Data Scientist Career Guide
Data scientists wear a lot of hats. They’re part mathematician, part analyst, part computer scientist, and part storyteller (just to name a few). And they can also be a real game changer for organizations.
To put it simply, we live in a data-driven world, and data science is focused on ensuring all of that big data makes sense. They’re uniquely equipped to interpret and manage data to solve complex problems. And as their role in business becomes more and more important, they’re in increasingly short supply—which is great news if you’re thinking about becoming one.
Data science has a role in may specific job titles besides data scientist, including data engineer, data analyst, and more. It's valuable to understand the role that data science plays in other career paths for data analysts and data engineers, and the role that data analytics and data engineering plays in data science as well.
Data scientists dig into big data with powerful technologies to gain information that helps companies meet their specific goals and needs. Sometimes in data science you are given a complex problem, and have to decide the information needed to solve it. Or, data analysts might need to look at historical data to create predictions about what will help a business make or save money in the future.
If you’re thinking about becoming a data scientist, the daily life in your career path may look like this:
Taking data from multiple sources, running it through an analytics platform, and then creating visualizations of the data
Analyzing data from multiple angles and searching for trends that could reveal problems or opportunities
Communicating data findings to business and technology leaders with recommendations to adapt existing business strategies
Working with artificial intelligence and machine learning techniques
Cleaning and validating data to ensure accuracy, completeness, and uniformity
Spotting trends, patterns, and relationships within data
Creating compelling visualizations from findings
Deploying text analytics and data preparation
Inventing new algorithms to solve problems and build analytical tools
Recommending cost-effective changes to existing procedures and strategies
“The decision to apply was the best decision I’ve ever made in my life.” Bryon Denton
Data scientists are highly educated, so you’ll definitely need a bachelor’s degree, most likely in data analysis, computer science, math, physics, or another related field. Most of these programs train you in computer programming, basic math, and more advanced math like statistics. But if you want to gain even more credibility in your field and stand out to potential employers, earning your master’s degree is the way to go. In fact, according to ADT Mag, 90% of today’s data scientists have an advanced degree, with 49% holding a master's, and 41% holding a Ph.D.
Keep in mind that the data science field is newer than most tech-focused careers, so staying relevant is extremely important. Continuing education will keep you on the forefront of the industry and be a safeguard against shifts in the job market. For these reasons, career-oriented data scientists should always be learning and evolving with the industry.
For many, the educational path to becoming a data scientist will look like this:
Step 1: Earn an undergraduate degree in data science or a closely related field
Step 2: Learn required skills to become a data scientist
Step 3: Consider a specialization, like cognitive computing, data mining, or statistical analysis
Step 4: Get your first-entry level job in data science
Step 5: Continue learning and gain additional data science certifications
Step 6: Pursue a postgraduate degree program, like a master’s or doctorate
If data science seems like the right fit for you, the good news is you can start preparing for your career right now before even stepping foot on a college campus or starting an online degree program.
You can learn a lot about what data scientists do by spending your free time visualizing data, researching tech tools and trends, and building machine learning models. Online resources like DataCamp and Lynda.com are also great for diving deeper into data science. And with a little self-teaching, you can start building up a portfolio of personal projects that highlight your technical skills and communication savvy.
Data Analytics – B.S.
Lean into data, and walk away with a cutting-edge online degree:...
Lean into data, and walk away with a cutting-edge...
Lean into data, and walk away with a cutting-edge online degree:
- Time: 70% of graduates finish within 37 months.
- Tuition and fees: $3,735 per 6-month term.
- Courses: 39 total courses in this program.
Certifications in this program at no additional cost include:
- AWS Cloud Practitioner
- CompTIA Data+
- CompTIA Network+
- Udacity Nanodegree—a unique, highly recognized credential designed to prepare you for a career in data analytics
- MSI Change Management (Optional Certification)
- Certiprof Design Thinking Professional Certificate (Optional Certification)
There are many job titles a degree in data management and data analytics will prepare you for, including:
- Data Scientist
- Automation Architect
- Business Analyst
- R Programmer
- AI Trainer
- Tableau Report Developer
- QA Analyst
- Python Programmer
- Analytics Manager
- Data Analyst
- Database Administrator
Network Engineering and Security – B.S.
For network engineering and security professionals looking for a...
For network engineering and security...
For network engineering and security professionals looking for a Cisco or vendor-agnostic experience.
- Time: 70% of graduates finish similar programs within 39 months.
- Tuition and fees: $3,735 per 6-month term.
Two focus areas: Students can choose between a Cisco or general program, allowing them to learn and gain experience in their chosen specialty.
Certifications: CompTIA A+, CompTIA Project+, CompTIA Cloud+, ITIL®*^ Foundation Certification, LPI Linux Foundations
The Cisco program also includes: Cisco CyberOps, Cisco DevNet, Cisco CyberOps
The general program also includes: CompTIA Security+, CompTIA Network+, CompTIA IT Operations Specialist (Stacked), CompTIA Secure Infrastructure Specialist (Stacked), CompTIA Cloud Admin Professional (Stacked), CompTIA Secure Cloud Professional (Stacked)
This program will help you develop strong skills in network design, network operations, and security management.
Data Analytics – M.S.
Lead businesses with strong analysis skills:...
Lead businesses with strong analysis skills:...
Lead businesses with strong analysis skills:
- Time: 72% of graduates finish within 18 months.
- Tuition and fees: $4,055 per 6-month term.
Master data mining, visualization, and SQL—and lead analytics at the business of your choice.
No need to wait for spring or fall semester. It's back-to-school time at WGU year-round. Get started by talking to an Enrollment Counselor today, and you'll be on your way to realizing your dream of a bachelor's or master's degree—sooner than you might think!
Hadoop is a powerful and open-source tool that’s used when working with big data and making sense of it. It includes a whole ecosystem of tools and technologies that are used by almost every data scientist.
Python is an object-oriented programming language with a wide variety of libraries that data scientists commonly use. One of the most important applications of Python programming language is in the machine learning domain.
SAS is an advanced analytics tool used by a lot of data scientists. It’s features can be used for extracting, analyzing, and reporting on a whole host of data. It also has a large set of analytics tools that data scientists use to convert their data into valuable business insights.
R is used extensively by statisticians and data analysts to create detailed analysis of data in order to pull valuable information from it. For this reason, R is considered one of the most important statistical computing tools.
Tableau is a business intelligence and data visualization tool that can provide detailed reporting. It’s often used by data scientists to show the results of their analysis in a way that’s easier for people to understand.
Also known as “structured query language,” SQL is one of a data scientist’s most commonly used tools. It works within relational database management systems and helps make sense of structured data.
Certifications combined with a degree can make you even more qualified for data science roles. Certifications demonstrate to your employer that you have top-industry skills that will directly be applicable to your daily work. The online degree programs at WGU offers these top industry certifications along with your degree at no extra cost. This helps you boost your résumé before you even graduate. Some of the best certification options for data science professionals include:
As you might’ve noticed from the list above, data scientists have a very broad skill set that covers everything from coding to complex math. They have a solid grasp of statistics and are leaders in new innovative techniques such as machine learning, deep learning, and text analytics.
When it comes to the hard skills required for this job, there are three main areas a data scientist needs to master.
- Coding: Working in computer coding programs is a big part of a data scientist’s job. SAS, SPSS, MATLAB R, Python, Java, C/C++, Hadoop Platform, SQL/NoSQL Databases are just some that a data scientist uses.
- Business Savvy: Data scientists have a solid understanding of the business sector they work in and create solutions to complex problems that align with business objectives.
- Technical Skills: Math (specifically linear algebra, calculus, and probability), statistics, machine learning tools and techniques, data mining, data cleaning and munging, data visualization and reporting techniques, and unstructured data techniques are all valuable.
So, what else does it take to be a great data scientist? Here are some of the sought-after personality traits or qualities that employers might look for in a potential data scientist hire:
- A natural curiosity: Data scientists are naturally curious and investigative people. The tech industry is constantly changing, and data scientists need to seek out the latest information and tools so they’re always learning and evolving with the industry.
- Creative problem-solving: The best data scientists can pinpoint problems and come up with creative solutions to fix them. Being able to discern which problems are important to solve for a business is critical, in addition to identifying new ways the business should be leveraging its data.
- Good communication: Data storytelling is an essential part of a data scientists’ job. Companies are looking for someone who can clearly translate their technical findings to a non-technical team, such as the marketing or sales departments. As a data scientist, you have to know how to create a storyline around the data to make it easy for anyone to understand.
- Solid collaboration skills: Data scientists are true team players. Since data impacts almost every aspect of a business, you’ll be expected to work with everyone from company executives to product designers to software developers—and sometimes even customers.
How Much Does an Data Scientist Make?
According to the Bureau of Labor and Statistics, the average annual wage for a data scientist is $131,490 However, that number might differ based on what industry you work in. Right now, the highest paying industries for data scientists are finance and insurance, professional services, and manufacturing.
What is the Job Outlook for Data Scientists?
The demand for data scientists is promising. In general, the job outlook continues to be on the up and up as the influx of data isn’t likely to slow down anytime soon and companies will need people with skills to weed through data and help increase its value. According to the Bureau of Labor and Statistics, job potential for data scientists is expected to grow 21% from now until 2029, which is much faster than the average for all occupations. All this to say, there are more data science jobs than there are data scientists to fill them.
Where Do Data Scientists Work?
The better question might be where do data scientists not work! As technology becomes more advanced, businesses need people like data scientists to analyze and maintain their data. Companies of all sizes—from Fortune 500s to small startups—are looking for data scientists to help them make sense of big data and improve their bottom line. Healthcare, government, business, finance, agriculture, and insurance are just some of the fields that need data scientists.