The second career

Last week, Gary Kolesar who graduated three years ago came back to Carnegie Mellon to visit me. Since he was an older student, I asked him to share his experience with my students. Following was what he said.

“When I went back to school to pursue a Master’s degree in Artificial Intelligence (AI), I was uncomfortable because I already had a Bachelor’s degree in Business and fifteen years of working experience. Even I had a good job as an accountant in a financial company but I knew that there was not much future in this field as the world has changed and sooner or later, technologies will take over. That was why I decided to return to school for another degree. I planned my second career carefully as I wanted to make sure that I will learn something that can last for a long time. I selected AI because Computer Science and related fields are growing faster and pay more than any occupation.

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To a person who is near forty years old to return to school is NOT easy because you are no longer young and may not fit with others who are in the early twenty. I look at other option before making my decision to go back to school. There is an option for someone like me as I could go online and learn from the Massive Open Online Courses (MOOCs.) This is a great choice and it is easier and faster. Many online courses are short, only a few weeks to a few months and as long as you work hard and follow self-discipline, you will do well. I took a few online courses from edX and Coursera and concluded that people can learn many things from MOOCs and after complete a number of courses, they can get a certificate then go looking for a job. However, MOOCs is NOT for me. I think MOOCs is great for someone who needs to have a job quickly but to develop a career for a long-term, it may not be the best solution. Of course, it is my own opinion as I know somebody may not agree with me.

I decide to follow the traditional path of going back to school for a Master’s degree at Carnegie Mellon. This is much longer and more expensive but having worked for many years, I save enough money for my education. I want to learn things at the deepest level to prepare for my career that could last for my entire life. The reason I choose Carnegie Mellon because it is the only school that offers a program in Artificial Intelligence at that time. After two years studying there, I received three job offers several months before graduation and I accepted an offer from Google. By that time, I knew that I had made the right decision to going back to school.

As computer science students, you already know that most computer programs start with a programming language and basic concepts. Many students do not like the concepts and theories but prefer to learn about technologies as they are more relevant to what they will use in the industry. If you go to MOOCs, you will learn a lot about technologies and how to apply them and you can get a job easily because most of the MOOCs courses are designed that way. They help you to learn how to apply technologies quickly but NOT give you enough time to go deeper on the concepts that you are using. When I took a MOOCs course, I can do many things but still do not understand all the details. In my opinion, traditional schools degrees are focusing more on foundational theories and concepts when online courses are focused more on the technologies and the application. To get a job, MOOCs are good enough but if you want to go further and deeper, traditional schools are better.

Many computer students believe knowing programming languages is good enough to get a job. Of course, most jobs require programming skills but if you want to go further and advance your career, you need to know more about math. In my opinion, math is the foundation of all computations, reasonings, and logic and to work in Artificial Intelligence and Machine Learning fields, you need to have good mathematic skills. When I was a business student, I did not like math but when I went back to school and took the “Introduction to Computer Systems” course, professor Vu explained: “Without a strong skills in math, you cannot develop your computational thinking and be able to solve complex problems in AI and Machine Learning.” At his encouragement, I took several math courses such as linear algebra, calculus, discrete math and statistic and I found that they helped me to learn many abstract concepts and provide me with new ways of thinking about the problems I will have to work in the industry.

To work in the Artificial Intelligence field, you need strong programming skills and understand how to analyze problems, how to collect the right data, how to label them accordingly and know how all the algorithms work. This is why I think every student must develop strong skills in computational thinking because you need to understanding of the problem that you want to solve first. Without knowing the problem well, you will make mistakes and will not be able to get into the solution. You need to know all the concepts and theories about Machine learning so you can select the appropriate algorithm to model your solution because there will be more than one solution to solve but you need to solve them in an automatic way or the best way. That means you will have to break down the problem into many small parts to reduce complexity. That means you will have to decompose a problem into many smaller ones and solve one problem at a time. The solution to one problem can be input to the next level and that is how you go deeper to solve complex problems.

If all you need is a good job, a Bachelor’s degree would be sufficient and most companies are looking to hire graduates with a degree in Computer Science and related fields. However, if you want to work in Machine Learning and Artificial Intelligence field, you need to get a Master or even a Ph.D. degree to go further and deeper to solve complex problems.

Sources

  • Blogs of Prof. John Vu, Carnegie Mellon University

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