Data Science Career Guide
While the job title of data scientist may be a recent invention, professionals have been using data science techniques for many decades. The advent of big data has supercharged these practices, creating a broad range of roles related to collecting, analyzing, and communicating information. Some data scientists may carry out several of these tasks, while others may specialize in a specific area. The common thread uniting them is a focus on using technology to synthesize disparate sources of information and generate insights that meet various organizational needs.
Explore our data science degree guide to learn more about studying in this evolving industry.
Why Pursue a Data Science Career?
Data science is a vast, fast-growing domain with many well-paying opportunities. According to the Bureau of Labor Statistics, individuals employed in this industry make a median annual salary of $100,910, with job opportunities expected to increase by over 15% through 2030. Additionally, many roles within the broader area of data science are projected to be in even higher demand in the coming years.
Perhaps more significantly, data science remains, in many ways, a frontier in which professionals are continuing to push the boundaries of what is possible with big data. Growing out of many different disciplines — including computer science, math, and business — its relevance is potentially endless, from fundamentally reshaping operations in marketing, finance, and communications, to playing a pivotal role in addressing climate change, developing self-driving cars, and space exploration. If you're looking for an exciting and lucrative career, data science can take you to many unexpected and interesting places.
If you're looking for an exciting and lucrative career, data science can take you to many unexpected and interesting places.
Skills Required in the Data Science Industry
Working in the data science industry requires a high level of confidence with applying mathematical concepts to real-world scenarios and utilizing technology to process large amounts of information. To be effective and successful in this field, it's essential that you have a pragmatic understanding of:
Statistical concepts such as correlation, probability, and standard deviation
Common programming languages, including Python, SQL, and R
Techniques related to data preparation via frameworks like Hadoop
Using algorithms to build machine learning models
Data visualization software, especially Tableau
While crucial to their role, these technical abilities don't account for the other skills required to be a data scientist. These professionals constantly look for patterns and relationships, and often translate gargantuan amounts of raw data into digestible insights or actionable solutions. To do so, data scientists must have:
The ability to think critically and approach problems with a fresh perspective
Working knowledge of business practices and priorities
Communication skills that help them present findings to a non-technical audience
The desire to work in a team
A sense of curiosity about new data and the answers it may contain
Careers for Data Science Graduates
Typical Qualifications: Bachelor's Degree
Primarily employed in the insurance industry, actuaries quantify and analyze uncertainty. Their job involves using data analysis and communication to forecast the likelihood of future events, determine the potential for risk with a given investment, and develop protocols that minimize the probability of unwanted outcomes. Actuaries may also often work with a team of legal experts to determine a client's insurance liabilities.
Typical Qualifications: Bachelor's Degree
Computer network architects are essential for organizational communication, as they help design, test, and maintain data sharing systems. Their day-to-day responsibilities may include researching new kinds of hardware and software, helping users troubleshoot network problems, and improving the efficiency of communications technology. Computer network architects must therefore have strong data literacy skills to carry out their tasks effectively.
Typical Qualifications: Bachelor's Degree
Businesses that rely on big data need the infrastructure to store large amounts of information. Database administrators and architects help meet this organizational need by structuring, cleaning, updating, and providing authorized access to data. Aside from working with different types of software, they may also regularly interact with crucial hardware components such as servers, which brings them in close contact with both data analysts and IT technicians.
Typical Qualifications: Master's Degree
Economists can have many different areas of expertise, but, in general, they analyze data to help businesses, governments, and individuals make decisions related to costs, savings, and investments. They are employed across a range of industries, including education, public health, finance, and healthcare, to name a few. Often, economists also conduct research and present their findings to different audiences.
Typical Qualifications: Bachelor's Degree
When businesses and individuals are interested in evaluating potential investment opportunities, they typically hire financial analysts to guide them through the process. These experts use big data to identify market trends and advise their clients on which opportunities to pursue. Financial analysts can further specialize in areas such as risk analysis and portfolio management, and can develop expertise dealing with specific kinds of financial products such as securities, mortgages, and loans.
Typical Qualifications: Bachelor's Degree
The primary responsibility of an information security analyst is to ensure sensitive data remains protected from breaches and other cybersecurity threats. They use many different security products to not only respond to online risks, but also anticipate potential dangers facing an organization's information network. Cybersecurity is a fast-moving field, so information and security analysts spend a significant part of their job learning to use new technologies and software.
Typical Qualifications: Bachelor's Degree
Management analysts take a big-picture view of a business to identify potential improvements in how it functions. Their work involves gathering many different kinds of data, analyzing the information, and then making recommendations based on their findings. They may suggest ways to cut costs, boost productivity, restructure different departments, and generally ensure a business is running at its best. Management analysts can be full-time employees or work as consultants on a project-to-project basis.
Typical Qualifications: Bachelor's Degree
Marketing departments and firms are increasingly relying on big data analytics to understand what consumers want. Market research analysts therefore use a variety of statistical techniques to help businesses determine how to sell their goods and services. They collect data on competitors, demographics, and market trends, and use advanced statistical software to forecast consumer demand and identify potentially new markets.
Typical Qualifications: Master's Degree
Mathematicians and statisticians work with data to solve a variety of different problems, ranging from those that affect business operations to ones that influence government policy. They develop sophisticated models that help them forecast outcomes in various industries, design experiments that help them test their theories, and present their work to both technical and non-technical audiences.
Advice from a Data Scientist
Aoun Jafery works as a vice president of client solutions in the marketing and advertising industry. Having started out as an analyst, he now leads advanced analytics and has several years of experience in entrepreneurship, market research, and web analytics. Aoun graduated with a BS in Entrepreneurship from Indiana University's Kelley School of Business in 2011. Continue reading to learn more about his work as a data scientist.
How would you describe your job?
In a nutshell, it's about solving problems and finding solutions. The premise of my work is to use data to answer questions or improve the status quo. Sometimes, this just means preventing bad decision-making.
What's your education background, and how did you get into data science?
I majored in entrepreneurship and corporate innovation at Indiana University's Kelley School of Business. Looking back, I attribute my start in data science to what I focused on in school. I don't have an MS in analytics or data science, because those programs, while more common now, weren't as prevalent back in 2007 when I was starting my undergraduate degree. But the overlap between data science and entrepreneurship is that you have to learn how to problem solve and get over obstacles. Data science is a messy field. So, a lot of time is spent coming up with workarounds for missing data, looking for alternative data, and, most of all, getting the data ready to use.
What do you look for in terms of educational qualifications when hiring a data scientist?
There usually isn't a one-size-fits-all. If I'm looking to fill a very junior role, then I expect to see some kind of education in data science because those programs are now abundant. If it's a slightly more senior role, then the education background can be a little broader and include people who've studied math, economics, or statistics. So, it depends, but from the perspective of someone in marketing and general consulting, I like a quantitative background.
What soft skills do you think are needed to be successful in this field?
Communication. It’s easily the most important. Data scientists in the corporate world are mostly decision-making aids. If you want to be successful as a data scientist, you have to be able to understand what decision-makers need. You also need to be detail-oriented. If you want to show the true value of your work, get into the weeds. For example, if your model or answer doesn't show anything more elaborate than what a subject matter expert could have guessed, you’re not adding the kind of value you’ve been hired for.
What general advice do you have for students aspiring to work as data scientists?
Stay curious. When you're being hired, you'll get asked a lot about technicalities, like your ability to program or understand math. All of that is actually secondary to your ability to understand problems. If you can't do that, you'll have to wait for others to provide direction and won't explore deeper questions yourself. It sounds clichéd, but keep asking yourself what happened, how did it happen, when did it happen, and most importantly, can I figure out why it happened?
In your opinion, is a data science career a good choice?
What makes a career good is extremely personal. If you're primarily interested in making money, then data science is a fairly decent option because you'll end up in the top quartile in terms of earnings. A data science salary will almost always provide a comfortable life, if not an extremely lavish one. It’s also a growing field and most companies are still playing catch-up to where they want to be, so there’s a lot of room for growth. But, if you like building things and improving things, then I would say yes because working as a data scientist is a little bit like being a digital engineer. Instead of building products, though, you build solutions.
What's the best part about working in data science?
The one aspect I personally like a lot about data science is the global community. There’s a tremendous amount to learn and it’s always growing. I really enjoy being able to just stumble across something new on a monthly basis.
Breaking into Data Science
The highly technical — and sometimes opaque — nature of data science may seem intimidating at first, causing you to shy away from a career in this industry. While the work of a data science professional is by no means easy, breaking into the field is far from impossible. The first step toward starting a career in data science is to get a bachelor's degree in data science, computer science or math, which can qualify you for many entry-level positions. If you're interested in gaining a deeper sense of the discipline to apply for higher-level roles, a master's in data science can help you achieve this goal. Lastly, if you're considering a career change, bootcamps are a great way to quickly gain the necessary technical skills to land a job in data science.
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