Last weekend, I officially finished my 100th Coursera course. I have been introduced to the world of e-learning 3.5 years ago and have since ventured into Coursera, DataCamp, and Udemy. Rather than a direct comparison between the different types of e-learning platforms out there, this article is a reflection on what I have learned, sharing tips and courses I’d recommend for those who are planning to start their e-learning journey or would like to find out how I ensure productivity in my e-learning journey!
№1. Make (Good) Notes
There are a lot of benefits to making notes! Read on to find out why and how to make good notes.
While it boils down to an individual’s learning style, I generally find that writing things down, organizing, and rephrasing content helps me understand the content better, especially if you are a visual or kinesthetic learner. I find it more effective to jot down the content somewhere else since it is easier to refer to it without revisiting the course videos.
I split my notes into topics such as “Machine Learning”, “Software Development”, “Project Management”, “Python” etc. If I had organized my notes by courses, a course such as “Data Science with Python” would have been in the same document, when it should have been split into Machine Learning and Python — where the theory belongs in Machine Learning and code examples should be parked under Python. By doing so, it is easier to expand the notes for other courses if they cover similar topics, which happens quite often.
I wish I had realized this sooner as I have rearranged my notes multiple times. Admittedly, I still find content overlaps (or even courses disagreeing on certain definitions!) and it takes extra effort to make notes. On these occasions, I find that reorganizing the content helps me recap those topics. Don’t be afraid to rephrase and reorder existing notes. As a disclaimer, I believe there is no “one good way” to make notes, but you have to start somewhere.
№2. You Cannot Escape Mathematics in Data Science
After taking various Data Science, Machine Learning, and Deep Learning courses, mathematics is at the heart of it all.
There are many branches of mathematics, some mathematics are more equal than others (haha). Below are some branches of mathematics I would recommend in alphabetical order, and in which scenarios would I use such knowledge,
Algebra and Matrix: Useful in areas of Computer Vision and Deep Learning to understand how the algorithms work and how to modify or improve them
Algorithms: I hardly use the formulas since they are mostly implemented already. However, ideas from these courses are useful for Software Engineering to understand how different algorithms may achieve the same outcome whilst having trade-offs in accuracy and complexity
Calculus: Useful in areas of Machine Learning and Deep Learning to understand concepts such as Stochastic Gradient Descent and loss function
Game Theory: Useful in the area of Reinforcement Learning where there are agents, actions, and rewards
Statistics: Useful for Data Analysis, i.e., statistical tests to test if two distributions come from the same population distribution, getting statistical summary from data, causal inference, and common pitfalls in inference. Statistics is also important for Data Science when dealing with continuous, categorical, or time series data
Do let me know in the comments if I have missed out on any branch of mathematics! I don’t think you have to be well versed in all branches of mathematics but focusing on areas relevant to your domain helps you plan which courses to take next.
№3. Don’t Just Focus on Coding Modules
Learning scripting languages is more relevant than (some) programming languages
I love coding. When I started my e-learning journey and looked at the course catalog, I had the urge to just learn everything — Python, SQL, R, Java, Golang, etc. I soon realized that not everything will be relevant to your career. Surely, the most used programming language for data scientists are Python and SQL (sometimes R), but there are other “coding” languages that are highly relevant and often overlooked. That is, learning Bash/UNIX and Git commands.
Bash/UNIX commands are command line tools that help manipulate files and directories, using commands such as ls to list file directories or mkdir to create a new folder. Whereas Git commands help in version control. You may be familiar with git add, git commit, git push to make changes to code repositories.
Granted, there are only a few Bash and Git commands you would use in your work setting, it is still useful to understand how it works behind the scenes. For instance, there are 4 ways to perform merging in Git — fast-forward merge, merge commit, squash merge, and rebase. Understanding their differences is crucial in choosing a suitable merging strategy for your project. Knowing how to implement them is important to know what to do when you mess up your code (or other peoples’ code) — it happens!
№4. Don’t Forget to Pursue Other Interests
Besides math, coding, and scripting courses discussed in previous sections, it is easy to burn out or have knowledge overload. Remember to take some time to pursue your other interests as well.
Explore the many categories of courses offered by e-learning platforms! Taking Coursera as an example, there are categories such as arts and humanities, business, computer science, data science, information technology, health, math and logic, personal development, physical science and engineering, social science, and language learning depending on what interests you.
All the information is out there, the world is your oyster.
For myself, I was also interested in coding best practices, data visualization, financial, organizational, and project management modules. To prepare for my Master’s in Computing (Computer Science), I also dabbled in big data, cloud applications, computer vision, database, and security modules. Personally, I prefer going for the breadth of knowledge before diving deeper into a certain topic, but to each his/her own.
№5. Relate Learnings to Projects
Apply, apply, apply!
It is rewarding to apply your learnings in real-life, be it in company projects or personal projects. While going through the course contents, think about how it can be useful or tweaked to work for you.
Learning about web scraping — what data would you web scrape for a side project, perhaps web scraping Twitter tweets for sentiment analysis? Learning about ML Ops — what models are you working on currently that will be deployed in the future? What models have you deployed, and did they follow similar practices as the course content? You get the idea.
It does not have to be a big jump such as learning to code and immediately making a web or mobile application. Small things can go a long way, such as writing readable, elegant codes that follow the newly acquired knowledge on coding best practices.
Conclusion
I had the opportunity to begin my e-learning journey when I started my career, and I have seen e-learning courses helping my friends land in their roles. Let’s not dive into the regret of not starting earlier, and the next best time to start is now. Hope this article has helped you in some ways to approach e-learning courses effectively, gain some clarity on what modules to prioritize and how not to lose momentum.
To recap, these are the 5 tips shared in this article
Make (Good) Notes
You Cannot Escape Mathematics in Data Science
Don’t Just Focus on Coding Modules
Don’t Forget to Pursue Other Interests
Relate Learnings to Projects
Thank you for reading! If you liked this article, feel free to share it.
Recommended Courses by Topics
In alphabetical order,
1- Algorithms: Algorithms (Stanford University)
2- Artificial Intelligence: AI for Everyone (DeepLearning.AI)
3- Bash: The Unix Workbench (Johns Hopkins University)
4- Cloud (Google Cloud): Advanced Machine Learning on Google Cloud
Cloud (IBM): Cloud Application Development Foundations
Deep Learning: Deep Learning (DeepLearning.AI)
Git: Version Control with Git
Golang: Programming with Google Go
Java: Java Programming and Software Engineering Fundamentals
Machine Learning: Applied Data Science with Python
Machine Learning Engineering: Machine Learning Engineering for Production (DeepLearning.AI)
Natural Language Processing: Natural Language Processing (DeepLearning.AI)
Project Management (Scrum): Scrum Master Certification
Project Management (Six Sigma): Six Sigma Yellow Belt
Project Management (Six Sigma): Six Sigma Green Belt
Python: Python for Everybody
Python: Introduction to Scripting in Python
R (basic): R Programming (John Hopkins University)
R (advanced): Advanced R Programming (John Hopkins University)
Software Engineering: Software Design and Architecture
Statistics (basic): Basic Statistics
Statistics (advanced): Statistical Inference (John Hopkins University)
Statistics (hard): Inferential Statistics
SQL (basic): Introduction to Structured Query Language
SQL (advanced): SQL for Data Science
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