How To Build An Online Course On Data Science

How To Build An Online Course On Data Science

The extension of the lockdown brought in social distancing, which not only impacted businesses but also shut down schools and colleges. This disruption has forced students, as well as working professionals, transition to online courses. The pandemic has also provided opportunities for data scientists to upskill themselves using online data science courses.

Responding to this, many ed-tech companies have come up with a variety of online courses for data science enthusiasts to use their content for free. These courses usually have experienced faculties and professors, along with the interactive live sessions, which can help students get a better understanding of the field.

These online courses in data science have left students with many options. The market is crowded, and there is more supply than demand for these online courses. And therefore, ed-tech companies need to build a comprehensive online data science course that can stand out in the market. Here is how businesses can create an all-inclusive online course for data science.

How To Frame The Course & What It Should Include

Framing a comprehensive data science online course depends on the depth and breadth of the course. Framing the course would help in understanding what to include in the course and whether to cover the general topic of data science or have specialised courses related to data visualisation, Python specialisation, and big data training. Each course targets different individuals, and therefore, ed-tech companies need to create courses focused on different interests in the field of data science. 

A comprehensive course needs to be appropriately structured where the learning should first start with basic programming knowledge like Python programming and SQL programming where students would be taught how to write functions, control flow, build basic applications and understand common data analysis libraries. Secondly, it should involve probability and statistics, which would include descriptive statistics, inferential statistics, and probability theory. Thirdly, it should consist of mathematics with calculus and linear algebra, followed by data wrangling where students would be taught how to access databases, clean the data and transformations using pandas and scikit-learn. The courses should also include data visualisation and exploratory data analysis, and lastly machine learning and deep learning techniques such as supervised learning, unsupervised learning, reinforcement learning, CNN, RNN, among others.

Apart from including the overall view of the subject, course developers should also focus on exploring newly emerging aspects of data science such as model explainability, storytelling as well as writing production-level code, which are critical for data scientists to drive a data-driven decision in organisations. While storytelling knowledge will help data scientists to layout business information better in front of stakeholders, explainability skill will assist in gaining the trust of clients and customers.

Alongside, as companies nowadays are relying on democratising data science, students need to learn AutoML tools like Auto-Keras, Auto-sklearn, Auto-PyTorch, to name a few, that can help data scientists to build machine learning pipelines for streamlining the workflows. Besides, these online courses for data science should also involve the understanding of ethics in data science, which will help aspirants differentiate right from wrong in terms of using data and the company’s sensitive information. This will also help newcomers of the industry understand the implication of privacy and the impact of data science on modern society.

Not only should it have an industry-ready updated curriculum but should also involve experts from the industry who can share their real-world experiences with the students. Another essential aspect that can be included is business knowledge, which can be extremely important for newcomers to survive in hierarchy-based organisations. Ed-tech companies should also create platforms, communities and forums for learners to collaborate and clear their doubts.

A general course will provide an overview of the field and attract a more significant number of learners. However, once they progress in their course/career, they will be required to diversify to more specialised knowledge. This necessitates that ed-tech companies include those specialised courses to address that learner-base.

To target specific industry requirements, ed-tech companies can also provide custom-made course packages that can help students gain more industry-based knowledge. Some of the beginners’ courses involve learning about data analysis, data visualisation, Python and R. However, some of the specialised courses cover in-depth knowledge about deep learning and machine learning. 

Many businesses nowadays are deploying end-to-end data science platforms like DataRobot, DataBricks, and other analytics platforms, to aid data scientists in integrating machine learning capabilities in their projects, and therefore, online data science courses must include these to simplify their project workflows. Furthermore, edtech companies should also create courses on the best practices for collaborating to open-source projects and competing on platforms like Kaggle and other hackathons. Learning is not all, one needs to showcase skills to increase visibility. This can be achieved if aspirants compete online and contribute to open-source projects on GitHub.

How It Should Be Showcased & Taught To Students

Many ed-tech companies have also started offering free online data science courses for students to learn their content for free and upskill during this lockdown period. Considering online courses are conducted remotely, ed-tech companies need to make their online data science courses interactive for students to get the maximum out of it. A comprehensive online data science course should include live sessions for students to interact with their trainers and mentors and discuss challenges faced by them. 

Alongside, courses can also include multiple students in live sessions for sharing insights, difficulties and perspectives with a broader audience. Mentorship and one-on-one interaction is another aspect that can be included in the course, as it benefits several newcomers who are willing to start their career in this competitive field. These courses should involve professional experts who can guide these students with required assessments to make them industry-ready. These online courses could also be video-based, combined with interactive learning and discussion boards, which will keep students motivated throughout the session.

The Quality Of The Course

To have a quality flow of learning, ed-tech companies need to design their curriculum with updated industry requirements, which can be done by incorporating experts from the industry who can guide students with their real-life experience. Ed-tech companies also need to study the market and make necessary changes to their curriculum, which can satisfy the needs of new-age employers.

Another way of assessing the quality of the course is by getting it reviewed by a selected group of data science experts and students to judge its ease of understanding and the clarity of the course. The online curriculum needs to be different from in-person classes. A quality online data science course should provide opportunities to students as well as newcomers to have real-world experience, allowing them to network with industry experts, and urge them to participate in hackathons, and attend seminars to build a personal portfolio for job interviews.

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Sejuti Das
Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at

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