University vs MOOC

In mid-2020 as the novel coronavirus lingered, Australian universities began to count the cost of lost international student revenue and hundreds of thousands of mostly services-sector workers were laid off. In an unconventional attempt to encourage reskilling, while bolstering university enrolment numbers, the Australian Government announced a relief package that would provide students with a semester of heavily subsidised, online-only education.

At the time, I had completed a handful of MOOCs (Massive Open Online Courses) on Coursera & edX and was looking for a more rigorous option that would help me progress faster towards a future career in data science, like an intensive bootcamp, or formal university study. In the end I enrolled in a Graduate Certificate of Data Engineering at the Australian National University (ANU).

The choice that had seemed like a steal, however, was complicated by a steady stream of stories that appeared in my feeds, questioning whether the costs and quality of a degree is worth it, i.e.:

Fortunately, courses said to be disrupting higher education are plentiful and many are free. After completing the program at ANU I delved back into the world of MOOCs to try some of the latest offerings: Google’s Machine Learning Crash Course and AWS’s Machine Learning Training. Both these courses are free, with the goal of encouraging future employees to master skills before the interview rather than being trained on the clock.

Below I have highlighted the pros and cons of both formal degrees and MOOCs/career certificates across a variety of categories, and where I see this rapidly evolving trend leading.


This is perhaps the most obvious point of difference between studying at a higher education institution and undertaking a MOOC. Even at a highly subsidised rate of AUD$2500 per semester (USD$1924), less than 25% the standard course fee, my tuition fees were still more than four times a yearly subscription to Coursera Plus or more than double the Professional Certificate in Data Science run by Harvard through edX.

Coursera, edX and many other providers also offer the ability to audit courses for free, although assignments are usually behind a paywall. Financial commitment may be a motivator for many students to see the course through, but one questions whether the anxiety induced by a $100K yearly college debt compared to a $500 Coursera debt might actually have the reverse effect.


If you find it difficult to self-motivate, then the rigidity of the university program may benefit you. Fail to submit coursework by the due date and the 0 you receive will, whirlpool-like, swallow your tuition fees. But love them or hate them, strict enforcement of deadlines is one of the primary definers of a college degree, and the satisfaction and growth you will attain working through challenging assessments (often at 3 AM in the morning!) is more than you will ever get from passively watching course videos.

However, this strict program is not for everyone. Studying online at ANU, I realised most fellow students had entered the program following several years in the workforce and were looking to reskill. Many, like me, were juggling work commitments and home duties caring for young children. Motivation was not the issue, but the lack of flexibility and inability to cater to an older student cohort saw many fall behind and drop out.

If you wish to progress at your own speed, most MOOC providers offer self-paced study with artificial deadlines that can be extended where needed.

Word of caution: Deadline extension has the tendency to become a weekly routine when work, social media & binge-watching Netflix gets in the way

Student Life and Sense of Community

Being able to digest material and overcome challenges together with your peers is one of the traditional strengths of attending university, although this has been complicated with online programs in the age of COVID.

Studying at ANU, logging onto the course forum to find that the question I had in mind had already been asked, and answered, was pleasing. Weekly Zoom tutorials too, provided an opportunity for questions to be answered in person and for limited discussion of the course material, although some tutors facilitated this better than others. But the most productive discussion occurred outside official university channels, in a Discord group created by students who found the forum clunky and outdated.

Would this experience have been different studying on-campus? Most certainly. But in order to cater to those with outside commitments or who are unable to travel to the university during the pandemic, facilitating online discussion and community is a must have, to compete with MOOC platforms that offer active course discussion forums attended by knowledgeable teaching assistants.

Teaching Methodology

Courses in my Data Engineering tended to be PowerPoint driven, with hours of lectures split into individual videos of 10-30 minutes, or at times 60 minutes. Python-focused coding material covered in the lectures was usually not provided as an iPython notebook or Python script, although scripts were provided as a reference in tutorials.

Most courses included weekly textbook readings (although these were usually not essential or examined) and internal or external exercise sets for practice. As with every university course, students worked towards completing assessment items (typically 2-3 assignments and 1-2 exams per subject).

In contrast to university courses which typically run once per semester, MOOCs typically refresh every month or every week. Other courses like those hosted directly by LinkedIn, Google, AWS are permanently available. This means that MOOC coordinators can refine and optimise their content & format based on progress analytics & feedback from thousands of students each year.

Google’s Machine Learning Crash Course offers a preview of the disruption coming to higher education, especially when you consider the teaching tools at their disposal.

  • Short, concise video lectures per topic (typically 2-5 minutes) with learning objectives, helpful visualisations and video summaries included
  • More detailed notes describing key concepts, attractively formatted with visualisations and generally under 1000 words
  • Playground exercises to allow students to experiment without needing to code (see the below graphic or try Google’s Neural Network playground here!)
  • Programming exercises run from Google Colab, equivalent to Jupyter Notebooks and a great way to review and edit clean, commented code
  • Short quizzes to check your understanding of key concepts

TensorFlow Playground

Granted, Google are one of the industry leaders in machine learning with boundless resources and expertise. But with several career certificates either currently available or coming soon across Information Technology (IT Support Specialist, Data Analyst, Project Manager & UX Designer), it is easy to see how these innovative tools could be rolled out across other STEM disciplines.

Relevance & Universality

There are trade-offs here that should be acknowledged and mitigated by choosing a balanced study plan. I found my university program generally covered the fundamentals of Data Engineering well and provided a solid foundation for understanding Python, SQL, R, data wrangling & data mining. But in a fast-evolving field, newer technology & industry developments from the last five years or so were touched upon only lightly or not at all.

To ensure the relevance of your university course, consider your post-graduation goals and keep on top of new technology and techniques that will enhance what you have already mastered.

In the same way, do not limit yourself to just one career certificate or MOOC specialisation, because the technology & techniques covered may promote the products affiliated with the course provider. Google trains students to use TensorFlow & Google Colab. AWS courses utilise their KNIME Analytics tool and provision EC2. Coursera & edX offer popular data analytics courses using proprietary software – even courses from university departments may use methods and technology not applicable in your region or industry.

The Future of Online Education

The gradual shift to online higher education has only been accelerated by COVID. While on-campus study will undoubtedly return in time, allowing students to re-immerse themselves in college life, there will be an increasing need for universities to justify their relevance and expensive tuition fees to avoid disruption in the face of quality online courses for a fraction of the cost, accessible worldwide in the global economy.

What differentiates the product that universities provide and the graduates it produces? Which old ways of thinking need to be set aside in favour of innovative approaches like AI personalised learning and assistive technology?

Whatever the answers, students will ultimately choose the options that help them succeed in a rapidly changing world.