Table of Contents
- UC Berkeley
Data Science education can come in a variety of forms, whether that be anything from online certifications, boot camps, or online tutorials, they all are justified and reputable paths to learning Data Science. For this article, I will be discussing the top five graduate programs in Data Science. These Master’s programs are from traditional four-year universities that have for the most part, recently, have added a Data Science degree to their brochure. I have already discussed other forms of learning Data Science, and I will have that linked below at the end of this article if you would like to learn about other forms of learning. The programs I will be discussing below all have beneficial aspects to them that can help you to ultimately land a role in Data Science.
Southern Methodist University, also known as SMU , is where I went to study Data Science. Although bias, I am not affiliated with their program, or for any of the specific programs below for that matter. I want to include this school as I have experienced it first hand; while the other schools are ones that I have either applied to or researched so I have learned more about their programs. The main reason I specifically enrolled at SMU is that it is a reputable school, regardless of Data Science, in the state of Texas. While it might not be as well known in other states, I think it is important to go to a school that is near or close to the state that you will be working in. This reason is not only for reputability, but also for making connections, both personally, and in terms of networking. Apart from the program being great in general, I can say that I am a Data Scientist now, and SMU is one of the main reasons I was able to get a job in the Data Science field. Now, let me discuss why it will be beneficial for you to attend and complete the SMU Master’s of Science in Data Science program.
The mainly online program lasts for 20–28 months and includes 33.5 credits; 21 core credits, 9 electives, 1.5 immersion experiences, and 2 capstone credits. The program also benefits from having two areas of specialization that you can choose from, which are Machine Learning, and Business Analytics.
Here are some of the main courses that you can take at SMU:
- Statistical Foundations for Data Science
- Applied Statistics: Inference and Modeling
- File Organization and Database Management
In the statistics courses, you can expect to become proficient in experimental design, analysis of variance, ethics, and communication of results. For the second course, you dive deeper into multiple linear regression and variable selection, as well as unsupervised learning, along with linear and quadratic discriminant analysis. What makes these two statistics courses unique is the focus on communicating what the results mean in a non-technical setting. I cannot stress enough how important it is to relay all forms of technical Data Science methods to any audience. You can assume most people at your company will not know what a p-value is, so although you will know, you need to practice and be able to know how to explain it to a variety of different coworkers so that they learn, or create action from your results. I believe that and other communication is what SMU does particularly well.
Here are the electives of Machine Learning specialization:
- Machine Learning II
- Natural Langauge Processing
- Data and Network Security
For the main program, you will take Machine Learning I no matter what, but for the specialization in Machine Learning, you will take a second course that focuses on supervised classification, as well as unsupervised classification, and deep learning, and is overall, more technical. You can expect to practice these concepts in Python and R programming languages. The program lists Matlab, but I do not remember that being the main language used, so you will focus more on Python and R instead. For Natural Language Processing or the NLP course, I believe I learned pretty much everything that is NLP, or as much as one can in a one 3 credit course. This course covers text classification, clustering, tagging, taxonomy, corpus analytics, and semantic query analysis. I have used a lot of these techniques in some of my Data Science jobs, so I can attest this course is particularly beneficial to enroll in. The Data and Network Security course is a unique course that I have not seen at most Data Science programs, which can be a pro or con depending on what you want to focus on in your future career. The course includes topics like ciphers, hash algorithms, and secure communication protocols. I did enjoy this course a ton and it was a nice break from typical Data Science learning, but I have not used it yet at work — although the concepts and questions of user authentication, privacy, and ethics of security are something I keep in mind.
If you find yourself more interested in the less technical aspects of Data Science, and more focused on the business side of Data Science, then you will want to specialize in Business Analytics. The good thing about this program is that you can have a base knowledge in Data Science, but focus on business, so if you want to be in a different role in the future like Director of Business, you will have the edge of knowing how to implement not only business, but also Data Science processes.
Here are some of the electives of the Business Analytics specialization:
- Business Analytics
- Time Series Analysis with R
The Business Analytics course focuses on ARIMA (auto-regressive integrated moving average), SARIMA, forecast modeling and comparison, as well as economic concepts. For Time Series Analysis with R, you will focus on models with serially correlated observations, multivariate time series analysis with VAR, as well as neural networks. Completing this course would definitely give you an advantage if you choose to be a more business-focused Data Scientist or as a Business Analyst.
- Capstone — similar to a thesis, this is your final project that includes a few others a part of a group project led by a professional in the topic that you are studying. You will also publish a paper in SMU’s Journal of Data Science — certainly looks good on a resume, as well as a unique experience to learn and become proficient in a specific topic. The paper I published was on Fake News detection.
- Immersion — an experience unique to SMU (in terms of who you will meet and workshop with), is the immersion where you will learn more, meet with your classmates (in person usually), and see the campus in person. It consists of a conference along with the presentation of your capstone project. You will present it in front of business leaders, Data Science leaders, your classmates, and your professors.
- Live Classes — since this program is online, they want to ensure that you are getting as close to an in-person experience as much as possible. That being said, you have live classes with schedules with usually about 6–12 people per class. This method means you will be participating actively, presenting, discussing, and immersing yourself in the course with others and the professor. Sometimes you will go in break-out rooms so you can dive deeper on topics with 2–4 people. The professors are usually the same ones that are teaching earlier in the day on campus, so you can expect your courses to be in the evening, which is a pro if you have a current job that you want to keep while studying.
As you can see, SMU’s program is very inclusive of all of the facets of Data Science. I can truly say that I have used most of what I have learned there in some form in real-world, professional use cases.
The University of California at Berkeley  was the other program that I was thinking of enrolling in (more so than the others below), ultimately choosing SMU over it of course, but in no way is it any less. It may be more beneficial for you to attend here instead or at the next programs that I will discuss. This specific program is named a little different, Master of Information and Data Science. Similar to SMU, it is online. This program is flexible with three main paths: accelerated, standard, and decelerated. Depending on what you want, either can be particularly beneficial — for example, if you have a current job or children, a slower path might be a better approach so you don’t spread yourself too thin.
Accelerated: 12 months
Standard: 20 months
Decelerated: 32 months
These options serve as a great way to explore your short versus long term goals.
Here are some of the courses from Berkeley:
- Applied Machine Learning
- Behind the Data: Humans and Values
- Natural Language Processing with Deep Learning
The first course here is encompassing of general Machine Learning topics and will help you get your feet wet with running models and discussing results, with a focus on big data. Next, is the course that focuses on legal and ethical issues of Data Science. I find this course to be quite unique because most programs do not dedicate a whole course to the ethics of data in Data Science — which is incredibly important. Lastly, the NLP course focuses on Deep Learning, which is also quite unique — some other programs I have explored do not have this added learning.
Benefits of Berkeley
- Capstone: similar to SMU, there is a Capstone that serves as your main project over the course of the program, summarizing all that you have learned that can also be applied directly to professional Data Science.
- Immersion: also similar to SMU is their immersion, you can participate and engage in workshops and network with industry leaders.
- Unique Course: their Humans and Values data course is quite unique, beneficial, and an interesting mix of topics.
There are a lot of similarities to Berkeley’s program when compared to SMU. The biggest difference is of course the school itself, and the focus on legal and ethical implications.
This program resides in the Data Science Institute of Columbia University for their Master of Science in Data Science degree . They actually have a Ph.D. specialization in Data Science as well, which is quite unique. The program breaks down the courses in the following disciplines:
- Computer Science
Within those disciplines, there are courses pertaining to Data Science.
Here are some that are unique courses from Columbia to highlight:
- Algorithms for Data Science
- Data Science Capstone and Ethics
- Topics in Operations Research: Personalization Theory and Application
- Topics in Quantitative Finance: Big Data in Finance
As you can see, Columbia has a ton of unique courses to choose from. Overall, I do believe they are the most encompassing of Data Science — with a focus on Computer Science and programming.
The Big Data course includes topics of algorithmic trading, while the Theory course is based more on personalization systems, recommendations — behavior-based and content-based.
You will also see that there is a Capstone course similar to the previous two courses. It depends on if you want to study in-person or online. Either way, you cannot go wrong with this program.
The Master’s in Data Science degree  has more credits than most programs consisting of 43 total. The program has two main focuses, including statistics like biostatistics and computational skills like Computer Science. The University of Michigan defines Data Science as a combination of Computer and Information Sciences, Statistics Sciences, and Domain Expertise. Therefore, these are what the Data Science courses are based on. The courses are related to the four following facets:
- Expertise in Data Management and Manipulation
- Expertise in Data Science Techniques
There are unique and tried-and-true courses as well in this program, some of the ones I will highlight include the following:
- Information Retrieval and Web Search
- Case Studies for Health Big Data
- Adaptive Signal Processing
- Advanced Signal Processing
The focus of these courses is signal processing and big data in relation to health. The University of Michigan also is composed of a reputable healthcare system, so it is no surprise that their Data Science program also has a focus on health topics. A unique focus, however, is the signal processing elective that they have that you can enroll in.
The Master’s in Applied Data Science program  at Syracuse University is 36 credits and is usually completed in 2 years. The core of this program focuses on analytics. While they stress analytics, the program also promotes its elective courses which will offer more flexibility to its students, including 12–15 credits worth. The main facets this program highlights are :
- collecting and organize data
- identifying patterns
- developing alternative strategies
- implementing business decisions
- communication skills
- ethical dimensions
The courses include a common core, an applications analytics core, and an electives core:
- Big Data Analytics
- Business Analytics
- Principles of Management Science
- Marketing Analytics
- Accounting Analytics
- Cloud Management
- Internship in Applied Datta Science
As you can see, this program’s courses all include some type of Analytics focus, so if you are wanting to be more business and customer-facing in your future Data Science role, this program is the way to go. The analytics courses focus on survey research, managing and synthesizing data, regression and classification, risk assessment, risk scoring, and anomaly detection. Of course, there is more to include, but these are some of the topics that I thought would be useful to include.
As you can see, there are plenty of benefits of attending a graduate program at the discussed universities. I have written more about SMU as I have attended and completed their program, with the other schools and their respective programs chosen based on my opinion as a professional Data Scientist. That being said, these programs may not be for you, but hopefully, you can figure that out from reading this article as well as taking your own deep dive into the programs. Ultimately, the decision of which graduate school you attend will be up to you. So, the most important factors when choosing a particular school can be summarized by the following:
- school location, even if remote/online
- online vs in-person
- amount of unique specializations
- type of specializations
- duration of program
- if remote, then if courses are live or not
The schools that I discussed are all excellent options and here are all of them summarized:
Southern Methodist University: Master of Science in Data Science (online)University of California, Berkeley: Master of Information and Data Science (online)Columbia University: Master of Science in Data ScienceUniversity of Michigan — Ann Arbor: Master’s in Data ScienceSyracuse University: Master of Science in Applied Data Science (online)
I hope you found my article both interesting and useful. Please feel free to comment down below if you have research, enrolled, or completed any of these Master’s programs. Do you prefer one over the other? What is your focus in Data Science — will it be Business Analytics or Deep Learning?
These are my opinions, and I am not affiliated with any of these programs.
Please feel free to check out my profile and other articles, as well as reach out to me on LinkedIn.
Here is my article outlining the top five Data Science certifications  if you would like to learn more about some shorter ways to learn Data Science:
 Southern Methodist University, Online Master’s Degree in Data Science,(2021)
 UC Berkeley School of Information, The Online Master of Information and Data Science from UC Berkeley, (2021)
 The Data Science Institute at Columbia University, Columbia M.S. in Data Science, (2021)
 Regents of the University of Michigan, UMich Master’s in Data Science, (2021
 Syracuse University, School of Information Studies, (2021)
 M.Przybyla, The Top 5 Data Science Certifications, (2020)