Like many other career fields, data science and all of the sub-fields such as artificial intelligence, responsible AI, data engineering, and others aren’t immune to the dynamic nature of emerging technology, trends, and other variables both outside and within the world of data. Even SQL has seen numerous changes; as data engineering becomes more complex, so must its tools. And with the world experiencing an AI renaissance, the importance of continuing your learner’s journey will only become more important for data professionals. So let’s break down, a few reasons in further detail, why continual learning in data science is so critical for those working in data science.
Just last week, the latest version of PyTorch was released. Though this is a single example, there is no dispute that frameworks and other developments are changing the face of data science at breakneck speeds. Due to the growing scale of AI and the further use of both unstructured and structured data, the field is seeing a constant evolution of new tools, methods, and techniques. Let’s take natural language processing for example. Right now, thanks to advancements in deep learning models and the use of NLP in multiple industries such as healthcare, investments are flowing and are helping to push forward even more development within the NLP world.
And the risks associated with not keeping up with the latest developments in the field? Well, for example, a team could use outdated methods and technology which could result in not only a loss of efficiency but also a reduction in the accuracy of models. If anyone works in a high-demand industry, they know for a fact that such a result could severely damage the credibility of the team and company.
As was briefly touched on in the above section, AI-powered technology is being applied to various industries and fields. To put it plainly, what works for one vertical doesn’t mean it will work for others. For example, data engineering in healthcare through ERPs won’t be the same as data engineering with eCommerce brands. One field isn’t better than the other, but the issue is that both have completely different needs and as anyone who works in data science knows, that’s a pretty important variable. AI that is used for a marketing platform that needs to optimize advertising campaigns, personalize content, and improve customer engagement, won’t be the same as a finance-focused AI program that seeks to predict market trends, automate the trading process or provide insights.
Though some of the vernacular is similar, each vertical comes with its own industry-specific challenges and opportunities. That in turn will create new frameworks, methods, and technologies which are unique to each industry. So staying up-to-date with these changes will be paramount for any data professional who wants to have the flexibility and security which that knowledge provides.
Let’s focus on the elephant in the room – how continual education is key to the employment prospects of data scientists and AI professionals. This is due to the fact that the needs of a company, non-profit, NGO, government agency, etc, will change as time goes on. Nothing, especially data science, remains the same. For example, compliance is critical in the healthcare industry and regulations change. What was acceptable in terms of medical data storage may not be so after a law is changed or amended. So being able to keep up with what is happening in the world around you will assist in keeping unexpected changes from coming out of left field. If you’re ahead of the curve with new changes it displays how agile you are as a data professional.
Of course, healthcare is one thing, but this can be seen in other industries. For example, maybe a new AI-powered technology for marketing was released. This is happening all the time. We see how advances in machine learning and AI can help businesses adapt to rapidly changing market conditions. This is especially true as the world continues to expand into the digital realm. So anticipating changing needs is one of the best ways to stay competitive in the job market.
Of course, this is job search 101, but networking doesn’t go away when you’re employed. Some even say that networking should be more important when you are employed. That’s because being able to properly network is an important aspect of continual education in data science. Being able to collaborate, share knowledge, and engage in professional development are legitimate ways many within data science keep up with the latest in trends. Yes, networking can look like meeting rooms at hotels but it’s so much more than that. Networking opportunities give you opportunities to see fresh new ideas, tools, and techniques firsthand. You even might find mentors and future colleagues & collaborators by networking.
And of course, there is the idea of a professional brand. As you evolve in your data journey, building that reputation and brand could mean the difference between cold calling or cold applying for jobs, or having recruiters seek you out. In one scenario, the power is on your side, and in the other, not so much.
It Improves Your Overall Skills
The one thing that continual education will do, and honestly centers around, is the improvement of your overall skill set. By embracing continual education, you are not only testing your metal of existing knowledge, but you’re also adding new tools into the toolbox. This keeps you more adaptable in the dynamic world of data science and you’ll be less likely to find yourself with obsolete skills. The truth is, it’s easy to get comfortable, sit back, and not make changes because something hasn’t broken. But that isn’t the world we’re living in any longer and tech is by far more sensitive to changes in the field than most others.
That’s because data science, for many industries, is the frontier. From content creation, copywriting, marketing, art, communications, shipping, and all others, data science has found itself to be at the cutting edge of industries now contending with rapid change due to evolving technologies. This is why improving your skills by continuing education is so important and will remain important for the data professional.
Conclusion on Always Learning in Data Science
What do you think? Aren’t these all excellent reasons to keep learning in data science? Well, ODSC fully understands the importance of continual education and is ready to help you develop those skills. Whether it’s at ODSC East, ODSC Europe, one of our Mini-Bookcamps, or on-demand training with Ai+ – where you can eat cereal and improve your machine learning expertise – ODSC has you covered at every avenue.
With ODSC mini data science bootcamps, you can get the training you need in a shorter period of time than most other data science bootcamps, with the information you need to get started, extra career resources, and on-demand training to a massive library on the Ai+ Training platform so you can learn what you want, when you want.
We have two options coming up in the near future. There’s ODSC East coming to Boston and virtually this May 9th-11th, currently 40% off, and also ODSC Europe coming to London and virtually this June 14th-15th, currently 60% off. Each bootcamp option includes pre-bootcamp sessions to help you get caught up to speed with important topics like data science and AI literacy, and access to our Ai+ Training platform for 1 year with on-demand sessions from past conferences.