Discovering Data Science and Dataquest During the Pandemic: My 2020 Story and Lessons Learned

31 December 2019 was my last day at work. It was a conscious decision to take some time off to be with my then 7-month old daughter. “Just for a few months”, I thought to myself. Twenty-five days later, the first case of Covid-19 was detected in Malaysia, and about two months after that a lockdown followed.

Between adjusting to new norms, doing odd jobs and being with family, the job applications I sent out disappeared into thin air (or at least that’s how it felt like). Besides being ghosted post-interview on two occasions, there were zero replies on others. Unfortunately, I learned the hard way that wearing many “hats” has its downside if you don’t play the cards right; I started work fresh off university working on SQL databases and SAP BI application handling finance data. Three years after that, I switched to e-commerce where I managed the website of a flower delivery startup and got some exposure to digital marketing. Despite the six years of experience in different industries, my lack of expertise in a niche area proved to be a disadvantage.

This got me thinking about what I want to do in the long run. I loved my previous jobs but I don’t believe I’ve found my passion. The uncertainty of where things were heading with my career and the pandemic in general, was a bit of a roller coaster for me personally. Turning 30 last year, I imagine some sort of stability for myself, and I was far from it. That said, I had to move forward and so the job search continued. This was when I stumbled upon Data Science.

I first heard of Data Science years ago through my university lecturer who took a course offered by the John Hopkins University. Besides that, I’ve no clue what it’s about. Truth be told, the little kid in me thinks Science is such a big word so Data Science sounds quite intimidating! Nonetheless, I gave it a read and was fascinated that there’s so much that can be done with data, and how things are done at scale is impressive. The other aspect that’s interesting is the various roles involved in just making data usable. The Data Scientist role undoubtedly is the most popular, but I found Data Engineering most intriguing.

So Data Engineering it is. It was just a decision, but this got me excited as I now had something to work towards. I Googled up the skills needed to become a Data Engineer. Being unemployed, I thought learning for free on websites and YouTube would be the most ideal. As I expected finding materials can be tedious and long-winding, I was cautious with spending time finding out what to learn and actually follow through with the learning. This was the mindmap of topics that were mentioned many times during my research.

Around the same time, I got news about Coursera sponsorship by MDEC through an alumni email. The agency is responsible for the country’s digital economy growth, and the sponsorship was an initiative to assist those unemployed and affected by the pandemic to upskill and have better job prospects. This was a great opportunity as Coursera is very well-established and it would definitely save me plenty of time looking for learning materials. I applied for a spot and was accepted to be a learner under the program.

Using the mindmap as a guide, I bookmarked relevant courses and started learning. SQL was the first lesson I completed on Coursera. I chose SQL because I have experience with the language and thought something familiar would be good to get the ball rolling. Next up was Python and Hadoop, both of which were new to me so the learning curve was fairly steep.

While it was occasionally challenging to keep up with doing Python, it wasn’t until the middle of the Hadoop lesson that it became clear the courses weren’t for me. Being lecture-based, although with hands-on exercises, it wasn’t sufficient for me as a beginner. The fact that I also picked out courses based on the main topics alone didn’t help; it seemed that some of the lessons were either too general, heavy on theories, or didn’t emphasize much on Data Science application. It wasn’t clear how things relate to each other and this made learning especially hard and unenjoyable. Even so, I was still on the fence; on one hand, it’d be wasteful to not take advantage of the free access but on the other, learning would be ineffective and I wouldn’t be able to make real progress.

After some contemplation, I looked for alternatives and it came down to DataCamp and Dataquest. I had taken an introductory Data Engineering course on DataCamp and liked the lesson thoroughly. The career track on top of the course, practice, and project modules are very well thought out. I hadn’t heard of Dataquest prior to this. However, I went with it eventually because projects are incorporated in the lessons implying that learning will be hands-on, and I’ll be able to build a portfolio at the same time. It also says on the website that real-world datasets are used which means the concepts taught are most likely to be practicable skills when I do get a Data Engineering job someday.

My learning on Dataquest officially started in early December 2020. Missions have been great but not without some challenges. The biggest hurdle so far for me was the first guided project. It was lengthy and everything was new: Python codes, Markdown syntax, data sets analysis, findings discussion. That said, completing the project was definitely worthwhile as I gained a lot of knowledge and it set my expectations towards subsequent missions and projects which I feel are manageable at the moment. This outlook allows me to be consistent with my learning.

Currently, my progress is at 21% and I aim to complete the path by August. These days, my routine consists of being with my now 21-month old daughter, solving missions as well as working a temp job creating Wordpress websites, doing SEO and soon will probably do marketing. Things are all over the place now but I believe my time here on Dataquest will change that soon. To get by, below is how I organize my days in Google Calendar. Together with my bullet journal and the Clockify app, the calendar helps me to keep myself accountable and ensure I commit to learning every day.

I still have a very long way to go, so wish me luck!


All the best for your journey @atikah.mohamad


@atikah.mohamad: you have an inspiring story. Keep learning and building, I’m sure your efforts will pay off eventually! Thanks for sharing your journey! Also all the best on your job hunt!


Thank you very much @vishallbabu5!

1 Like Yes, hoping things will fall into place with the learning and all :slightly_smiling_face: Thank you for reading!


I use both DataCamp and DataQuest and although DataCamp is a great resource with plenty of different courses (some are very high quality, like Statistical thinking in Python) I do not like that they do not force you to think over the exercises and solve them following just the instructions of what you have to do.

On the other hand, DQ forces you to code everything: from library imports to printing the results giving just some instructions. And their exercises can be solved in multiple ways.


To a certain extent that’s how I felt with Coursera, at least for the Python course. When I went through the Python course on Dataquest, several things were completely new to me when it was already my second time sitting for the “fundamentals” course.

I feel the course and mission structures in Dataquest are more effective for my style of learning, although I’m starting to see the need for supplementary materials as the learning curve gets steeper on some parts.

Thanks for sharing your thoughts here @artur.sannikov96! :blush:

1 Like

No resource will give you all the information you need. I also use DataCamp, read books, articles, other people’s code, etc:)