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When you become a data scientist, it’s easy to think that you fully understand the field and know all the major tools and techniques you’ll need to thrive in the industry. However, that’s not necessarily the case. In reality, data science changes just as rapidly and readily as the world itself – all the time!

Data science is, of course, more important than ever before. Regardless of industry, organizations use data science to:

• Market their products and services more effectively
• Determine who their ideal audience members are
• Make big business decisions, and more

Data scientists, therefore, are the specialists in charge of collecting, analyzing, and publishing results for data sets. Although data science is unlikely to diminish in importance in the future, it will undoubtedly shift as an industry as key metrics, or data analysis methods change with the times.

If you’re a data scientist, you must evolve with the industry rather than remain stagnant. If you grow with your industry, you will:

• Have better employment and promotional opportunities
• Understand your field better
• Make more of an impact for your clients or company, particularly for using data in marketing

Just like tradespeople need to grow in their skill sets, data scientists must also grow in the ever-changing world we inhabit. With that said, let’s break down how you can evolve your data science skills while progressing your career.

Stay Abreast of New Developments with Blogs/Publishers

 
The blogosphere, especially for data science and similar industries like tech or finance, is larger and more robust than ever before. This is great for front-line data scientists or those using talked-about technologies.

Why? Because it makes it easy for data scientists to stay abreast of new developments like machine learning, keep an eye on how the industry is evolving, and learn new things just by reading blog posts about data science itself.

Not only is this great for your career and your mental health, but it’s also excellent for your understanding of data science as a specialty. Plus, no matter how good you are at data science natively, the odds are there are at least a couple of gaps in your understanding.

Good news: data science blogs and published research papers can often fill in those gaps and leave you with a more holistic understanding of the industry overall. On top of all that, if you develop a healthy blogging habit, you’ll maintain a learning routine that will serve you well into middle age and beyond.

In a nutshell, blogging and reading research papers about data science can help you maintain proper discipline for critical thinking as well as writing and reading about data science and analytics.

In some cases, keeping abreast of new developments may help you be a more attractive hire when applying for a better-paying position.

Apply for Higher-Paying Positions Regularly

 
Speaking of applying for better-paying positions, all data scientists should be on the lookout for opportunities to advance in their careers and pay ranges whenever possible.

We are long past the economic environment where employees spend 20 years or more with the same company. Now, it’s time to be a data scientist mercenary and sell your specialized skills to whoever pays the most.

This is great for your career trajectory, of course, as it is for your wallet. But it’s also good to ensure that you are always at the forefront of data science as a field. If you apply for and are hired for higher-paying positions, you’ll have a greater opportunity to interact with new data science technologies and techniques.

The result? You become a better, more well-rounded data scientist, and it’ll be even easier for you to be promoted or get a higher-paying position in the future. In many ways, aggressively pursuing new positions or promotions is a snowball effect, where applying for new jobs becomes easier, and you become more successful the longer you pursue this strategy.

Pursue Side Projects

 
While having a main career focus or goal is important, it is also important to come up with a list of side projects you could do in your free time.

Let’s face it: most data science work isn’t all that fun, especially if you do it just to get the paycheck. But many data scientists originally get into the field because of a passion for data science overall.

You can maintain your passion for your field and enjoy yourself by pursuing side projects like developing apps, analyzing data sets on Statista, and so on.

For example, according to a recent survey, 62% of respondents prefer to manage their investments with an app. So who better to start whipping up a data-focused investment application perfectly suited for these folks’ desires than a data scientist like yourself?

As you can see from the above example, side projects are also great opportunities to build a portfolio, which you can leverage to get higher-paying positions as well. Side projects often give you the opportunity to flex your creative data science muscles in a way that traditional positions don’t.

Keep Practicing with Online Challenges

 
Lastly, keep your skills sharp and ready to go by practicing data science with online resources. The Internet has a treasure trove of challenging opportunities to put your skills to the test, such as:

• Data science algorithm tutorials
• Algorithmic logic challenges
• Coding challenges
• Statistics tests
• And more

Even better, some online challenges come with certificates that you can then put on your resume or LinkedIn profile. Once more, completing these challenges and acquiring any relevant certificates could make you a more attractive hire when your dream position becomes available.

All in all, growing as a data scientist is more important than ever, especially as new professionals enter the workforce and become your competitors. By following the advice above, you’ll remain a sharp-eyed, forward-thinking data scientist with full knowledge of the new technologies and developments in your field.

 
 
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.

Source: https://www.kdnuggets.com/2022/01/grow-data-scientist-everchanging-world.html