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Artificial Intelligence (AI) & Machine Learning (ML)

Author: Aditya Kumar is a senior at Wootton High School in Maryland. He is considering pursuing a computer science degree with a focus on AI. He learned about how to make and train a simple model using python and how different kinds of models could be used for different tasks. Intrigued by the myriad of applications that AI has to offer and extremely excited about the capabilities of AI.

In this document, Aditya shares his experiences and recommendations on what you can do in high school to become a data scientist or pursue your career in Computational Modeling and Data Analytics (CMDA). AI is one of the most in-demand technologies of today's job market. Artificial Intelligence is not our future - it’s the present. Hence, learning AI for students has become a necessity.

For High Schoolers

What courses they should take in high school? What can they do to prepare academically?

A good foundation of Math, Statistics, and programming language like Python, R, Java, basic data structure, and algorithm design courses will be very useful courses in High school. Artificial Intelligence is a very big field, and it encompasses sub-fields like NLP, Speech Recognition, Computer Vision, Robotics, etc. These subfields that I just mentioned are applications of Machine Learning, which basically is nothing more than applied statistics and probability. Thus, to begin to understand any of the above areas, one must start with basics -- probability theory, statistics, linear algebra, optimization, and information theory. Mastery of these 5 subjects, in my opinion, is essential to understand and appreciate the theory behind the fields such as Natural Language Processing (NLP), Computer Vision, and Machine Learning in general.

How to get AI/ML volunteer opportunities as a high school student?

Sharing your skills and talents to benefit the lives of others or supporting non-profit organizations and try to make the world a better place is a noble and time-worthy personal pursuit.

Many opportunities exist in the field of data science as it plays an important role in all aspects of modern life. You can participate in many events or short to long term data analytics or data science projects or collaborations run by some well-established organizations that are serving to advance a good cause:

· DataKind (U.S.) and Data for Good (Canada): Offers global volunteer opportunities for data science and analytics consulting, project management, event planning, and more.

· Data Science for Social Good: Reviews and scopes data science projects needed by “social good” organizations, then connects volunteers with the projects.

· Catchafire: Provides a place for nonprofits to advertise skill-based volunteer needs, including data analytics projects.

· Statistics Without Borders: Organizes data professionals to help international causes through their statistical expertise.

· United Nations Volunteers: Recruits volunteers from all backgrounds to assist projects supporting peace and development worldwide — currently including opportunities focused on spatial/GIS and data visualization

How can they make themselves a “well qualified” candidate for a good undergraduate Data Science program?

Get involved in volunteering work, internships, research on AI or data science related fields. Read research papers from top tier sources like Google Brain, FAIR, Microsoft research and DeepMind. In addition, Udemy, EDx, Coursera, and Udacity are great online learning resources and have a vast range of classes, especially in programming, AI, and computer science. Find a university that teaches a good data science curriculum. Get involved in the Open-source Opportunities where even if you are new to data science and coding, it’s worth your time for the lessons you’ll learn, the contribution you’ll share with the world, and the experiences you can add to your resume.

What extracurricular opportunities should they look for? How can they find out about these experiences?

There's no single answer for what extracurricular activities to look for, other than look for clubs you're interested in, and be well rounded. In addition to your school science clubs, find ways to get involve in the scientific research projects, especially during the phase of data collection and data analysis and learn programming languages such as R, Python and Data Structure. Students can also participate in Hackathons and Competitions such as Kaggle competitions, DrivenData, Devpost , HackMIT, Artifical Intelligence Summit Idea-thon, etc.

What summer programs should high school students look for? How do they approach research and acquiring positions?

Start looking for internships in places like Google Brain, Facebook artificial intelligence research (FAIR), Microsoft Research, or DeepMind. Have some online presence by blogging, writing, and/or making videos concerning basic machine learning. Build your own ML stuff in your spare time and open-source the work on GitHub. Network with other people in ML using Quora, Reddit, or Twitter, and reading their questions is a great example of how you can gain knowledge in ML during your less stress full time in summer.

Choose your own data adventure during summer to build your skillset. There are plenty of challenging problems out there that you can address, even if you prefer to operate independently or can’t commit to a competition or organization. Come up with your own project and find publicly available data that could help you address it in some way, whether through modeling, data visualization, app building, data storytelling, or any other approach. For example, data sources related to COVID-19 and to issues around racial injustice etc.

What should they consider when applying to undergraduate Data Analytics/Science programs?

As big data is everywhere, and almost every industry is being reshaped by the need for data-driven decision-making. Because it’s still an emerging field, employers are looking for individuals who can responsibly evaluate and interpret big data.

Wonder if data analytics is right for you? If you enjoy identifying patterns and using your communication skills for effective collaboration, now is the perfect time to consider a major in data analytics. To be successful in the data science field, you’ll need an understanding of science, mathematics, and statistics, a foundation in computer science, and experience with complex data sets. For many students who want to study subjects like data analytics and computer science, check out the following link – for the top undergraduate computer science programs for data analytics, data science and ML. Any college /university you’re considering to apply for should be able to tell you how their program. Be sure to ask questions that help you determine if they produce well-rounded students.

Is research experience necessary to get admission into a good undergrad Data Analytics/Science program?

Research experience is very useful if you’re considering applying for an undergraduate data science program. During the design strategy phase of the Data Science related research project, students typically tend to find answers to the following two primary questions: What is happening? Why is this happening?

While collecting and analyze the data, they must devise a scale by which they will evaluate the data they have received, to decide what indicators will be and will not be very important. A good research design predicts challenges and includes a plan for each step of the process which is very important.

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