10 Ways to Learn Data Science as a High School Student
Are you someone who needs a lot of information before making a decision? Do you love critical thinking and drawing conclusions from large sets of data? If yes, you may be a good fit for data science, which is one of the fastest growing fields today! Spanning across a diverse range of interests that you may have, data science will be relevant and crucial – artificial intelligence to precision personalized medicine and even self-driving cars. It is a great time as ever for you to start learning data science to prepare for your college and career paths.
Data science is a highly compelling field for anyone, including high school students to explore because they can apply it to so many topics that interest them - whether that's analyzing trends in sports statistics, gaining insights from social media data, or using machine learning to program a robot. There are lots and lots of free resources and student-friendly data science competitions and courses available. Making the most out of these will help you work on hands-on and engaging projects.
What is Data Science?
Put simply, data science is the field of study that combines statistics, computer science, and critical thinking to extract insights, trends, and patterns from data. If you work as a data scientist, you use computational tools to analyze millions of individual data points and uncover hidden patterns and trends. The ultimate goal is to gain data-driven insights to make smarter, evidence-based decisions while solving complex problems.
Data Analysts use skills and knowledge across so many different areas like statistics and probability, programming, and computer science. Data analytical skills are becoming increasingly relevant to machine learning as well, where millions of historical data points are analyzed to make predictions of future trends.
Why Should You Learn Data Science in High School?
Given that data science is an ongoing area with constant advancements, high school is the ideal time for you to start learning. By developing data skills early on, you can easily give your profile an edge for college admissions and future careers. Data science is one of the fastest growing and highest paying fields, and experience with data can open up many opportunities, both during and after high school.
There are generally two broad approaches to start your data science journey - the top down and bottom up approaches. With the top-down method you start working on projects and learn as you go. You get into the depths of theory and math once you have played around with some hands-on projects. With the bottom-up approach, you start by learning the foundations and then get into the practical applications of the subject.
The top-down approach is definitely a more exciting way to learn and makes the journey quicker and easier. There are many free, virtual, and comprehensive courses and resources to help you learn this way. So to get you started, we have compiled a list of 10 ways you can learn data science in high school!
Take online courses in data science and begin building your technical skill-set
The most streamlined way to learn data science is to take courses and classes about data science. Luckily, there are plenty of great courses available that will allow anyone of any background to learn data science or bolster their skillset even further. We have listed a few of our favorites below.
Coursera's Applied Data Science with Python Specialization
Cost: None, $49 for certification
Time Commitment: 7 - 10 hours per week for 20 weeks
Audience: Beginner python knowledge is necessary
This specialization course on Coursera consists of five parts that target all aspects of data science. The course covers the introduction to data science with python, then moves onto data visualization and machine learning, and concludes with “text-mining” and social network analysis. The outcome is a skills-based certification that shows your strength in data analytics.
This course is pretty comprehensive and free to take, only needing you to pay for a certificate. You will likely need to spend around 7-10 hours per week to follow along with the course, finishing the whole specialization in 5 months.Udacity’s Data Science Nanodegree
Cost: $400 a month
Time: 4 months for completion
Audience: Beginner Python and SQL knowledge needed
Udacity provides one of the most popular and comprehensive courses in data science. This four month program covers every aspect of an introductory data science course. The course covers the beginning of data science, data visualization and manipulation, and practical statistics relevant to data analytics. However, this course does not cover machine learning unlike some of the other courses, but it does introduce students to more logical and statistical knowledge. This program is much more like a virtual course, where you have access to real-time support and office hours, real-world projects, career services, and personalized feedback.
Udacity estimates that it takes the average student four months to finish the course and the cost is $400 a month or $1400 for an upfront package. However, Udacity offers personalized offers for everyone depending on their income, allowing you to avail the course at a reduced cost!EDX Data Analysis Essentials
Cost: Free
Time: 6 weeks, at 4-6 hours per week
Audience: Beginners with an interest in business
With a unique business oriented data analytics course, the Imperial College of London on edX offers a course on the fundamentals of data. It is specifically designed for those interested in pursuing an MBA in the future. With very low requirements and an average time commitment of 4 hours a week, this course can serve as a short, but comprehensive overview of data science as applicable for business and management.
The course covers data visualization and modeling and trains students on how to draw conclusions from data and make decisions based on it, which is crucial for any aspiring computer science or business student. If you are just looking for a course purely focused in data science and not its diversions towards business, then other courses may be more applicable.DataCamp
Cost: $300 per year
Time: User dependent – many different courses and skills depending on what you want
Audience: Anyone
DataCamp is one of the few platforms that strictly focuses on data science education. It is a great choice for students just getting started and for those who already have experience in the field. DataCamp trains you in introductory data science and upskilling. It has specialized programs for specific skills like SQL, data processing, statistics, finance, and machine learning, and a virtual workspace to run your own projects. The yearly cost is $300 for the entire platform, but the company often offers discounts.IBM Data Science Professional Certificate
Cost: Free
Time: 5 months at 10 hours a week
Audience: Beginner
Sponsored by IBM, one of the world’s leading tech corporations, this course is great to kickstart anyone’s data science and machine learning journey. It’s a particularly good fit if you have no prior experience with data science. The course specializes in the use of Python to learn what data science is, experimental methodology, SQL, machine learning, statistics, visualization. It ends with a capstone project that requires you to make use of all the key skills you learn through the course. The course consists of 10 parts spanning five months and is completely free and beginner friendly!Google Data Analytics Professional Certificate
Cost: Free, can pay for certificate
Time: 4-6 months at 10 hours a week
Audience: Beginner to advanced
Another corporate sponsored certification is by Google, in collaboration with coursera to create a series of courses in data science and analytics for anyone. The course is suited for you if you are looking to explore concepts in data science or upskill. Unlike most courses, this one offers programming training with R, SQL, Python, and Tableau. It also connects you with employers throughout the course. The topics covered span beginner-level introductory data analysis, business intelligence, and advanced data analysis. Note that this course is completely project-based and involves a lot of hands-on learning.
Join a university summer program
Outside of just digital courses, some of the world’s top universities offer courses that allow students and adults alike to learn data science, taught by world renowned professors. The primary difference between the online courses mentioned above and the university online courses that we’re recommending is that these courses will often have live lectures, office hours, and direct relationships with professors, but likely have less flexibility and stricter deadlines. However, if you’re preparing for a college trajectory in data science, joining a summer program hosted by a university may help you prepare for college-level academics better.
MIT Applied Data Science Program
Cost: $3900
Time: Part-time job commitment for 12-weeks, with intense projects and live lectures
Audience: Advanced students
The MIT Applied Data Science program is the one of the most recommended and popular academic data science programs and is directly taught by MIT lecturers. The program is extremely comprehensive, covering data visualization, Python, SQL, machine learning, neural networks, recommendation systems, and more!
Over twelve weeks, you will attend live lectures from MIT professors, and begin developing a deep professional portfolio. The course is updated year-by-year, with a new self-guided module in ChatGPT and generative AI. This program is designed to help you to develop your own capstone project. However, before you join the course, it is important to note that you will be required to have a good grasp over the foundations of data science and have experience with programming languages. MIT also offers their own list on a different set of resources to learn data science, check it out here!Harvard Data Science Principles
Cost: $950
Time: 4 weeks, 4-5 hours per week
Audience: Beginners
Harvard’s unique data science principles course seeks to make data science more accessible and approachable. Harvard’s course specializes in the use of real-world examples and case studies, to analyze the different and diverse methods of analysis, synthesis, and visualization that is possible within data science.
Over the course of nine modules, you will get an introduction to statistical prediction, causality, visualization, data wrangling, privacy, and ethics – a wide range of topics, but all essential and crucial to being a data scientist. It is important to emphasize here that this course is much less technical, but instead focuses on the ability to understand, process, and be creative with data. Therefore, while still being an extremely popular course in data science, if you want to learn coding and the math behind data science, you should look at another course.
Participate in Kaggle competitions to put your skills to the test
Cost: Free
Time: User-dependent
Audience: Intermediate to advanced
Kaggle is the world’s largest community for data science, and hosts a range of different competitions. If you have some data science knowledge or have begun learning from the courses mentioned above, then you should consider being a part of these competitions. It tests your skills in unique scenarios, and allows you to see how your code compares to others. This gives you a chance to see other people’s solutions and work on improving and learning from a passionate community around you. Kaggle hosts beginner friendly events to advanced challenges with prizes. However, while it is open for everyone, having some prior experience would be helpful and give you a competitive edge. This can be a pretty solid addition to your resume as a student, as not only are you learning more about a subject, but also applying it in the real world and solving problems using out-of-classroom concepts.
Participate in hackathons for a competitive edge
Cost: Free
Time: User-dependent
Audience: Advanced
There are many corporations, data science companies, universities, and communities that run hackathons for anyone to participate in. The goal is to invigorate a community of skilled and passionate data analysts and thinkers to solve some issues – some real, some fake. You should keep in mind that hackathons are extremely competitive and require you to have advanced coding skills. This may not be a good fit for you if you are just looking to get started. Popular hackathons include Major League Hacking, Hack from Home, and Hackerearth. You can also check out this post on the computer science competitions for high school students to find a hackathon that interests you!
Prioritize for your learning goals: pick the right programming languages and tools for data science
Seeing how all of these courses introduce you to learning some sort of computational language, it is essential for you to learn how to program to be successful in data science and adjacent fields. The most common languages are Python, R, and Stata. There are many online tutorials and coding platforms to learn all of these languages, such as CodeAcademy. Learning computer science will continue to develop your critical thinking and problem solving abilities, which are easily transferable to the rest of your professional and academic career. While learning these languages, we suggest working on small projects that involve analyzing a dataset to see where your programming skills can be applied and what more you have to learn.
Attend workshops, webinars and subscribe to newsletters
Thanks to data science being such a new and prevalent field, there are lots of beginner-friendly workshops, webinars, and conferences that specialize in data science to 1) showcase new advancements in data science, 2) train beginners to develop their ability to be a data analyst in a concentrated bootcamp/workshop, 3) upskill advanced data analysts, and/or 4) allow passionate data analysts to meet their fellow passionate peers. Here are some workshops and webinars that are digital: Data Science Webinars and Training - BrightTALK, Data Science Webinars, and Online Data Science Events. Alongside a list of data science conferences.
Why newsletters? Staying up-to-date is everything in tech! We highly recommend checking out newsletters such as Python Weekly, KD Nuggets, Data Science Weekly. Some of the news items may be too advanced, and for professionals, but even if you begin to research the different concepts covered in an update, you’ll build up a lot of knowledge overtime.
Follow modern data science experts
Cost: None
Time Commitment: Self-paced and can vary
Audience: For anyone
Regardless of whether you are a beginner or seasoned data scientist, keeping track of modern experts and their work with data science is a good way to stay up to date. It helps you keep in touch with the modern field of data science which is constantly evolving. Some good people to follow include Andrew Ng, Hilary Mason, Kirk Borne, and Monica Rogati. They often tweet, write articles, post about cool new trends, publish their personal work and advancements, and even provide ways to learn data science. For example, this article by Monica Rogati provides an interesting perspective on becoming a data scientist or following the social media of Kirk Borne to constantly keep up to date with new and exciting advancements and applications in data science.
Join data science communities
Similar to finding mentorship, finding other people interested in the world of data science can help you along your journey, whether it is finding a group of like-minded peers, having a ready network of people to ask questions, develop projects, or finding collaborators. This can be a good place to start. Communities that you can take a look at include Women in Big Data, Women in Data, Reddit, Aspiring Data Analysts, LatinX in AI, Black in Data, Out in Tech, or Disability in Tech.
Put your knowledge to the rest by using open source tools
FastAI is an amazing all-in-one platform for the world of artificial intelligence and deep learning, which are fueled by data science. The platform connects you with courses, books, blog articles, up-to-date news, software packages, and more – all resources essential to you in any stage of your data science journey. They aspire to make deep learning accessible and approachable to everyone, regardless of background and skills. This is a good option for you if you are interested in having access to a range of resources. Check out the cofounder, Rachel Thomas, talking about AI and accessibility.
Seek mentorship opportunities
As a high school student, you may require guidance and mentorship on how to get started with data science projects, to learn more, and receive real-time feedback. A good way to go about this is to find a professional in the field, who is willing to support young students and be their mentor. There are traditional methods of finding mentors, from cold calling industry professionals to LinkedIn hunting for willing mentors, but there are new platforms that have risen to connect people early in their data science career to professionals. Veritas AI is an example of this, where students can work 1-1 with mentors on projects that interest them! You can find the application form here. In the past students have worked on projects across domains like healthcare, finance, environmental science, and more.