’ is synonymous with the word ‘human,’ we’re a highly analytical species naturally. If you pause to think about the questions you ask during a day, the connections you make around new information, the way you dissect any problems that might come up in order to find a solution, these are all analytical skills! The advances in data science are just allowing us to apply those skills in different ways, and tackle bigger problems.

Q: What specific topics related to data science would you encourage high school students to become familiar with? (ex. Collecting, cleaning and validating data; Interpreting data, qualitative vs. quantitative data, methods of data collection such as interviews, surveys, focus groups)

A: Data science work truly requires a broad skill set range. The first area I would focus on is more around ‘analytical thinking’, learn how to ask questions, check your biases, and understand how to present information with appropriate context. The next area that I would focus on is math (and statistics). And listen, I know math is not always a favorite subject! But I can’t stress enough how critical those skills are. You’ll definitely need to understand data processing, which is where most of your examples above fall under. And finally, you need data skills! Learning coding languages like sql, python will allow you to extract and query data. There are a lot of great free resources. I actually recommend joining kaggle.com and trying out their free challenges in this area.

Data Science Curriculum for High School

EVERFI: Data Science Foundations is a 101 course dedicated to providing high school students with the skills and knowledge they need to accurately evaluate the ROI of data science education and career options. This foundation course will introduce students to what data science is and why it matters in four modules. It will cover foundational knowledge including collecting, visualizing and understanding data. The course will also include an overview of industries that use data science, employ data scientists, and contain an exploration of the role of data science in daily life.

Online Course Modules (approximately 30 minutes each; optional offline lesson resources available):
Module 1: What is Data Science?
Module 2: Collecting, Cleaning and Validating Data
Module 3: Analyzing and Visualizing Data
Module 4: Reporting and Acting on Data

Learn More

1https://www.bls.gov/ooh/computer-and-information-technology/computer-and-information-research-scientists.htm
2https://www.discoverdatascience.org/resources/learning-data-science-in-high-school/