As you may have noticed, data analytics is always evolving as a field, so it's not surprising to see data science changes from year to year. What was hot and trendy in 2020 may not be as prominent soon and vice versa. That's not to say that you should expect to see drastic changes in 2021, but it's good to adapt your expectations, taking into account the latest trends. In this article, we'll explore all that and see how you can benefit from these insights for your career in data science.
It's no secret that deep learning is gaining even more popularity, particularly in time series analysis. So, RNNs are bound to rise in demand as a skill, particularly if you are involved in the field's forecasting part. Additionally, healthcare seems to be becoming more aligned with this tech, so it is expected that more medicine-related organizations are going to be looking for data scientists to join their ranks. What's more, IoT is expected to incorporate AI, making our work more relevant to infrastructure projects. Moreover, we should be expecting Reinforcement Learning (RL) to grow further as to use cases of it, such as chat-bots, are growing in popularity. Finally, it seems that more and more people are becoming aware of data science and AI's benefits, so it's easier than ever to make the business case for advanced analytics in a company. Simultaneously, the cloud provides a viable solution for the hardware required, something that's bound to stick in the coming years.
Based on the above, it’s reasonable to deduce that the data science specializations more likely to be more relevant this coming year are AI expert, data engineer (particularly the one geared towards machine learning, aka, machine learning engineer), and those data scientists with domain knowledge in healthcare and IoT. Naturally, Natural Language Processing experts are bound to remain in demand, particularly if they possess chat-bot know-how.
Beyond all that, it’s important to remember that the one thing that’s bound to remain relevant in the years to come, regardless of these trends, is the data science mindset. This mindset involves various aspects, such as problem-solving skills, creativity applied in analytics work, meeting deadlines, and collaborating with other data professionals, to name a few. The data science mindset is our attitude towards the data science problems we have to solve. As such, it's something essential and perhaps more relevant than whatever skill is in vogue at any given time.
You can learn more about the data science mindset and other relevant topics in this field through one of my books, titled Data Science Mindset, Methodologies, and Misconceptions. There I explore the various aspects of the field without getting too technical, all while highlighting those skills that make up the data science mindset. I include some soft skills and some hard ones that are still relevant today, even if some of the tools have evolved since then. So, check it out when you have a moment. Cheers!
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Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.