"I joined the program with an engineering degree, wanting to switch career tracks. The class progression in the program built a solid foundation for me. The first year classes were a good mix of theory and practical experience. Through ProSeminar I landed an internship that let me work on an open-ended problem. That internship, which was on computer vision with deep learning, was the reason I got my first job as a machine learning engineer. After that, I continued to learn and grow. Now I will be starting a new job at Aurora as a software engineer working on data analytics systems for self-driving cars, but my background in statistics and machine learning were cited as a distinguishing factor."
"Think deeply about what future career / job roles you want to pursue and tailor your learning to meet those goals. If you are interested in an industry job, make sure your coding skills are sharp and you can talk confidently about your past projects. Definitely apply for internships -- not only will you get experience that can help you land your dream job, but you may be given a return offer if you impress. Finally, consider taking some cross-disciplinary classes in CS since they often teach commonly used tools and techniques. Some examples are clases on deep learning, cloud computing, HPC, or algorithms and data structures. Even if you want to do a pure data science job, you'll probably be interfacing with engineers. Some shared understanding is always good to have. Finally, learning is a lifelong experience. Don't be afraid to ask questions and learn along the way!"
Statistical Science