There’s been a lot of doom and gloom, discussing the job market in recent years. For good reasons, many traditional careers are on the decline. It stands to reason that locomotive firers and watch repairers might have cause for alarm.
Of course, data science is a massive and still-emerging field. Breaking into the industry isn’t as simple as simply procuring a certificate. It can be, however, if you know what to look for and where to focus your energy.
Here are some of the most prevalent trends in data science, to help you hit the ground running in your data science career.
2018 Data Science Career Trends
Let’s start by disclaiming that data science is likely to play a part in nearly every industry as time moves forward. A stunning 2.5 quintillion bytes of information are created each day. Scientists, governments, and business owners alike will be looking for ways to leverage that data towards a multitude of different goals.
Here are some of the most prevalent data science trends of 2018, however, to give an idea of the way data is transforming different industries.
1. Specialists Are Overtaking Generalists
Data science has been around long enough that the discipline is starting to diversify. Several years ago, data scientists were expected to assess every analytic and master every metric imaginable. Business owners and data scientists alike realize that’s just not practical, or even possible.
In the coming years, expect to see a lot more collaboration between various data scientists, engineers, and analytics professionals.
Companies that are merely looking to hop on the bandwagon will likely still be searching for ‘jack of all trade’ data scientists, but that they’ve only recently started asking “What is data science?”
Companies looking for data specialists means they’re further along in their understanding. That means they likely have a lot more resources to put towards their data science departments. If you’re searching for a respectable salary as a data scientist, this is one sign to look for.
2. Automation Is Still On The Rise
Automation is here to stay. It will only continue to grow and spread. Trying to fight that would be like forsaking standardized parts and combustion engines during the Industrial Revolution.
Although they’ve been around for a while, AI and machine learning are still very much in their early stages. Soon enough, fully automated processes will become a daily reality. It is up to data scientists to implement that reality as well as making sure it runs smoothly.
Software tools, advanced filters and algorithms will all be necessary to get these automated programs off of the ground.
Data analysts and visualizers are also going to be increasingly in-demand, to help interpret all of this data and ensure these processes are doing what they should.
3. Get Rich
With so much data being produced every single day, we are “long on knowledge, but short on insights,” to paraphrase a popular saying. To put it another way, we need a way to transform knowledge into wisdom.
Big data vs. rich data has been a data science trend for several years. The ‘vs.’ is a bit of a false flag, however, as data scientists need to be using both, especially if they’re working in business in any regard.
Rich data is an essential component of the mechanisms that power AI’s personalized recommendations and predictive algorithms. It involves intricately complex analysis of how data interacts.
To truly get in front of this trend, data scientists need to understand the inner workings of AI. You need to understand what your algorithms are doing and why they’re doing it. This will also help to explain both the processes as well as your decisions to business professionals like marketers, sales teams, and investors.
Data visualization is probably the most important part of explaining complicated data and analytics to ordinary folks like marketers and salespeople. As we’ve already pointed out, we are deluged with an increasingly overwhelming torrent of data each and every day. Data visualization is how we make those raw numbers understandable.
Data visualization is also a useful way to communicate as much information as quickly as possible. With the average Internet user’s attention span being slightly shorter than a goldfish, at this point, that fact alone will continue to be significant.
Consider this infographic on the daily routines of famous people from designer Mason Currey. Comparing the work/life balance of Corbusier to Sigmund Freud would likely take a lot of words. Data visualization pulls it off in seconds, and a lot more as well!
5. Stay Hands-On
Data science is still an emerging industry. It is not time to simply sit back, let the algorithms do the work, and collect a paycheck. In fact, data science will likely make it so those days are never to be seen again.
Instead, data scientists and analysts need to understand the latest developments in your field. We’re scientists, after all, as well as engineers and analysts. None of these disciplines are known for resting on their laurels and being content.
No matter what role you play in your organization, it’s important to stay abreast of how you’re using data. You’ll also need to be well-versed in the software you’re using and your organization’s overall goals. This will help train new recruits, explain your actions to investors and CFOs, and make sure all branches of the organization are communicating effectively.
Data science is one of the most exciting developments of the 21st Century. There’s a lot to know and keep up with, but there also tons of great tools for data scientists of all experience levels.
Ready To Begin Your Data Science Career?
That’s great news! There is a shortage of data scientists, at the moment. That’s only going to get more severe as Big Data, AI, and automation continue to spread and revolutionize every aspect of our lives.
Check out what’s Trending In Data Science today, to find out how to break into your data science career!