There is a ton of data out there, and it’s the data scientist’s job to make sense of it.
Data scientists serve an important role in modern-day decision-making, working with businesses and other organizations to turn their data into actionable insights. It’s a job that’s as creative as it is analytical, with a lot of opportunities out there for people who can bring the right skills and talents to the table.
If you’re wondering what to do with your statistics or computer science degree, or if you simply have a passion for big data, here’s what to know about becoming a data scientist—including salary potential and the types of data science jobs that are available.
Data Scientist Job Description
A data scientist is a technical and analytical professional who analyzes and interprets large sets of data. They are hired by businesses in all sorts of industries to solve their complex data needs, with a high demand for data scientists in engineering, finance, government, and healthcare.
Much critical thought goes into the role, since data scientists are often presented with unstructured data sets that need to be organized before they can be processed. A good deal of creativity is required, too, and a data scientist must be confident in their ability to come up with new and novel approaches as the situation calls for.
Here are some of the tasks that you can expect if you decide to be a data scientist.
Before a set of data can be usable, it has to be organized. Data scientists take large sets of unstructured data and compile them into something that can be easily scanned for patterns and trends. This data can be textual or numerical (and sometimes both), so a data scientist has to be comfortable organizing multiple types of data into a more approachable format—and turning that organized set into more easily readable dashboards and reports for others to access.
Forecasting and Problem Solving
Once the data is organized, the data scientist often pulls out insights to help solve business-related problems and make predictions about what an organization can expect moving forward. An example of this would be a data scientist interpreting consumer behavior data to inform trends, so that a company can decide how to best market a product. A data scientist may also be responsible for making outcome predictions, diagnosing operational problems, or helping companies optimize things like shipping routes, product packaging, and launch schedules.
Machine Learning and Programming
Data scientists create new data models using machine learning and computer programming. These models codify successful approaches and make it easier to apply them to future data sets. They also contribute to the data scientist’s forecasting efforts, pulling out insights that might not be obvious to the naked eye.
Most data scientists work as part of a team, whether that means working alongside other data scientists and analysts or working with other key players within a business. So while a lot of tasks are done independently, there is still a collaborative aspect to the role, and a data scientist must be able to both do their job well and communicate their findings and processes to others.
Skills You Need for Data Science
They might work with computers instead of test tubes, but a data scientist is still a scientist. As such, they must be proficient in a number of skills in order to test their ideas and come up with solutions. These include:
- Machine learning and deep learning
- Computer programming (Python, R, SAS, and SQL are all common languages for a data scientist to know)
- Statistical analysis
- Data processing and organization
- Pattern and trend analysis
- Team communication and collaboration
- Data modeling
Because there is constant innovation in the field, a data scientist must also be able to stay on top of new techniques and applications, updating processes as needed to increase efficiency and output.
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How to Become a Data Scientist
There is a high demand for skilled data scientists. In order to be a data scientist, however, you have to first become familiar with the technical requirements of the job. This can be done through a formal degree program, but experience can go a long way, too. There are pros and cons to both directions, so be sure to do your research when deciding what path you want to take as you pursue a career in data science.
Get a Bachelor’s or Master’s Degree in Data Science, Computer Science, or a Related Field
Growing interest in data science has led to an increase in data science degree programs, with many universities now offering specialized tracks for aspiring data scientists. If the school you’re interested in doesn’t have a dedicated data science degree program, then a computer science degree, data analytics degree, math degree, or statistics degree could be a good alternative.
Getting a degree makes you a more attractive candidate when applying for jobs in data science. It could also open up the door to valuable opportunities such as internships and mentorships. Keep in mind that some businesses may require a degree before you can be considered for a position, so it’s worth doing some digging into various industry expectations to see what you’ll need to do to be competitive.
Teach Yourself and Gain Data Science Experience
School isn’t for everyone, and that’s okay! There are plenty of online courses, guides, and bootcamps that you can use to develop your skills in machine learning, statistics, programming, and data visualization. And if you’ve got a knack for these subjects and a lot of drive, you may be able to establish a career in data science without going through a pricey degree program first.
Of course, getting experience will be necessary if you want to snag a paying data science job one day. Join data science communities online to learn about available opportunities and start building your skills with real-world projects.
Average Data Scientist Salary
The average salary for a data scientist is $136,309 per year, with the typical salary falling in the range of $121,443 to $150,223.
Factors that will impact your salary potential include your education and experience, as well as the range of skills that you can offer a business. If you’re not keen on salaried work, you could also go it alone as a freelance data scientist and make decent money hiring yourself out for data projects on a contract basis.
Types of Data Scientist Jobs
Figuring out how to be a data scientist is one thing. Figuring out your ideal job situation is another. Some data scientists prefer to work in-house for an organization, while others prefer a freelance lifestyle. Which one is right for you? It largely depends on your personal preferences and your ultimate career objectives.
Work on Staff for a Business or Other Organization
Lots of businesses are hiring data scientists, and they’re willing to pay good money when they find the right person for the job.
Working a staff position means that you’ll work solely on data management and analysis for a single organization. This is a chance to immerse yourself in the unique needs of your employer, gaining expertise in their industry and positioning yourself as an integral part of their team. You’ll likely have opportunities for upward mobility too, as well as access to new tools as they’re developed. All these perks mean that these jobs are quite competitive, though, and it may take time to get where you want to go.
Work as a Freelance Data Scientist
Not all businesses need a full-time data scientist on staff. And in the instances when they do need data science performed, they look to freelancers to fill the role.
Working freelance in data science gives you complete control over the clients and projects that you take on. It could also be a way to earn more money doing less work. To increase your chances of success, consider specializing in one or more niches. This will help you narrow down your client list and offer more refined skills to the people who hire you.
To find projects as a freelance data scientist, utilize freelance platforms and more general job boards. Referrals can be an excellent source of work, too, so do a great job for your clients so they’re more likely to recommend you to someone else.
Jobs That Are Similar to Data Scientist
A talent for working with data can propel you into all sorts of careers. If you’re interested in data science but want to broaden your horizons, other jobs that might be appealing to you include data analyst, data engineer, machine learning engineer, statistician, or database administrator. All of these jobs use similar skills and can let you build a career around a passion for data manipulation and management.
Difference Between a Data Scientist and a Data Analyst
The terms are often used interchangeably, but there are a couple of differences between the two roles.
Perhaps most notably, a data scientist is considered to be a higher-level and more technical position (with a higher paycheck, accordingly). They’re tasked with building the models, algorithms, and raw data that unearths or organizes insights, whereas a data analyst may only be tasked with interpreting that data. With a narrower set of responsibilities and a lower bar of entry, getting a job as a data analyst could help you on your way to becoming a data scientist. It could also be a fulfilling alternative, especially if you want to do similar work but prefer to dive into data at a later stage.
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