Staying relevant to students while preparing them to be successful in the future is the winning formula that all teachers hope their curriculum achieves.
This includes more than just teaching students the facts. It’s also about finding ways to teach that build additional, valuable skills they’ll continue using long after your class is over.
Utilizing data science in education is one of these methods. Data is everywhere, and it’s relevant to just about all things.
Even if it’s not immediately apparent how data impacts a given discipline, just conduct a survey; responses = data.
Data can play a crucial role, especially in the areas of science and math.
Still, first, you must decide what data to use, think about how it relates to your curriculum, and form a strategy that allows you to collect the information you need efficiently and from reliable sources.
The first questions to ask about data
Looking into teaching data science tilts toward the overwhelming if you focus on the data itself.
Instead, setting goals related to working with data in general, rather than the sheer volume of it, is a more productive approach.
No matter what discipline, students should be able to:
- Collect data
- Organize data
- Make sense of what the data tells them
- Communicate its impact to others
To get to this point, it’s a good idea to look at the big picture of data science.
What is data science?
Data science education utilizes both science and math skills in conjunction with each other.
It combines statistics, scientific methods, algorithms, and scientific computing to teach students how to extract specific information from seemingly unstructured piles of data.
What is data in math?
Data is pretty much everything in math— if you consider data to be nothing but numbers.
Yes, it’s more complex than that, but from a mathematical perspective, data is a collection of facts (numbers, measurements, etc.) that can be collected and analyzed.
What’s a data scientist?
Learning about data and how to use it is essential for various careers.
This person uses data to inform decisions and understand aspects of the world around them.
What do data scientists do?
Since the demand for data scientists is increasing, familiarity with data and interpreting it is an excellent skill for all students to have.
For those who decide to become data scientists, typical tasks include looking at data to pull out patterns and insights, creating predictive models, and answering the big questions a company may have.
The effects of data science education in the classroom
A person’s ability to analyze and collect data is becoming so essential that the primary effect of including big data in education is that it prepares students to be more successful as they age.
The specific datasets may correlate to what you’re teaching in class, but the ability to work with the information, in general, helps students build invaluable skills that stretch across all disciplines.
Day-to-day, teaching data science enhances the classroom experience, creating yet another outlet to engage students and get them into science.
Since data is constantly changing, it’s a way to keep things relevant in the classroom, which helps students connect what they’re learning to the real world.
Using data also breaks the pattern of learning a science standard in the same old way.
It enables students to get involved, to discuss the data (and the science) while learning how to analyze existing information and synthesize their own.
Working with data helps students refine their critical thinking skills as they interpret and understand data and hypothesize how to collect it when what they need to know isn’t readily available.
How datasets connect math and science
Mathematics naturally integrates with science when it comes to data, too.
Even on the most general level, you’re bringing together statistics with some form of science.
It’s something you see every day out in the world, and mirroring it and making connections in the classroom makes it more applicable to students.
For example, checking the weather. When you see a 47% chance of rain, a meteorologist uses statistics to give you the scientific probability that you’ll need an umbrella.
Math + Science.
Other data science applications can include assignments at just about every grade level.
Teaching data analysis to elementary students may involve collecting data about each other, like tracking how many students have brown, blue, hazel, or green eyes, then analyzing that data to tell you the most common eye color in the class.
If they make a graph of their results, even better.
Using this same information as they get older, students can calculate the percentages within the class of each eye color occurrence and guess what eye color a new student would have.
Data science for high school students can get a little more complex.
You can challenge them with real problems out in the world that need data to solve.
You can provide them with the datasets to analyze and evaluate or require them to go out and collect information independently.
Students would have to write a survey and ensure a certain number of their peers responded before even beginning to think through a solution to the question at hand.
The importance of high-quality data
Regardless of what specific data is necessary for any given project, a valuable thing to teach students is the difference between high-quality and “dumb” data.
It’s the same for students working on a research paper — distinguishing between facts and misinformation.
There is a significant difference, so when talking about the integrity of data, make sure to stress that it should be:
- Curated from reliable (primary) sources in usable chunks - you don’t want to encourage students to grab a little data from here and more from there just to tell the narrative they want to tell.
- Connected in a logical way that’s not necessarily obvious - if you’re looking at what factors impact ice cream sales, you wouldn’t necessarily want to look at how temperature correlates. Everyone knows people like cold food when it’s hot outside.
- Interesting to the class. Data can help people learn, but only if it’s something they really want to know. Think about what’s age-appropriate and relevant in the lives of your students.
- Current and comprehensive - while you can’t always find recent data to answer specific questions, at some point, you’ll go too far back to really feel like the information is still reliable. Ensure students aren’t only using the first dataset they find but also investigating when it was collected and how thorough a job was done.
Challenges of teaching data science
Handling data correctly and teaching students the importance of curating quality information, no matter the assignment, helps combat common challenges associated with teaching and learning data science.
It removes the risk of poor-quality data interfering with results and helps narrow down the amount of usable data that’s really out there.
What it won’t account for is time— the most valued asset in education and the hardest one to get enough of.
Collecting data, especially quality data, takes time, and factoring in that commitment to your curriculum isn’t always easy.
One way to make it easier is to assign a data-driven project.
Maybe one week, students craft a survey to collect data or decide what research they want to do.
They can keep working on this portion on their own.
The following week is about collecting and organizing data in a usable way.
The third week analyzes the data to find what truths it reveals, and the fourth week is for presenting what the students found.
Breaking things down like this means less time is taken in a single batch to work on this project, and you can keep students engaged in the process longer.
This becomes even easier when the assignment connects to your students' interests and what’s happening in the local community.
Data science education is an essential Part of 21st-century Learning
There’s no way to limit the sheer volume of data out there. The trick is to look at data science education as a way to teach students vital skills.
This includes how to curate data on their own and work with it. It’s not necessarily about you doing all the work for them but giving them the tools to handle data appropriately.
Even starting at an early age, teaching data science will prepare your students to succeed throughout life, no matter what career path they eventually pick.