Summer is ideal for high school students to take advantage of their vacation and enroll in a dedicated professional skills training program. Students with an interest in technology, who are looking to find work in industries like business or finance, who want to work with statistics or who want to carve a niche for themselves in specialized fields may want to consider learning data science skills. As more and more industries are beginning to take advantage of big data, learning how to wrangle, query, and interpret this data is a highly in-demand skill. Plus, many data science training programs are beginning to emphasize machine learning and artificial intelligence programming, meaning that students enrolled in these courses can stay on top of the cutting-edge of new technologies.
1) NextGen Bootcamp: Python Data Science & Machine Learning
Students interested in an immersive and robust data science training program should consider enrolling in NextGen Bootcamp’s Python Data Science & Machine Learning Summer program. In this course, students will learn how to program using Python, the most popular programming language in the world and one of the most essential languages in data science. Python is one of the easiest programming languages for new coders to learn, so this course is an ideal entry into the world of computer science. Students will learn how to write, read, and compile basic Python code in the first half of the course, and they will begin learning how to work with large datasets. Then, once students are comfortable with basic Python programming, they will learn how to write conditional statements and queries essential to working with datasets.
In the second half of the course, students will learn how to use the major Python libraries of Pandas, Matplotlib, and scikit-learn. These are dedicated data science languages, and they allow programmers to use Python code to organize, query, interpret, and visualize their data. Students will work with hands-on projects that guide them through the basics of building and interpreting complex datasets using Python, and they will learn how to produce clean and evocative data visualization projects like graphs and charts that communicate information about their data. Finally, students will learn the basic fundamentals of using Python to write machine learning algorithms and the functions of artificial intelligence programming. By the end of this program, students will have a solid understanding of all of the major topics in data science, and they will be prepared to continue their Python and machine learning training in additional professional development courses or as part of a college computer science curriculum.
Students regularly praise the depth and breadth of the content offered in this course. One student notes that “we covered something new every day, which made me feel that I was exploring a lot about Python, ” while another said that the “class really helped to improve my programming skills and knowledge! It covers a good span of topics ranging from vanilla Python programming to machine learning.”
2) NextGen Bootcamp Computer Science Summer Certificate Program
Students looking for a more complete computer science summer education can enroll in NextGen Bootcamp’s Computer Science Summer Certificate Program. This course combines NextGen’s Python Data Science and Machine Learning program and its SummerJava Programming course, providing students with a comprehensive introduction to computer science and the world of programming. These are two of the most accessible programming languages for new programmers. They are the two languages most commonly utilized in high school AP computer science programs and introductory college computer science courses. This beginner-friendly program is an ideal place for students to learn basic computer science concepts such as how to read code and write large programs, and the skills you learn in this course will be transferable to almost any computer science-related field you enter.
In this course, students will begin by learning how to write and read code using the Java programming language. Students will learn the differences between procedural and object-oriented programming, and they will learn how to write programs using both. This will give students an important foundation for building their skills as they learn more complex programming languages. Java is also an important language for software engineering projects, so learning it also has practical applications. Then students will learn how to write code with Python and apply that programming knowledge to data science tasks, like writing complex queries or using Python libraries to visualize data. By the end of the program, students will have a robust knowledge of Java and Python programming and be prepared to continue their training at a collegiate or professional level.
Students praise the accessibility and beginner-friendly nature of the course. One student writes that the course “has laid out an easy-to-follow path for me while also allowing me to learn so much in just 15 days.” Another student called it “a great class for coding beginners who want to learn the basics of Python.” This is an ideal course for anyone interested in learning computer science who is unsure where they want to start.
3) Digital Media Academy: Data Literacy in a Global Society
Some students may not be interested in diving headlong into a Python-heavy training course at the start of their data science education. These students may want to consider a course that explores the more practical and theoretical aspects of working with big data, like Digital Media Academy’s Data Literacy in a Global Society course. In this class, students will learn about the logic and processes that underscore computational thinking and ideas surrounding concepts like automation. After learning about the basics of computational logic, students will receive instruction in data literacy, including lessons on where data is most commonly utilized and how these new data tracking and analyzing processes are changing major aspects of our everyday lives.
This is a good course for students to take before enrolling in a more advanced data science course, particularly if students aren’t sure where they want their data science training to take them. Students enrolled in this course will learn how data shapes the global economy and how it affects everything from global politics to our media consumption habits. This course offers students an introduction to the practical, social, and ethical questions that surround big data and data analytics, and this education will serve as a vital foundation for the kinds of work that students are tasked to do with data if they pursue their education further. This course offers self-paced lessons for students and teachers and a certificate of completion for students who pass the program.
4) iDTech: Artificial Intelligence and Machine Learning Certification with NVIDIA
One of the most exciting developments in data science is the rapid development of machine learning and artificial intelligence technologies. Students interested in this subfield of data science may want to consider enrolling in a course like iDTech’s Artificial Intelligence and Machine Learning Certification with NVIDIA course. Machine learning programs allow machines to read incredibly large and complex datasets (for more than a human being could read in a lifetime) and then interpret that data to find trends and patterns that would be invisible to human operators. This technology is primarily used today in content recommendation systems for services like Amazon and Netflix, but the development of new algorithms and inexpensive processing power has made it so that the technology is poised to quickly find a home in a range of different fields and industries. Learning how to work with Python for machine learning early on in your education can help you lay the foundation for working on the cutting edge of this new subfield of data science.
In this class, students will receive hands-on instruction in using Python for data science and analysis. Students will learn the basics of programming queries and database functions, and how Python is utilized in machine learning projects. Then, they will receive an NVIDIA® Jetson Nano™ neural network processing device so that they can begin training machine learning algorithms. In the second half of the course, students will learn how to write the programs that teach their Nano to recognize images and sort and organize them based on their training. Through this process, students can see exactly how complex machine learning algorithms learn from data and how they can more effectively control what kinds of information the neural network engages with. This training is a great foundation for students wanting to engage with more advanced AI projects later in their computer science careers.
5) UConn Pre-College: Data Science
Many colleges and universities offer high school students the opportunity to earn college credit and learn in a collegiate setting in summer session courses. The University of Connecticut, for instance, offers a pre-college Data Science program for students interested in learning Python and R programming. This course uses simple and complex games as a means of making data tracking and analysis clearer to students and students will learn how to program their own sample games using R. This training will help students get hands-on experience in computer programming and data science, making this course an ideal entrypoint into the world of data analytics. Students will learn important data science principles and they will learn the practical aspects of statistical analysis, domain knowledge, and computer science.
This program is taught by members of the UConn mathematics faculty and aims to provide students with an understanding of college-level statistical analysis. Students enrolled in the course will receive hands-on, practical training in math and computer science, and they will learn these skills in a college classroom environment. Once students are comfortable with their knowledge of data science, R and Python programming, and statistical analysis, students will begin working on projects that allow them to apply this knowledge outside of the scope of the classroom. Students will also begin learning how to use Python to visualize their data, giving them the tools to communicate their findings in a way that is easy for a general audience to understand. This course doesn’t provide UConn credit, but it is an ideal place to start learning college-level mathematics over the summer.
6) Columbia University: Big Data, Machine Learning, and Their Real World Applications
Students interested in big data and machine learning will likely want to understand how they can apply their training to real-world problems and scenarios. Courses like Columbia University’s Big Data, Machine Learning, and Their Real World Applications offer students a chance to learn the practical applications of data science techniques, a particularly relevant skill given the increasing relevance of data analytics in almost every facet of everyday life. Students will learn the basics of computer programming, and they will receive training in using computer applications to manipulate, export, transform, and visualize their data. They will also work with machine learning technologies and learn how algorithms are trained and utilized in big data projects.
This course focuses on teaching students how to analyze and interpret data using real-world, practical projects that mirror the kinds of data collection projects that are becoming increasingly common in the world of business. Students will learn how to use data to produce actionable business insights. Students will work with newly emerging technologies to learn how big data can give them a leg up in almost any professional endeavor, and they will learn how to make sound, data-driven decisions based on the use of these technologies. This course has no formal prerequisites, but it is recommended that students have a familiarity with algebra and statistics and a basic understanding of computer programming.
7) Practical Programming: Python Machine Learning Immersive
Practical Programming’s Python Machine Learning Immersive course is an ideal introduction to the skills students will need to program and utilize machine learning algorithms using Python. In this course, students will learn how to apply their knowledge of Python programming to write logical and linear regression models that allow machine learning algorithms to function. Students will also work with the statistical concepts that allow machine learning algorithms to create accurate models and forecast information based off of their existing datasets. This course will also cover concepts such as bias and variance to help students create more accurate and useful models for their algorithms to utilize. This process is vital, since even the most complex and detailed machine learning algorithm won’t be of much use if the data that is going into the system is inaccurate or the people responding to the forecasts aren’t trained to read data.
This is an accelerated, remote learning program, and the course is open to students 18 and older, so high school seniors and incoming freshmen college students should consider this course. This program aims to provide students with the tools they need to use machine learning algorithms in practical, real-world scenarios and the hands-on instruction that students receive in this course is geared towards business and data analytic processes. Students are expected to understand Python programming and be familiar with Pandas and Matlib. The course will touch on subjects of advanced computational statistics, but students will not be required to have a background in mathematics to succeed in this course.
8) Syracuse University: Data Visualization and Analysis
Syracuse University offers an on-campus, pre-college Data Visualization and Analysis program open to high school students entering their sophomore, junior, or senior years or students who graduated high school in 2025. In this program, students will learn how to transform raw data into actionable insights and rhetorically effective visualizations. This is a hands-on program in which students will learn how data is collected and how experts analyze the quality and accuracy of this data in advance of more complex analytics practices. Students will learn how to use data analytics tools to make sense of massive datasets and how to read and interpret them, starting with instruction in using Microsoft Excel. In addition, students will work hands-on with software applications like Tableau to learn how to transform unintelligible data into persuasive and clear visualizations (such as charts, maps, and graphs) that can be used to communicate their findings to nonspecialized audiences.
Course time is divided between hands-on lessons in workshops and lab time in which students collaborate on group projects. These hands-on lessons will help students get practical experience working with the kinds of datasets they expect to find in a professional setting. This course is ideal for students to start mastering practical data analytics skills and learning how to use industry-standard tools like Tableau. This week-long summer session is taught by Syracuse University professors, and students will have access to the technology labs available on campus. This course can be taken for transferable college credit, and the course comes with a university certificate of completion.
9) Pacific University: Sports Analytics Camp
One of the reasons that data analytics is becoming such an in-demand professional skill is that so many different industries are tailoring data analytics techniques to gain a competitive edge in the marketplace. While many courses focus on the use of data analytics in business and finance, some courses focus on more narrow applications such as Pacific University’s Pre-college Sports Analytics Camp. In this program students will learn how to apply data analytics and advanced quantitative methodologies to the business and competitive landscape of professional sports. Students will work with experienced data analytics professors and professionals and will have the opportunity to meet and work with experienced industry experts. Students interested in the application of data analytics techniques may be interested in a course such as this one, since more and more fields and industries are following suit and leveraging unique data analytics techniques.
In this course, students will learn how to apply data science to the field of sports analysis practically. This includes lessons in using advanced analytics and metrics to predict player and team performance and how these quantitative results can be used to inform long-term decision-making. Students will also learn how these skills can be applied to other elements of the sports industry, including collegiate athletics and scouting, fantasy and esports, and fan engagement strategies. Students will also dedicate time to learning about the career paths that exist in sports analytics, offering them the opportunity to learn what kinds of further skills they will need to hone to find a career in the industry. This course is an ideal fit for students interested in working in the professional sports industry or students with interest in data science who want to explore some of the more specialized places in which they can find work.
10) Code Connects: AI and Big Data
Code Connects is an online tech education provider that offers summer computer science training programs for students entering grades nine through to graduating seniors preparing to enter college. Their AI and Big Data course offer students the opportunity to receive a comprehensive data science education that focuses on the uses and applications of machine learning and AI technologies. Students enrolled in this course will learn through hands-on exercises, including one in which they train their own machine learning algorithm in image identification processes. Students will learn how to program using Python and the scikit-learn library, and they will learn how to program basic machine learning algorithms to interpret large datasets. This is an ideal introduction to the world of complex artificial intelligence programming, and the course promises to set students up for long-term success in their future Python training.
This course will also introduce students to important data science concepts pertaining to working with large datasets. Students will learn how data is collected and aggregated, and they will learn how machine learning algorithms process and interpret data using various programmed logical statements. Students will also learn how data scientists work to ensure that the data that they provide to a machine learning algorithm is free from bias and being utilized ethically. Students will need to consider these questions as they pursue their data science education, particularly as big data becomes increasingly essential to the operation of major industries like advertising and finance. This course offers a solid foundation in machine learning and data science for students looking to build their skills in preparation for more training in this cutting-edge subfield of computer science.
Why Learn Data Science over the Summer?
Summer seminars can be a great way for students to learn specialized skills that they may not be able to learn in their regular high school classes. Not all schools offer computer science courses, let alone something as specialized as data science, so students interested in learning how to build machine learning algorithms, for example, may stand to benefit from taking a dedicated summer course. These classes let students receive focused instruction in the specific topics that interest them the most. Given that there are so many different data science training programs available to students, it shouldn’t be hard to find the summer data science program that fits your needs.
In addition, data science jobs are in high demand, so if you are considering pursuing a career in computer science, analytics, or business, it is useful to get an early start on your training. A summer data science program will likely introduce students to Python programming and data analytics tools, so students will have a leg-up as they begin more advanced training programs like college-level courses or on-the-job training. This early training can provide long-term benefits to students since they will become accustomed to working with Python and other programming languages. Since many of these programs offer college credit or equivalent levels of training, students will also find it easier to succeed in their introductory level courses (and sometimes even bypass them entirely).
Learning data science over the summer is also a great way to prepare students for applying for college degree programs if they are confident that they want to study computer science. Not only does completing one of these programs provide students with vital foundational knowledge, but it can demonstrate to college application boards that a student is invested in their education and are serious about pursuing advanced data science as an educational path. This can be the edge a student needs to make themselves more competitive for getting into their top choice of schools.