Data science is becoming one of the most in-demand skills in today’s economy. Companies seeking to exploit their data are on the rise and will continue to be in demand as they strive to find new ways to analyze their data more efficiently and effectively. But what does it take to be a data scientist? What do you need to do (or know) in order for your career path to align with that of a data scientist? In this article, we’ll look at some key characteristics of successful data scientists and see how these traits might help you land a job as an IT professional or get promoted within your current role.
Data scientists work alongside IT teams.
Data scientists work alongside IT teams. In fact, it’s not uncommon for data scientists to be involved in the design and implementation of a data-related project.
IT teams are responsible for collecting, storing and analyzing the data; while data scientists determine what type of analysis needs to happen on that information–and how best to do it.
Data Scientists are creative.
Data scientists are creative. They must constantly look for new ways to solve problems, and this can lead them in many different directions. For example, a data scientist may decide that a simple algorithm isn’t the best way to find out how long it will take your customers to complete their orders; instead they might decide to use machine learning techniques or even try applying artificial intelligence algorithms instead.
A large part of being creative is being able to think outside the box–and since there aren’t any real “boxes” when it comes to data science (or any other field), creativity can be an important skill for anyone who wants work as a data scientist!
Data scientists are data-driven.
Data scientists are data-driven. They have a passion for understanding and analyzing data, and they are curious about the world around them.
A data scientist is creative, able to think outside of the box in order to find solutions that might not be obvious at first glance. Data scientists also have the ability to communicate their findings effectively so that others can understand them easily–even if those others aren’t familiar with all aspects of this field!
The best way for you as an IT professional who wants become a part-time or full-time data scientist is learn how ask questions about your own work environment: How can we improve productivity? How do users respond when we change things about our product/service offering? What does success look like from our customer’s perspective?
Data scientists are passionate about data.
Data scientists are passionate about data. Data is the fuel that drives their engines and they love to dig into it, looking for patterns and insights that would otherwise be hidden from view. They are curious about what makes things tick and how they can use this knowledge to improve systems or make them work better.
Data scientists are passionate about the future! They want to know what’s coming next so they can prepare for it, whether that means developing new products or services based on emerging trends in technology or science (or both), creating new algorithms for analyzing large sets of data with machine learning tools like TensorFlow or Keras/PyTorch–or simply keeping their skills sharp by learning new technologies on their own time in order to stay ahead of everyone else who doesn’t have access these same resources at work (which probably includes most people).
To sum up: data scientists are fascinated by anything having anything remotely resembling relevance whatsoever because if something isn’t interesting enough then why bother doing research at all?
Data scientists are curious.
Data scientists are curious. They’re always looking for new ways to solve problems, and they’re always seeking out new data sources that can help them do so. Data scientists tend to be lifelong learners who constantly improve their skills by attending conferences, reading books and articles, or taking online courses in topics such as machine learning or statistics.
Data Scientists know how to ask the right questions (and how to choose the right questions).
As a data scientist, you should be able to ask the right questions and get the right answers. You should also be able to explain your findings in simple terms so that anyone can understand them. Data scientists are not just statisticians who use advanced mathematics to analyze data; they also have knowledge of business processes and methods for communicating their findings with non-technical people.
A good example of this is an analysis done by Netflix on its customer service call center logs from 2016:
Data Scientists know that it’s not all about the numbers.
Data scientists are more than just data analysts. They need to understand the business, the problem, and all of its context. In order to do this effectively, they must be able to communicate with people from many different disciplines–from marketing to finance to engineering–and present information in ways that make sense for each audience.
Data scientists don’t just work with numbers; they also use text and images as part of their analysis process. They need strong visual thinking skills so that they can identify patterns within large sets of unstructured data (e.g., photos or videos).
If you want to be a data scientist, you should focus on learning how to ask the right questions, interpret your results, and communicate your findings in a way that is meaningful to others.
If you want to be a data scientist, you should focus on learning how to ask the right questions, interpret your results, and communicate your findings in a way that is meaningful to others.
This means that you need to have an analytical mind and understand how to use data for decision-making. But it’s also about being creative with the information at hand–creating new ways of looking at old problems or finding novel uses for existing tools. A good example of this type of thinking is found in this article from The New York Times: “How Data Scientists Are Using Bitcoin’s Blockchain To Track Food Supply Chains.”
Conclusion
The next time you’re thinking about what kind of job you want, remember that it’s not just about the title or salary. You should also consider whether or not this is something that will make you happy and fulfilled in the long run. If your goal is to be a data scientist, then be sure to focus on learning how to ask the right questions (and how choose them correctly), interpret your results correctly, communicate effectively with others so they understand what needs done next step; all of these things will help make sure your work makes an impact on those around!