SAS has always believed in the power of education, but in today’s data-driven economy, it’s more important than ever to ensure our students are introduced to data science at an early age. We as a company are focusing our resources on creating student experiences in data literacy, computer science and artificial intelligence — the foundational components of data science.

For many students, and perhaps even teachers, data is conceptually siloed into math, popping up only briefly in other subjects. However, the use of data drives innovation across domains, and students need experiences that demonstrate this real-world value. Like reading, data and computer science should not be seen as single disciplines, but rather an interdisciplinary tool for unlocking innovation across areas of our lives.

Here are five things all students (and teachers) should know about the field of data science. You’ll notice a common bottom line: Follow your passion, and have some data science in your back pocket.

## 1. Data science is math in pursuit of innovation

Let’s sort out the difference between math and data science. Math is where we hear students continually asking: “When am I ever going to use this?” That’s not a question we ever hear when students begin learning how to read. With reading, we teach students foundational skills early in their education and then quickly transition to application, asking students to use this skill to learn across disciplines. It becomes obvious why reading is important outside of school. Math rarely gets the same treatment, and it should because math is foundational to data science, and data science is foundational to understanding the proliferation of data in our connected world.

In short, data science combines math with context in order to solve problems and drive innovation. Without context, data is just a bunch of meaningless numbers.

The key takeaway: There’s data in just about everything that we do, and when contextualized properly, humanity becomes smarter, more efficient, and more innovative.

Good news, you don’t have to love math to be a data scientist. Why? Because computers do.

In fact, computers are really good at doing the tedious math you might find boring or confusing. However, computers have a hard time doing all the things humans are really good at. When it comes to problem solving, computers cannot replace the human ability to collaborate, empathize, think critically, reason and apply. The human ability to look beyond the numbers is the reason we can respond quickly and effectively after a disaster or understand why COVID is disproportionately affecting minority communities. Turning data into information we can use requires human passion -- passion in the form of curiosity and domain knowledge.

The key takeaway: Learn how to make computers do the grunt work while you do the innovating.

## 3. Get paid to “speak both languages”

Companies are looking for change makers with industry knowledge who can push the needle of progress. Because data and technology are in everything we do, we're in desperate need of people who are knowledgeable in the areas they work in, but also can interpret data to create new knowledge. These individuals can “speak both languages” so to say.

Without domain knowledge, data scientists can’t fully represent the problem they’re trying to solve.

• Where are the pain points?
• Where do you look for data?
• Who are the industry experts?
• What are potential solutions?
• What has worked well in the past?
• What does this finding mean for different groups of people?

Effective problem solving starts with understanding the problem, then applying techniques and strategies (such as data and computer science) to work toward a solution. In fact, you can't know if you’ve successfully solved a problem without collecting some sort of data.

The key takeaway: People who have domain knowledge and analytics knowledge and can combine the two are particularly sought after in today’s job market. Almost every company, no matter the industry, benefits from having a data scientist on staff and generally compensates them handsomely.

## 4. Innovations fail (or worse) without diversity

Without diversity, our systems fail. At best, they are useless and obsolete. At worst, they divide us further. Data-driven innovations are only as successful as the data upon which they are built. Applying computer vision to automatically detect skin cancer, or using natural language processing to process commands for digital assistants will not work if the models at their core are built using only light skin tones or native English speakers.

Unfortunately, we’ve seen the effects of such failures play out recently. Joy Buolamwini’s powerful AI, Ain’t I A Woman spoken word piece is just one influential warning for the future, and there are others. Tools created to streamline hiring and recruiting, finish your sentences, predict employee satisfaction, and advertise to you on Facebook are just a handful of applications rolled back due to unintentional bias.

The key takeaway: We need YOU to be part of the solution. We’re all consumers of data-based technologies; make sure you're represented in the development process.

## 5. Data can be bad, but also be very good

You've watched the Social Dilemma, right? We get it. Data has earned a sordid reputation as being used for manipulation and violations of privacy. This is an unfortunate truth, but one that emphasizes the importance of data literacy. Even if you don’t apply data science in your career, people will try to use data to manipulate you. Make sure you have the foundational skills to be a critical consumer of data in the media and throughout your daily life.

Or, go one step further and commit to using data for good.

For every example of data being used for malice, there are several examples of data being used for good—we’ll venture to say great! From conservation efforts to disaster response to the COVID crisis, data is at the forefront of our progress in creating a safer, more sustainable world. Start small. Consider these elementary students using data to have a more successful book drive for their local community.

The key takeaway: Consider how you will use data to solve the issues you’re most passionate about.

## Providing opportunities to do more with data

At SAS, just as we believe in the power of data, we equally believe in the power of education. We believe in creating educational opportunities for all students that demonstrate the relevancy of analytics. Our new focus on data literacy, computer science and AI, focuses on foundational skills and data-driven community service that position analytics as the key to solving the world’s most pressing problems.

Follow along as we roll out big announcements in 2021. You can also join in now as we celebrate computer science education week all season long. We can’t wait to make a difference with you.

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