Big data is big business. More companies than ever are using data science in their decision-making processes and even as part of their core product. From fintech startups to innovative healthcare companies, more data means better decisions, better products and more profits.
It’s no surprise that data scientists -- those who design and build systems to process data -- are in high demand. In one article, Harvard Business Review called data science “The sexiest job of the 21st century”. And it’s been consistently ranked one of the top 10 jobs on Glassdoor for the last several years for its high job satisfaction and starting salary.
What exactly does a data scientist do? Where can you find a job as a data scientist? And where can you learn the skills you need to get started? To find out the answers to these questions, keep reading.
What Does a Data Scientist’s Job Involve?
When you think of a scientist, you probably think of white lab coats, test tubes, goggles and a laboratory. While the work environment may not look exactly the same, a data scientist is a scientist in the sense that their goal is the same: to first ask the right questions, and then find the answers to those questions.
For example, a data scientist may be asked to design a system to detect credit card fraud. First, she would need to work with the stakeholders to answer some questions and make sure the problem is well-defined. What types of fraud are we looking for? What data will we have access to? Is the data standardized, or does it need to be cleaned up?
Then, she’ll make some decisions on what tools can be used to analyze the data. Will she use the SMACK stack for big data analysis? Or is there a better tool? Then the data scientist will get to work designing the processes and algorithms that will actually handle the data and report the results. Once the design is complete and the stakeholders have signed off on it, it’s time to actually start building the software. In some cases, data scientists will write a specification -- a list of rules of how the program should function -- and then hand it off to a team of developers or software engineers. In other cases she may manage the project directly.
Data scientists are also responsible for improving existing data processing systems. For example, a data scientist may be brought in to improve an existing fraud detection system. She would look at its design and find ways that it could be made better -- faster, fewer false positives, or more accurate detection.
Where Can I Get a Job as a Data Scientist?
For a long time, only the largest and most well-funded companies made use of data science to make decisions. But that has changed. Now that computing power -- and therefore data processing power -- can be purchased cheaply and easily from cloud providers, more companies than ever are using big data. And so more companies than ever are hiring data scientists.
Fortune 500 companies are always a good place to start looking for data science jobs. Similar roles -- though not exactly the same -- might be called “business analysts”, “data analysts”, “business intelligence specialists” or “data engineers”. All of these jobs perform similar duties to data scientists, with some minor variations in day-to-day work.
Another great place to find a job as a data scientist is with a startup. Many small companies are developing products that apply data science and big data analytics to existing problems. For example, “fintech” startups -- a combination of “finance” and “technology” -- make apps and platforms that aim to help consumers and businesses analyze their financial data in order to make better forecasts, goals and budgets. Other startups are applying these same principles to healthcare, real estate and even restaurant management.
With both legacy companies and new startups making use of big data, now is the perfect time to start working toward a career in data science.
How Much Can I Earn as a Data Scientist?
Data scientists are among the most highly paid professionals in the computer science field. Average salaries for data scientists start around the $100,000 level, but can increase or decrease depending on location. For example, the average compensation for a data scientist is San Francisco is $121,000, while the same role in Washington, D.C. pays an average of $90,000.
What Skills Do I Need to Become a Data Scientist?
A data scientist is primarily a problem solver. So critical and analytical thinking skills are a must for an aspiring data scientist. In addition, you will need a strong understanding of statistics and statistical analysis. This is a critical skill for making sense of and drawing conclusions from the vast amount of data you’ll be tasked with analyzing as a data scientist.
To design the software that will process the data, you will also need to have some experience with programming. Python is the most popular language for building data science applications. Its many frameworks and libraries for data analysis make it easy to process large amounts of data. SQL remains the most popular database technology for storing and retrieving data, so having an understanding of relational databases is helpful also.
Additionally, data scientists need to understand advanced data processing and analysis techniques. This includes new and emerging technologies like machine learning, which will continue to be a critical part of a data scientist’s toolkit.
Soft Skills for Data Scientists
Despite the name, most data scientists don’t spend their days locked away in laboratories solving theoretical problems. They are on the front lines of businesses, helping key decision makers to plan, forecast and analyze. So communication skills and business intuition are two very important skills for data scientists to learn.
As you continue your career as a data scientist, you will likely find yourself having to explain difficult topics to managers and business leaders -- including directors, VPs and C-level executives. You’ll also need to be able to communicate with business stakeholders to collect requirements and understand the business impact of the systems you design.
One data science expert was asked by Harvard Business Review “Which skill is more important for a data scientist: the ability to use the most sophisticated deep learning models, or the ability to make good PowerPoint slides?” He made a case for the PowerPoint slides -- because communication is such an important part of what makes a good data scientist. So as you learn the technical skills around data science, be sure to also learn how to communicate those ideas well to others.
How Can I Learn Data Science?
Many data scientists start with an undergraduate degree in computer science, mathematics, or another STEM field, and then continue with graduate studies in a more specialized topic. But if you’d like to see if data science is right for you without investing a lot of money, try SoloLearn’s free Data Science with Python course. Through six modules and 127 quizzes, you’ll get an overview of the techniques and tools used by real data scientists. The course uses the Numpy and Pandas modules in Python to ingest, analyze and visualize data in order to solve a variety of problems.
If you already have a general understanding of programming, statistics and other analytical methods, you may be ready for a more advanced course, like Harvard’s online data science course. As you move through that course or other undergraduate programs, SoloLearn is ready to help you with our suite of practice tools and online learning community.
Learning Soft Skills for a Data Science Career
As we mentioned above, just as important as technical skill is the ability to communicate complex ideas to less-technical audiences, especially business leaders and executives. As you’re learning data science -- either through SoloLearn or an undergraduate course -- take the opportunity to practice your communication skills at the same time.
As you learn each new idea, explain it to a friend or family member in a way that makes sense to them. And think about how what you’re learning can be used to solve real-world business problems. By doing so, you’ll gain valuable practice in applying your knowledge and helping others to understand complicated topics.
You can also practice your soft skills by participating in the free SoloLearn discussion forums. There you’ll have the opportunity to help new learners to work their way through the course material. By explaining data science concepts to new learners, you’ll help yourself retain the information better and also boost your communication skills at the same time.
So are you ready to jump into a career as a data scientist? If so, head on over to the free SoloLearn Data Science course and get started today.