Wouldn’t it be great if full-time data scientists could teach data science and help in eliminating the shortage of talents in the domain? But, can they have the time to teach data science? They can do it, but not with the traditional classroom-based approach. If not for the conventional classes, developers can adopt a few modern practices to help others learn data science.
Today, there is a dearth of talents who can deliver on the organisations’ objectives. This is mainly because the skills required within companies vary from what aspirants learn through various education platforms like Coursera, Edx, among others. However, this doesn’t mean that MOOCs are irrelevant. Instead, they are crucial for learning the ropes of data science, empowering aspirants to learn new things quickly.
But, in this cut-throat competition, aspirants need to differentiate from others to get desired job offers. And as most of them rely on MOOCs, more or less, they acquire the same skills. Consequently, professional data scientists should contribute to the community by spreading the knowledge they gain while working at organisations through various means.
Here are a few ways that data scientists can adopt to teach data science effectively while still working as a full-time data scientist.
One of the best ways to teach data science is through blogs. Data scientists can leverage various content platforms to share their insights into organisational requirements that usually are not taught in online courses. “Writing is the most underrated skills in data science,” said Parul Pandey, data science evangelist at H2O.ai. Parul believes in communicating and sharing knowledge using various platforms like Medium and LinkedIn.
Often data scientists think that they are not an expert and restrict themselves from spreading their knowledge. However, it is not about knowing everything but participating in sharing ideas and perspective, thereby teaching others. Therefore, professionals should engage in spreading awareness, both technical as well as the experience they gain while working for organisations.
Data scientists can also participate in the writer’s program of Analytics India Magazine to spread their ideas and opinion through written content.
Audio & Video
Podcasts are another effective medium that data scientists can opt to propagate their leanings and spread awareness about the skills needed in organisations. Undoubtedly, there are numerous AI-related podcasts, but they are mostly inclined towards the trends in the market as they mostly feature leaders in the technology space.
However, one can also leverage the platforms like Anchor, Podbean, among others. However, it isn’t very easy to talk about the techniques for optimising algorithms, but it is good enough for advising and sharing experiences. Besides, podcasts have the advantage over writing as one can expedite the process of sharing information as it doesn’t require skills involved in writing.
Data scientists can also create video classes and host it over YouTube with detailed explanation. But the disadvantage of it is that one needs to put a lot of efforts in making decent videos since everyone does not have video editing skills. Nevertheless, they can host webcasts to provide training, thereby eliminating the need for editing skills.
Today, LinkedIn has become essential for someone who wants to teach various skills. Developers are actively using it for engaging with communities by cross-posting links from their blogs and podcasts. “LinkedIn is the new Facebook,” said Gary Vaynerchuk, an entrepreneur, influencer, and author. The organic reach of content in LinkedIn is enormous, which makes it a must-use platform for teaching. Along with blogs, data scientists today post Kaggle kernel link to get engagement and start discussions about technical knowledge.
While only a few data scientists opt for visiting colleges for spreading data science knowledge, committing to conventional classes can be strenuous for them. “In a month, I used to work with the startup for three weeks and spent a week delivering data science training, explained Srivatsa Srinath, a chief data scientist at Almug Technologies. However, should one which they can adopt such methodologies but that can be scarce.
It is possible for data scientists to teach while being a professional, but requires additional efforts to create content and contribute to the community. However, if data scientists take that extra stress, they can be an enabler in mitigating the shortage of talents gap. Besides this not only helps aspirants but also assists them in building a strong foundation in data science by teaching to others.
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