How did the underage data scientist get into the club on New Year’s Eve? By showing a fake IID.
Awful jokes aside, it’s time to address the elephant in the room—and it’s a big one. Without a doubt, the overarching data science trend of 2023 will be the same for multiple industries—and that’s ChatGPT. Since its launch last month, doomers and gloomers are convinced this AI chatbot spells the end of data science as we know it. Read on to learn why this isn’t the case, and discover other trends that will impact data science this year.
ChatGPT Won’t Replace You
So, ChatGPT. You’ve heard a lot about it recently, and all the online chit-chat might have you reconsidering a career in data science. (The last time you had a career crisis was when you had to choose between data science vs. programming!) It’s time to clear up the confusion.
While ChatGPT is certainly impressive, it’s difficult to know what the future will bring, except that data science will continue to thrive in 2023 and beyond. Sure, ChatGPT can generate base code, solve programming complexities and create SQL queries. But it won’t replace the work you’ll do as a data scientist—at least not in its current form—apart from eliminating some simple coding jobs. It can’t design system architecture. It can’t build entire models from scratch. It can’t even access data after 2021.
Remember, data science is a complicated field that requires real human beings with critical thinking skills to crunch numbers and analyze data for insights. ChatGPT, or any AI, is incapable of thinking the way you do and can’t substitute human emotions and judgment. At least not for a very long time.
So forget the Redditors who say ChatGPT will replace your job. Read about more interesting topics, such as data scientist salaries and data science vs. programming. If you become a skilled data scientist with the right talents, you’ll have nothing to worry about.
AI and What You Need to Know
Okay, it’s time to talk about AI that’s not ChatGPT. Artificial intelligence will continue to influence how data scientists analyze data and make predictions this year. As AI tools get smarter, they will automate many tasks that take time from scientists, such as data cleansing. That makes them a great tool—and again, they won’t steal your job.
If you’ve just dipped your toe in the world of AI, now’s a good time to learn more about the tools you’ll use when you become a data scientist. The best AI platforms allow you to generate insights from data faster than if you were to handle this process yourself.
You’ll learn more about AI if you enroll in a data science boot camp or program, but you can familiarize yourself with this technology now by checking out which tools successful scientists use in their day-to-day work and finding out how simple AI algorithms work. That is, unless you’d rather revisit your data science vs. programming dilemma.
Why Is Data Governance Important?
Another data science trend to keep in mind in 2023 is the ongoing need to adhere to data governance legislation. You might have already heard about frameworks like GDPR, HIPAA, and CCPA. If you haven’t, all these laws govern how companies (like the ones you’ll likely work for) process, store, share and manage personally identifiable information. That information includes names, addresses, credit card details, Social Security numbers, etc.
Knowing about data governance is critical because you’ll be dealing with large data sets with personally identifiable information at some point in your data science career. Knowing how to process and move data from one location to another is a skill that’s as important as analyzing data and generating insights. While data science and data governance have different goals—the first is processing as much data as possible, and the second is limiting the use of data—these disciplines are equally important in the real world.
Real-Time Data Is Critical
Real-time data will be more important in 2023 than ever. Companies that hire people like you want actionable, up-to-the-minute intelligence for business decision-making, not insights from last week or six months ago. The issue is that real-time data requires more expensive infrastructure, so organizations will invest more in this technology as the year goes on—if they haven’t already.
There are many use cases for real-time data, far too many to list. But here are a few examples of when you might need to analyze real-time data in the future:
- Your boss wants to identify supply chain issues that could occur in the next 24 hours based on the latest available data. Batch data processing will take a few days to show results, meaning you’ll need to decipher complicated AI models and identify patterns in data as soon as it’s created.
- Your client wants to know whether to invest in a company’s stock because it’s been performing extraordinarily over the last few days. However, that stock could crash at any moment. You’ll need to use real-time models to decide whether it’s a good investment or not.
While ChatGPT will dominate conversation topics in 2023, make sure to pay equa lattention to trends such as data governance, real-time data and other AI developments. Keeping your ear to the ground—or eyes on the news—will help you navigate the ever-changing data science landscape and turn you into a more confident scientist in the future.
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