How Federal Employees Can Get Training for the Best Job in America

The best job in the United States is machine-learning engineer, according to job posting site Indeed.com. With an average salary close to $150,000 and a whopping 344% growth in job postings, these engineers are in high demand throughout the country. 

Including stock compensation, “A.I. specialists with little or no industry experience can make between $300,000 and $500,000 a year,” according to The New York Times. 

Beyond that, the federal government needs trained data scientists to help sort through one of the largest repositories of information in the world. Every agency relies on these specialists to compile and analyze data that affects everything from major national infrastructure plans to disaster relief in the face of hurricanes and wildfires. 

So how can aspiring data scientists prepare for this lucrative field? A master’s degree from a good university is the conventional route, but the challenges are daunting: They accept a fraction of applicants, charge steep tuition, and take years to complete. 

For those with a background in computer science, there are good options for instructor-led training in Washington, D.C., and online. These often include certification to help advance your career without the huge investment of a university degree. 

Nothing beats getting hands-on training from some of the smartest data scientists around. Programs such as NobleProg, GTC DC and Data Science Dojo range from entry-level overviews to focused, multi-day workshops on specific applications.

While in-person training is ideal, it’s not a necessity. Plenty of world-class organizations, including New York University, Stanford University, Massachusetts Institute of Technology and Harvard University, offer online courses that walk students through courses in AI, machine learning and deep learning. These programs allow aspiring data scientists to engage in serious study in their own homes.

• Coursera offers a variety of classes in AI, machine learning and data science, in collaboration with partners like AWS, IBM and NYU. The most popular is an 11-week course through Stanford University focused on machine learning. Topics include supervised and unsupervised learning, recommender systems and deep learning. Students also learn how to apply algorithms in robotics, text, computer vision, audio and other areas. Students can earn a Coursera Certificate credential that confirms successful completion of a course.

• edX, founded by Harvard and MIT, is a nonprofit educational platform that offers more than 2500 courses across a range of disciplines from Harvard, Microsoft, Columbia, and other top institutions. In addition to free introductory classes in AI, robotics, machine learning, and deep learning, edX also offers more advanced courses with professional certification like the HarvardX data science program. 

• NVIDIA’s Deep Learning Institute offers instructor-led workshops, self-paced online AI training and certification. The program includes a variety of low-cost training courses covering data science, computer vision, object detection, image classification, video analytics and healthcare. 

• Udacity’s School of AI offers classes on a number of subjects such as Intro to Machine Learning, Deep Learning, Natural Language Processing and Artificial Intelligence. It also offers free classes to get started, including Intro to Artificial Intelligence. While students can choose their own set of courses, Udacity also offers Nanodegree programs that have a pre-established course curriculum and syllabus.

As our connected world continues to produce more and more useable information, the federal government and large companies need data scientists to help sort, process and put that data into action. From cybersecurity to healthcare to transportation and infrastructure, data will become the key to solving some of the world’s toughest challenges. Training options like these will help more engineers get up to speed quickly, while advancing their career potential.

Kirk Borne is the principal data scientist, data science fellow, and an executive adviser at Booz Allen Hamilton.