Table of Contents
I recently finished the MS in Data Science at Eastern University. This is a 30-credit program that is designed to be taken part-time and finished in 10 or 20 months (depending on whether you take 2 courses or 1 at a time.) The program uses accelerated 7-week courses and has 6 terms per year so that you can get trained as a data scientist in a hurry.
I started in January 2021 and I’m slated to graduate in May 2022, pending a passing grade on my capstone. (Update: I passed!) I previously made a post when I first started and another one when I got about half way. This post summarizes my final thoughts of the program now that I’m finished.
The courses that I had not completed at my half-way review included DTSC-600, DTSC-670, DTSC-680, DTSC-690, and DTSC-691.
DTSC-600 Information Visualization
This course I actually took near the end, along with 690. This course has no coding, but instead focuses on Qlik and Tableau. There are two required textbooks:
- Information Dashboard Design by Stephen Few. 2nd Edition. Publisher: Analytics Press
- Storytelling with Data: A Data Visualization Guide for Business Professionals. Publisher : Wiley; 1st Edition
For each module, you’ll do some reading and complete a quiz. Then you’ll create a dashboard using a given dataset (e.g. Australian Tax Revenue or Console Sales) and then answer a second quiz made up of questions based on the dataset.
This course is being retooled, so it will actually include more matplotlib in the future. I can’t comment on what that might look like going forward. I didn’t find this course super helpful because I already have a background in Tableau.
DTSC-670 Foundations of Machine Learning
This was a super fun course, though challenging. There are 9 assignments. The assignments involve doing data cleaning, building machine learning models, building pipelines, and other things that will be useful for the capstone. It uses Python. You can read my DTSC-670 Tips and Tricks article for more information about this course.
DTSC-680 Applied Machine Learning
This is a continuation of 670. A very challenging course, but also really helpful to improve your matplotlib graphing skills and machine learning knowledge. You build a KNN classifier in this course without actually using the KNNClassifier. You also do a complete end-to-end machine learning project without any scaffolding code to guide you.
You do some matrix math (that wasn’t too bad) and you also build an artificial neural network (ANN) using TensorFlow. I haven’t spent much time with either TensorFlow or PyTorch (which is becoming more popular) so this was a heavy lift. I still need to re-watch the 3 hours of video on ANNs in 680 and make sure that I brush up on my knowledge.
DTSC-690 Ethics in Data Science
This was a nice break from all of the coding in 670 and 680. There are 40 readings throughout the term. The median length was 6 but they range from 4 to 8.
For each set of readings, you write one substantive post (I think 250 or 300 words was mentioned in the syllabus) that synthesizes your thoughts for the whole week’s readings and post it on a discussion board. Then you reply to at least 3 students reflections with a similar 250 word post and do that for the 7 weeks.
That makes up about half the grade. The other half of the grade is a presentation on a topic of moral, ethical, or religious significance. My presentation was titled Machine Learning, National Security and Civil Liberties: Technology in Conflict.
Each student gets placed in a group (mine had 8 students not including me) and you need to watch each presentation and give feedback on it, similar to the 250 word posts. The presentation was due part-way through the term, so that you have time to give feedback.
The big fish! This is what all of the program is leading up to. Your capstone. There are three options for doing the capstone right now:
Standard Machine Learning Project
This is what most people think of, when they think of the capstone. You collect some data, complete exploratory data analysis, build a machine learning model, evaluate it, and then create a way to interact with your model.
In the database project, you use concepts similar to DTSC-660 where you build a database, create an ERD and schema, and then write functions in Python to work with that database, and create a way to interact with it.
Data Science in Education
In this project, you build a Jupyter Notebook and at least an hour’s worth of lecture to go with to teach a concept in data science education.
Early in the term (or even before the term starts) you’ll choose which project you want to go with. Once the term starts, you’re assigned a project mentor. This is a previous graduate or professor who will review your project proposal and provide detailed feedback. While they can’t provide specific help with debugging or coding like a GA would, they can provide you with advice.
You can see an example proposal here. This is very similar to the one I submitted. My mentor provided direction, asked questions and helped me improve my actual proposal, which was on a different topic.
After the Proposal
After the proposal is approved, you get to work! You have the rest of the term to complete your project. I didn’t talk to my mentor much throughout the term, but that’s because I felt like I had a strong proposal and so I didn’t need much clarification.
One thing that is important is that your proposal must provide a way for you to interact with your model outside of Jupyter/Spyder. This means using something like Flask. Flask is its own mini-language that took some learning so I would recommend practicing with it before you need to use it in 691.
Submitting the Final Project
The final project consists of three components: your completed code, a project submission (which is similar to your proposal but instead of showing what you would like to do, it shows what you actually did), and then a 30 minute-maximum walkthrough video that goes over your project.
My mentor gave me detailed feedback on my Python. I was really surprised and impressed. He walked through my code closely and made many recommendations. I hope I can be like him when/if I’m ever a mentor in the future!
Now I’m just waiting for my final grade. This is a Pass/Fail course so I’m hoping that I passed and then I’ll graduate sometime next month. Edit: I got an email letting me know I passed, which is the last hurdle. Now all that’s left is to officially receive my diploma.
This program is definitely one where you get out of it what you put into it. I’ve learned so much over the last 16 months and look forward to continuing to build my data science skills. For the price, I think the program is really great. You could complete a bootcamp for $10K and come out of it with the same skills but no credential.
I think students should try to build and maintain a portfolio throughout the program which will increase their ease of job hunting when they graduate. For what it’s worth, I got a job with a significant raise (and Data Analyst in the title) while I was in the program and I attribute that to Eastern.
One more thing: the program continues to change. Courses are being updated each term. For example, DTSC-575 and DTSC-650 are already different from the versions I took, even though I only took them 6 months ago.
They are rolling out new courses and an updated course plan where you only need to take 4 courses required: 650, 660, 670, and 690. Then you’ll take 6 electives. Right now there is one elective being tested now and several more planned.
Lots of exciting things. Good luck everyone!
18 thoughts on “Eastern University MS in Data Science 2022 Review”
Thanks Dustin for this and for all of your reviews and guidance! Eastern University was not even in my radar until I stumbled upon an ad and discovered your blog in my research. I am now half-way through the program and I have gained SO MUCH from it! Good luck on your future endeavors!
Great stuff! Really glad you’re enjoying the program as much as I did.
Hi there! Thank you so much for sharing your experience with Eastern University. I just applied for the program, as I’m eager to change my career to data science and have zero coding skills (but plenty of math skills). I’ve seen a few short posts in Reddit about it which led me to seriously consider applying, but to be able to follow your journey and your experience really helps too. Hopefully these aren’t paid reviews to skew the rest of us 🙂 Just kidding! (or am I?)
One question for you though about your experience getting a job. Is it an unrealistic expectation to land a job titled as Data Scientist following this program? I noticed you said you got a Data Analyst job, but (no offense on this) it seems like Data Scientist has more prestige and often comes with better pay. One comment I see on the bootcamps is from people who are disappointed they couldn’t get a Data SCIENTIST job, but only could land a Data Analyst job instead. Was this your experience at all?
Thanks for reaching out. I can confirm these are not paid reviews in any way, and that I blog about whatever’s happening in my life! It is a bit tricky to get a Data Science job right out of a Master’s or a boot camp. The reason is that Data Science is not itself an entry level job, so the people hiring for them usually want people who have built up the experience, often in a Data Analyst job, where they’ve begun to demonstrate the use of those data science skills before they get hired. You’ll have the skills you need but not necessarily the experience. Doing a Data Science internship can be valuable, though I always had a full-time job so I never did one.
The biggest differentiator between someone who gets hired and someone who does not, is their portfolio, and the associated line items on their resume. If you can ensure that you have a solid 3-5 projects in your portfolio when you start job hunting, this will make it easy for potential employers to evaluate your skills. This takes a lot of time to put together. Since I’m pretty busy, I’ve been slowly building my portfolio. For example, right now I have two projects: my capstone and another project. I need to continue polishing my capstone before it’s ready for primetime so really I only have the one project ready to show off.
In my case, I’m not in a big rush to move to a DS job so I can continue working on my portfolio. For someone who is looking to make that big jump though, I would recommend spending as much time as you can building out projects and making that portfolio. Data Scientist as a title does have more prestige and yes, often pays better.
Hope this helps!
Thank you so much for this thoughtful reply. This context is super helpful. I was only asking to see if your experience was consistent with others. I’m not actually a big stickler for titles, but I just want to be realistic about what to expect after graduation (and building a portfolio)– I mostly just want to shift to a career that is satisfying and challenging. I absolutely love problem solving and my 10+ years as a food scientist in the packaged food industry has been riddled with more politics than problem solving so I am looking for a different way to use my skills. If it’s more realistic that I start as a data analyst, then that’s fine by me! Thanks for the tip on the number of projects to shoot for! Best of luck to you and thanks again for sharing your experience on this blog.
I thought this was an excellent reply to a question that I was wondering, myself. I realized on my last interview that a strong portfolio is key to demonstrating skill and landing a job.
My question about this program is how immersive it is and how confident you feel about using any of these tools or skills once you have completed the program. (I am sorry, that is two questions.) My previous undergraduate degrees were more of an introduction to tools, rather than actual hands-on time with them. With this being an accelerated program that is affordable and designed for the base learner, I am afraid this is what I would be getting again…more of an introduction than an intimate knowledge of the skills and techniques needed to do the job.
Thank you so much for your review. One more question…is there anything you would change about the program or were not so fond of?
The program starts at zero. It definitely gets into more hands-on stuff quickly. You start coding in the second course. There are some big projects in 650, 670, and 680. In 670, the second assignment is a massive data analysis of the millions of rows in the BRFSS dataset. You also get the capstone at the end. There’s programming in virtually every course so you do get the opportunity to get your hands dirty. There is not a lot of theory, only the minimal amount you need to understand what an algorithm is and why you would use one. I felt prepared for a junior Data Science position, though I haven’t pursued one yet. My Python skills were definitely stronger than my R skills.
The one thing I would change has already been done: you no longer have to stick with the 10 courses provided. They were up to 12 last I heard, and adding more. So if you already know basic Python/R you can skip the intro courses to those topics and instead take a course on Advanced Data Manipulation, Solving Business Problems with R, Deep Learning, etc.
Hope this helps!
Thank you for sharing your experience. Would you be so kind to share a link to your portfolio? Thank you!
Hey Dustin, I enjoyed your review of the program. A quick question, how math and statistics heavy is the program? A lot of the classes are named differently, but I see that their description is similar to other stats inference and regression classes from other programs. I don’t want to miss out on learning those topics if I do end up applying. Thank you!
Thank you for sharing your experience, I a really considering Eastern University MSDS program as they are affordable and pretty quick. My biggest concern is to land a job (a descent pay one and potentially the job offer to work remotely) after I got the degree. You mentioned that you ‘entered’ the industry as a Data Analyst during your time in the program. Does this happen a lot or did you do something ‘different’? If you don’t mind me asking, what was the salary range when you ‘got’ this job during your time in the program, also, was it remote? You also mentioned that you got a ‘raise’ after you received the degree, how much of a ‘jump’ would a person expect? Is it hard to land a job, and would Eastern University help in any way in finding a job?
Thank you for doing these reviews. i am looking into this program as well. I had a few questions if you don’t mind.
1. What was your level of programming before starting this program: Python, R, SQL
2. Do you recommend taking elective Intro courses such as DTSC 520 Fundamentals of Data Science and DTSC 550 Introduction to Statistical Modeling? How could someone with basic Python and R knowledge do in these courses.
I agree that Eastern University MS in Data Science is a great program. The courses starts from basic and go all the way to advance machine learning (680 and 691). I have enjoyed doing this degree as well. I find this degree well planned and well organized. The material covered is what most universities cover, some haphazardly some use difficult language to explain basics, but Eastern University professors teach at perfect level and make things easy for students to understand. I will only say watch out for the courses – 580, 670, 680 and 691.
Back in February of 2022, I was looking for a MS in Data Science program that was affordable and taught the skills listed in many of the Data Scientist job postings that I read. When I found the program at Eastern, it seemed to be the perfect fit. After stumbling across your review, I applied right away and never looked back! Today, I’m just 3 classes away from completing my degree, and in a few days I will start my new job, building ML models to create pricing predictions! Thanks for taking the time to share your experience.
Amazing work, congrats on your progress and the new job!
Hi Dustin, thank you for the review! It was very helpful. I am a few years removed from college and while I used maths in college (Economics degree), I don’t use much other than basic calculations in my current job. I also have only been slightly exposed to R, with no other coding experience or exposure. When you say these programs “start from 0”, is it realistic to think of somebody who wouldn’t consider themselves a math/coding whiz can keep up in the programs? Eager to learn more as I think data science and analysis may be able to really help me in my current role. Thanks again for all of the great material.
It’s very realistic for someone who hasn’t done any coding to be successful in the program. It assumes no knowledge of programming languages and starts with the very basics. In 520 you install Python and practice basic things like creating and assigning variables, doing calculations with loops and other really basic stuff. 550 does the same thing with R. 575 has you practice basic Python. 650 gets into intermediate R. The courses all build on each other. It’s hard work but definitely doable, especially if you take one course at a time.
How can you do exam online? Through examity? Can we choose the time for exam schedule?
Or the exam date and time are flexible?
Exams are actually done right inside BrightSpace with no proctoring currently so you can do them 24/7. They previously used Respondus LockDown which was virtual proctoring (it recorded you but there was no need for any kind of scheduling because the video could be reviewed by the professor later.) Hope this helps!