Why Getting a Masters in Data Science was the Best Decision EVER!

Why Getting a Masters in Data Science was the Best Decision EVER!

How an MSDS helped me land my Data Science Job

In May of 2018, I graduated with an undergraduate electrical engineering degree. Even though the program was rigorous and the concepts were very informative, I was not convinced that it was the right path for me. As I looked back to the four years at university, I recalled all the research labs I was a part of and the various projects I worked on. I realized my favorite moments were in front of a computer, programming in Python, C++, or MATLAB, and sifting through all the data collected. At the time Machine Learning and Artificial Intelligence were all the rage. As I was researching the topics of ML, AI, and Data Science, I could see myself working in this space.

Having an engineer’s mindset, I needed to know how every component in a system worked. I needed to know the advantages, the risks, and the various use-cases. When I looked for resources online to learn Data Science and Machine Learning, all they offered was a quick 3-week to 3-month curriculum that just went over the methods to implement ML algorithms in Python or R. There was very little emphasis on the actual math and design behind these systems. It was at this point I decided to go back to an academic institution to learn Data Science the proper way. I enrolled in the 2-year Data Science Master’s program at the George Washington University in Washington, D.C. because of its access to federal Data Science opportunities and its emphasis on neural network engineering and design.

My 2-Year Curriculum

As you can see above, each semester built on top of the previous one. The first two semesters spent most of the time teaching students how to wrangle data, manipulate it, and model it. Courses such as Data Mining, Data Warehousing, and Data Visualization really focus on this. Other courses like Applied Statistics, Pattern Recognition, Advanced Time Series, and Machine Learning were very math-heavy and made sure each student was able to design and describe a model without code before moving onto the programming lectures.

The second year focused on the “fun stuff”, in my opinion. At this point, students were exposed to frameworks such as TensorFlow, PyTorch, Keras, Caffe, OpenCV, and NLTK. Using cloud instances from AWS, GCP, or Azure, students were taught how to build, improve, and deploy elaborate Neural Networks, CNNs, RNNs, LSTMs, and Autoencoders.

Key Takeaways From Earning My MSDS:

1. Companies want Data Scientists with Graduate Degrees

   We have all encountered the ultimate catch-22: getting your foot in the door when most entry-level jobs require prior experience of up to 3 years. According to Glassdoor , most companies are “looking for candidates with 5–7 years of industry experience and/or has a Master’s or PhD”. These days, companies are willing to use projects in lieu of industry experience. The beauty of earning a Master’s in Data Science is the ability to hit two birds with one stone. After 2–3 years through an MSDS program, you leave with a Master’s degree and roughly 4–5 projects that are original, well-thought out, and highlight your progression in Data Science.

2. Invest in Your Future

   You don’t need to tell me that grad school is not cheap. Trust me, I know. However, I knew that if I wanted to be competitive enough in the data science field, I needed to go back to school. It was a tough decision, and at the time I didn’t have a job. I worked at a coffee shop and was a paid graduate research assistant. Any other free time was dedicated to my classes. In the end it all paid off when I landed a dream Data Science job at a top consulting firm. Everyone wants a high reward with little to no risk, but with the competition growing every year, people need to be willing to spend the time and money to gain a competitive edge.

3. Put the Science back in Data Science

   Based on my experience testing out various Data Science certificates and online courses, many instructors focus on just teaching students the “easy way”. There usually is no regard to looking at scientific journals, advanced mathematical concepts, or research techniques. While I was in the MSDS program, I had the amazing opportunity to work on a research project with my professor and publish to a scientific journal. It wasn’t until I started my job that I noticed how much a ML project is based on the scientific method. It’s all about staging a hypothesis, testing it out, and tuning to optimize.

Was it Worth it?

In short, absolutely! Data Scientists are in demand now, but companies are expecting a high caliber of programming, business, and leadership skills. Submitting myself to a 2-year program where I focused only Data Analysis, Machine Learning, and Deep Learning shaped me into the Data Scientist I am today. I do not know what the future has in store for me, but I am certain I have a strong foundation to take on any challenge in the data world.