Online Master’s in Data Science — Worth taking the plunge?
By: Matthew Bitter
Tags: Masters Degree, Data Science, Online Masters Programs, Coursera, Course

Regardless, I found the program VERY helpful 👍 and I learned a great deal. It really is what you make of it in terms of how deep you want to go into a topic. The only caveat ️I would mention is that if you want to get into deep learning specifically or you have a very specific technical position in mind then I would suggest going into a more tailored boot camp type of program and not doing a formal master’s degree⚠️. The reason I suggest that is because this degree did not go into great detail on the state of the art of neural networks but just covered the basics, most of which you could find in a good tutorial online. However, if you want to know the foundational stats and algorithms behind these concepts that you will use throughout your whole career then a program like this is what you are looking for.

Data

The data used for this analysis was gathered by my fellow student manually. He carefully documented how long it took him to complete each assignment, lecture, exam, mid term, quiz and project throughout his whole journey. So huge kudos to Paul for collecting all this! 🙌. I have his consent to publish the results.

Results

Overall, the degree took approximately 1,133 hours to complete. Eight courses were required, and I assumed a 12 week term in calculating the hours per week.

Image by Author | List of courses taken with hours

Here is the same data broken down by Task Type. Not all courses had final exams, mid-terms, projects or quizzes. However, all courses had assignments and lectures.

Image by Author | Bar chart of courses

Plotting hours spent doing assignments versus hours spent in lecture with a 30th percentile horizontal line we see that most of courses had over 20 total hours of lectures. Distributed Systems took close to 40 hours of lecture time while Data Cleaning took less than 10 true hours of lecture. This will be discussed later.

Image by Author | Scatter Plot of Assignment v.s. Lecture time /w Lecture percentile

Conversely, with an 80th percentile vertical line we see that the majority of courses had less than 90 total hours of assignments. Applied Machine Learning course is a clear outlier 📊. That specific course had close to 170 hours of assignments. Why Applied Machine Learning had significantly more time spent on assignments will be discussed later.

Image by Author | Scatter Plot of Assignment v.s. Lecture time /w Assignment percentile

For fun, I trained a clustering algorithm where the number of clusters does not need to be specified like OPTICS or DBSCAN and got the chart below. Each colour dot corresponds to a specific cluster. Applied Machine Learning (AML) and Practical Statistical Learning (PSL) seem to be in their own clusters.

Image by Author | Scatter Plot of Assignment v.s. Lecture time /w Cluster Number Legend

Discussion

Firstly, the disparity between time spent completing certain courses, for example Data Cleaning versus Practical Statistical Learning (PSL) is directly related to the complexity of the content. Specifically, Data Cleaning mainly covered theories such as data flows, database design and RegEx which are generally known by folks in the analytics profession already. Therefore, most of the videos can be watched on 2x speed which is why the hours spent in lecture is comparatively low. It does not mean that there are only 10 hours of lecture 😌.

PSL went into the guts of how specific clustering, classification and regression models worked. These more complex concepts called for re-watching of the lectures multiple times to fully understand grasp the topics to be able to earn 100% on the quizzes. Moreover, there were four projects in PSL in addition to all the other assignments and quizzes. 💪

Both courses required skills in either R or Python however they covered totally different areas.

Image by Author | Bar chart of courses with data labels

Secondly, we saw that Applied Machine Learning course required a VERY high time investment on assignments. There was no final, no quizzes and no project just A LOT of assignments. Personally, I LOVED this course because each assignment felt like a mini-Project and you had no idea how you were going to figure it out in time 😅. But in the end you did and it was great!

Thirdly, I consider Data Visualization and Data cleaning the “easy” courses. You could take two or three other courses in the same semester while working full time and not go completely insane 😤. I did not do that but many of my other classmates did.

Conclusion

If you are considering pursuing an online Master of Computer Science / Data Science degree then I would highly recommend it. This degree program was very technical compared to similar programs that include law, privacy and research design courses in their curriculum. I feel that a more technical degree translates better to an online format compared to master’s degrees that focus more on social/qualitative courses for obvious reasons.

The degree was still a lot of work as you can see from the hours spent 😭. The silver lining is that not all courses take up every waking moment of your life to complete. There are “easy” 😺ones and there are killer 💀ones, as long as you know what you are getting yourself into you can come out at the end of it a better person.

Lastly, I want to also mention that you should take into account the opportunity cost of doing other things for those 1,133 hours you will be spending on your master’s degree. The good thing with an online technical masters compared to an MBA style masters is that they are typically significantly cheaper 💰.