Was My Data Science Masters Degree Worth It?
By: Matt Przybyla
Tags: Machine Learning, Data Science, Education, Work, Learning

Table of Contents

Introduction Data Science Courses Career Summary References

Introduction

I went to Southern Methodist University (SMU) for my Masters of Science in Data Science degree [2]. This decision came to be after working as an Analytical Chemist for a few years. While I majored in Biology and minored in Chemistry and Mandarin Chinese for my undergraduate degree, I always had a focus on studying science along with experiment design in general. As I found myself unsatisfied as a Chemist, I started to look for other careers and what it would take to achieve that respective profession. I looked into data analytics, software engineering, and data science. I had performed similar concepts from all three of these possible careers in my current roles at the time, but I was nowhere near even landing a job interview. To solve my problem, I had to ultimately look for formal education. Below, I will discuss what data science means to me, the courses I took at my university, and if any of this experience was applicable and conclusively worth it today.

Data Science

I have compiled several articles on data science in general as well as the differences and similarities between this field, machine learning, and data analytics, as well as the impact on online learning. Feel free to check out my profile, in addition to those specific articles that I will list below at the end of this article.

From my experience, data science can best be summarized as a field in which traditional statistics and mathematics are automated by using library and packages from tools and languages like:

Python, R, sklearn (scikit-learn), TensorFlow, Keras, and pandas.

While the field is ever-changing, the common themes of problem-solving, programming, statistics, and business understanding are at the forefront of the core of data science. For me, there were possibly a few places that I could obtain all of these skills. I applied for a degree in data science because I did not have an official undergraduate education in it, I wanted to better myself, and to be competitive in a very competitive job market.

Courses

Photo by Max Duzij on Unsplash [3].

Although I will list the specific courses at SMU, most of them can overlap at similar universities and programs. In my program, not only were there various courses, but there were two main specializations as well: machine learning, and business analytics. The first focuses on the coding aspect of data science along with the common libraries used, while the latter stresses the importance of the design of a project and how it will drive business value to your future company with data. I will also highlight which courses I believe are the most beneficial and useful for real-life applications if you desire to find similar courses at this same or different university.

I will talk to the core classes first, then will include the elective classes next.

Core Classes:

Statistical Foundations for Data Science

This is one of the first courses you take because, in order to learn both the fun and fancy algorithms of data science, you will need to have a strong base in both beginner data science and statistics. The key takeaways for this class were statistical sampling, analysis of variance (ANOVA), regression, and communication of results, with the main tools being SAS and R.

I was surprised how great this course was, and I highly recommend studying statistics first before performing any data science. It will also make you considerably more desirable in interviews when interviewers ask you how to develop an ANOVA for example, and you thought you would need to only focus on programming a deep learning algorithm.

Doing Data Science

Another course taken at the beginning of my program, this course stressed how important exploratory data analysis (EDA) is on the first parts of any data science project. To really start using algorithms like Random Forest or K-means, you will need to have a usable dataset that you understand, and EDA helps you to have a strong dataset that you can run models on.

Applied Statistics: Inference and Modeling

This course is heavy in statistics, and perhaps where students struggle the most. The target is not on programming here but on regression, time series, and applicable cases for businesses. A fun thing I learned here was predicting the stock market using an autoregressive moving average model (ARIMA).

File Organization & Database Management

Whereas the previous courses targeted statistics, while using tools and languages like SAS and R, this course covered SQL, Python, and GitHub.

Setting up your data science project does not start with a dataset, but with developing one from querying using SQL.

Machine Learning I

Perhaps the most fun core class, this course goes over all of the common machine learning algorithms, like support vector machines (SVM), multinomial naives bayes, and logistic regression.

Visualization of Information

It is important to learn how to communicate your complicated results to stakeholders, so learning how to visualize can help solve this problem considerably. Most jobs you can use tools like Tableau, Google Data Studio, or even program your own in Python with popular libraries like matplotlib and coding in Java with processing.

Quantifying the World

This course wraps up all of your foundations you learned and summarizes how to wrangle data, clean it, perform EDA, and model your dataset. It is also where you start your capstone project, which at other universities might be also known as your thesis.

Elective Classes:

I will not go specifically in these but they are mentioned on SMU’s site. I would say the most important class here is natural language processing (NLP). In my experience, some data scientists avoid it, but in your job, you will most likely encounter text data and will have to process tons of it. It is actually my favorite facet of data science and can help to analyze documents, classify text, and return sentiment. Here are the elective classes that are included in the program I took:

Cloud Computing

Machine Learning II

Statistical Sampling

Natural Language Processing

Business Analytics

Time Series Analysis with R

Data and Network

Career

To answer the question, was my Masters of Science in Data Science degree worth it? Yes.

The first benefit of this program was that I was able to do it online while I was working. Education can be quite expensive, so I was extremely happy knowing that I was still making money while learning. I did get to do live video communication for every class and attend in-person events, which leads me to the next benefit of this degree on my career. Attending the conferences from this program, in addition to working with my classmates from reputable companies, I quickly built my network of data scientists. One of those conferences was actually a course we had to present on — capstone. It was where I presented along with two other classmates, our topic on how to classify fake news, all because of my degree and its respective studies.

It is extremely valuable having this big of a project and publication on my resume that I can communicate and describe to other data scientists and people who I am interviewing. Because I obtained a degree that focuses purely on data science at a high education level, I was able to land jobs at Indeed, HomeAway (Vrbo), and now where I currently work, a successful startup in Austin, Texas called ScaleFactor. I do not know where I would be without successfully completing this program. It has allowed me to become an educated, professional data scientist.

Summary

While this path may not be the best for everyone for various reasons, I believe that for me, it was the reason I became a data scientist. I learned a ton and made awesome connections and even better friends from it along the way. So yes, it was worth it. I hope you found this article useful and thank you for reading! Please find these articles from me that include the differences and similarities of data science, data analytics, machine learning, as well as the impact and result of learning data science online [5], [6], and [7] respectively below.

References

[1] Photo by Ronni Kurtz on Unsplash, (2019)

[2] Southern Methodist University, Online Masters in Data Science, (2020)

[3] Photo by Max Duzij on Unsplash, (2019)

[4] Photo by Christina @ wocintechchat.com on Unsplash, (2019)

[5] M.Przybyla, Data Scientist vs Data Analyst. Here’s the Difference., (2020)

[6] M.Przybyla, Data Science vs Machine Learning. Here’s the Difference., (2020)

[7] M.Przybyla, Yes, You Can Become a Data Scientist Online. Here’s How., (2020)