Table of Contents
Introduction
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.
DTSC-691 Capstone
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.
Database Project
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.
Proposal
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.
Final Impressions
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!
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!
Hi Ezichi,
Great stuff! Really glad you’re enjoying the program as much as I did.
Dustin
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?
Hi Porcelina,
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!
Dustin
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.