Select Page

5 Significant Benefits of Online Learning for Data Science

by | Data Science, Experience, Tips

The internet has made access to information very easy and affordable. Technology has been completely integrated into how we learn and how we work. It can all be supplemented or entirely provided by online education from primary school to degree level. Learning within a brick-and-mortar establishment like a university or college is not a necessity to achieve what you want to do in life. This post will describe five major benefits of online learning and how to make the most out of massive online open courses (MOOCs) and interactive online courses like Dataquest.


  1. Online learning is affordable.
  2. Online learning provides unlimited access to knowledge.
  3. You can create your learning environment to cater to your needs and limit distractions.
  4. Online learning builds self-discipline.
  5. You can tailor a learning schedule to fit your time constraints.
  6. Interactive MOOCs like Dataquest give you an efficient path to learning with guided portfolio projects and online communities to learn.
  7. Go beyond prepared portfolios and sign up for competitive data science platforms like Kaggle.
  8. Do not rely exclusively on MOOCs; invest in books.
  9. Short list of the best online courses available for data science, machine learning, Python, and R.

1. Online Learning is Affordable

Online learning is much more affordable than traditional “in-person” learning due to the lower overhead. This overhead comes from operating the program and, in the case of the student, the cost of commuting. The price range can vary depending on the method you choose. The table below shows that for MOOCs and online interactive courses, you will spend at the most roughly $300 depending on how long you take to complete the course, which is significantly cheaper than colleges or boot camps. Courses such as Dataquest offer a monthly subscription of $50.

FactorUniversity/CollegeBootcampsBooksMOOCs/Online Interactive Courses
Time Investment1-4 years~ 12 weeksNot DefinedNot Defined
Interactive?YesYesNoYes (depending on choice)
Complete?YesYesNo (use more than one book)No (use more than one MOOC)
Credential?YesYes (depending on bootcamp)NoNo
Table showing the characteristics of different education options

2. Online Learning Provides Unlimited Access to Knowledge

If you choose to do a course at a traditional university or college, you will be limited by the available materials at that specific institution. Online you have access to course material in any institution worldwide. Because there is little investment in enrolling and being present in a learning environment, you can try out courses online based purely on curiosity. You have more freedom to take several courses. Moreover, if you have a specific career goal and want to undertake a particular program to get it, you will not be limited by location.

Removing location dependence to learn will help broaden your perspective on ideas and learning methods. Online courses provide an opportunity to network with people from around the globe and become more culturally aware. You will also have access to new ideas from professionals and academics in other countries who you may not have discovered if you went to a local institution.

3. Create Your Environment to Cater To Your Needs And Limit Distractions

Attending classes in person can be uncomfortable, spending hours in a posture-breaking chair with minimal breaks. If you study in the library or the student common room, you can experience distractions making your learning process less efficient. A well-made online course will provide all the lectures and materials necessary to be accessed from the comfort of your home. You have more control of your environment at home, limit noise, use comfortable furniture, and take breaks when they best suit you.

However, with extra comfort comes additional discipline. It would be best if you made a working environment free from distraction and not too relaxing that you procrastinate. Aim to have a work environment where you are not on your sofa, bed, or anywhere you associate with being at rest. All you need is a desk and a comfortable chair. I go into more detail about how to be as efficient as possible, working at home in my post called “7 Best Tips For Remote Working For Data Scientists.”

4. Online Learning Requires Self Discipline

At university or college, a senior academic will always oversee your course progression and hold you accountable for your work, whether it is a professor or doctoral tutor, etc. Alongside academic staff, your peer group is a source of motivation and additional accountability. This push does not exist with online learning; you must rely exclusively on yourself for accountability. Finding incentives from within fosters self-discipline, which is one of the most important lifelong skills to develop. As a data scientist, you will often take the lead on projects that require motivation, a sense of direction, and the discipline to work towards targets. Learning online allows you to build your knowledge base for your field and soft skills like self-discipline, time management, and stress tolerance.

5. You Can Tailor a Schedule to Fit Your Time Constraints

Life is complicated, often as a student, you will have to juggle a job, your family, and your personal life with your education. Online learning does not require going to a traditional classroom with set meeting times and curriculum. Most online courses are not live; therefore, you can fit your education with other commitments.

With online learning, you can learn at your own pace, which can help alleviate a lot of pressure common in traditional education. Teachers often go at a pace that is too fast for students and then require them to complete a task based on the lesson. This learning mode can make it more challenging to ask questions and explore new concepts in sufficient detail. Flexible online learning means you can slowly grasp concepts and move forward only when you believe you have fully comprehended them, which will boost your knowledge and graded performance in the long run.

Take Advantage of Interactive Courses and Competitive Data Science Platforms

Massive Open Online Courses emerged in the late 2000s consisting of video lectures of university courses provided by platforms such as Coursera and EdX. MOOCs provide a lower entry-level for everyone to learn university-level data science and machine learning courses. Many courses are free-to-audit and require a one-off fee to get a certification for completing the course. MOOC certifications are not universally regarded and do not hold the same recognition as a university degree and should not be the main reason for completing a course.

MOOCs are becoming increasingly more interactive, evolving beyond video lectures to include capstone projects, quizzes, assignments, and coding interfaces.

Online interactive courses such as Dataquest differ from typical MOOCs by dispensing with video lectures. Instead, Dataquest uses textual lessons combined with an interactive coding environment, which allows you to complete exercises without having to set up a local environment on your machine. When you run your code, you can get instant feedback.

Dataquest platform screenshot: Source.

Interactive learning is particularly beneficial for computer science-related fields such as data science, emphasising practical knowledge. You will need an understanding of programming syntax and structure at the standard machine learning algorithms like k-nearest neighbours, all of which you can learn through practice.

With Dataquest, you can take specific “paths” with the end goal of specializing in a particular role, for example, Data Analyst, Data Scientist, or Data Engineer. If you are going for the Data Science path, I would recommend going through Machine Learning by Andrew Ng before you start to understand the underlying machine learning algorithms. Dataquest may not include details necessary for your full comprehension, given that it is read-only.

Around a third of the Dataquest course material is free, from which you can learn a lot. The basic plan ($29/month) includes access to all materials in the data analyst path. The premium plan ($49/month) provides you with

  • An active online community (Slack).
  • All portfolio projects (Jupyter notebooks).
  • 1-on-1 consultation with data scientists to discuss challenging questions.
  • Resume reviews.
  • All available courses.

Access to all of the guided portfolio projects alone is worth the price of the premium subscription. Portfolio building is one of the best actions to land a data scientist role. I discuss the best ways to start your data science career in my blog post titled “7 Best Tips to Help Get a Data Scientist Job From Scratch.” However, Dataquest projects alone do not suffice; you should aim to apply the knowledge built from online courses to real-world data problems.

One of the best places to start diversifying your data science portfolio is Kaggle. Often companies and governments will release datasets to discover patterns and provide solutions, for example, the COVID-19 Open Research Dataset Challenge (CORD-19). Tackling real datasets will give you the experience needed to be a better data scientist and give you the chance to provide a substantial benefit to society and get recognition for your work. However, do not expect Kaggle to provide you with all the necessary experience to be a data scientist; a lot of learning is done on the job. In Industry you will often be handling noisier datasets. Kaggle’s datasets are prepared and often with the features outlined by domain experts. Evaluation of Kaggle problems revolves around model accuracy; in Industry, accuracy is a factor. Still, there is a balancing act with the cost of model deployment, scalability, and integration with existing solutions.

Moreover, machine learning is not essential to every aspect of a data scientist’s work, whereas, on Kaggle, all datasets require machine learning. The takeaway from using Kaggle is that it can give you access to a broad range of datasets and help hone your skills to become a better data scientist. Here are some similar platforms to Kaggle:

Your learning does not have to centre around a data challenge or prepared project. If you have an idea, you can create an application and share it on Github.

If you have little experience with Python-based data science tools, you should learn the most common tool for data science challenges, Pandas. You can go through our tutorial on Pandas titled: “Introduction to Pandas: A Complete Tutorial for Beginners.”

If you want to develop your programming skills, you can use the free online compilers the Research Scientist Pod provides.

And if you want to become more familiar with statistics you can use the free statistics calculators, and tools the Research Scientist Pod provides.

Invest Beyond Online Tools

Learning online has significant benefits; ebooks and MOOCs have made accessing information flexible and scalable. However, books are necessary to make the most of your online learning experience.

Using a textbook that requires pen and paper for note-taking and question answering can help boost your ability to process and retain information. While you can still take notes with online courses, you may rely on the keyboard to make notes. Make a habit of writing down your thoughts when reading books and taking part in online courses. Being able to forge a link between theory to your thoughts will help engrain it in your brain.

Practice combining a book with the content on your screen during online lessons to get a more in-depth insight into the topics discussed. Find the books closest to your course that go into further detail. When it comes to becoming an expert in a field, you can never have too much information or do too much reading. You can find a shortlist of the best books to own for data science and machine learning below:

Physical books are a permanent reference, whereas online information can change quickly. Investing in physical books will not depend on your internet connection or computer battery life. Treat books as the invaluable tools they are for your blended learning program.

Concluding Thoughts

Online learning is fast becoming a standard method for education. You can access any course anywhere and scale your knowledge by applying it through interactive online courses, portfolio projects, and competitive data science challenges. Undertaking an online course with a community will require you to embrace communication and project management technology, which is essential in our increasingly remote-working world. Professional tools such as Skype, Zoom, Dropbox, Slack, Trello, Git, and Basecamp are standard tools. Getting comfortable with these tools before starting a new job will give you a significant head start and allow you to build a virtual rapport with your teammates, tackle projects efficiently and independently, and teach you how to handle a virtual workspace.

Before you embark on an online course, ask yourself the level of investment both time-wise and financially, you are happy to commit to; this will narrow down the available options. It may be the case you want to do an online degree program for four years, or you want a several-month-long specialization course in data science to boost your portfolio. After you have determined your investment level, determine how you learn best. Sometimes this requires trial-and-error; you may already know through previous courses you have completed. Making this judgment will help align you with the best path based on your learning. If you are more visual and audio-focused, extended video lectures with quizzes may be for you. If you switch off during lectures and find you learn best through an immediate application, interactive online courses like Dataquest might be your best option. Either way, for a more comprehensive understanding of the concepts of data science and machine learning, it is best to combine video lectures with coding practice. In other words, do not rely on any single platform or mode of education.

Think about your learning process as a blended approach, where you take the best of all worlds: books, MOOCs, interactive online courses, competitive data science projects, and native projects. If you consider that strategy, you can learn as much as possible about any given topic.

Thanks for reading to the end of the post! Share this post and put your thoughts in the comment section. Sign-up to the mailing list to be notified when we release new material. See you in the next post!