Data science is a rapidly growing field offering lots of career opportunities across many different industries. It’s one of those jobs that are so highly respected that it might be intimidating to even consider trying to break into the field.
Let’s take a look at the skills you’d need to learn, and then the high-paying data science jobs you might be able to get once you do.
What is data science?
In a nutshell, data science is about using data to answer questions and develop solutions that help drive data-driven decisions for an organization. You’ll need to tap into a wide array of knowledge that includes artificial intelligence (AI), business intelligence (BI), and data analysis.
Skills required to become a data scientist
Most data science jobs call on you to take a big pile of data and turn it into a vital company resource. You do that through data collection, system analysis, or the building of programs capable of learning and evolving with the introduction of new information.
People who work in data science have a solid foundation in:
- Statistics
- Big data
- Model development and deployment
- Software engineering
- Deep learning
- Communication
- Data visualization
- Programming
- Data Analytics
- Data analysis and manipulation
Data science vs. data analysis
Data science involves gathering information from various resources and relying on business intelligence, machine learning, and analytical skills to make sense of the data. Many organizations rely on data science to inform their business decisions.
Data analysis is about going deeper into what the data represents. Analysts use their statistical skills to review data. They also create visualizations and other ways of communicating the results of their analysis.
What is a data scientist?
Data Scientists are a combination of a mathematician and computer scientist. They have their feet in IT and business, giving them unique insights into both sides of their organization. The rise of big data has led to a need for individuals capable of providing analytical insights that help businesses set priorities and reduce risk.
Data science jobs
One of the great things about the data science field is the variety of roles available. You can find work whether you’re just starting in the field, you’re still in college, or you’re a professional looking to change careers.
Entry-level data science jobs
Keep in mind that entry-level can be a relative term. Many job postings marked as entry-level may still call for at least a year of experience. It can pay to intern in a role that allows you to make use of your data science skills in a professional environment. Roles include:
- Data Analyst
- Business Analyst
- Junior Data Scientist
- Data Scientist I
- Technology Program Analyst
- Statistician
Early jobs for data science students
Many opportunities are available for students looking to gain professional experience in the field:
- Data Scientist Intern
- Junior Data Scientist Apprentice
- Analytics Intern
- Machine Learning Research Intern
How to get a data science job without a data degree
Wondering how to become a data scientist without a degree? Your best bet is to take online courses that teach you the skills to work in data science. Once you get up to speed, start applying for jobs that don’t have degree requirements.
If you already work in an organization with data science roles open, see if you can move to a new position.
If you’re serious about a data science job, a data science bootcamp is the perfect way to get the training and skills you need without a data degree.
How to get a data science job with no experience
Before you think about applying, do a self-assessment. Write down the skills you currently have. Are they comparable to the kind of role you’re seeking? How long would it take you to get up to speed?
In addition to having the technical skills, you need to know about the current trends in the industry you’re looking to enter. What roles are companies looking to fill, and what tools and languages are being used by data scientist professionals?
Make sure you have the necessary skills, like complex mathematics and programming languages like Python and R. And polish up your visualization and communication skills — a good data scientist presents their work in ways that deliver valuable insights.
Practice how to solve real-world problems you’ll encounter at work. Solving problems helps you gain experience and confidence when you interview. It also helps you connect with people already working in the field.
Learn more about becoming a data scientist. And when you’re ready for the challenge, consider a data science bootcamp.
Top 12 jobs for data scientists
As mentioned earlier, Data Scientist salaries are high, no matter where in the country you live. Throw in the future outlook for data science as a whole, and it’s clear that data science is a great career for anyone.
Below are the top 12 career paths for Data Scientists.
1. Data Scientist
Data Scientists use math and programming skills to analyze data sets for insights that might benefit their organization. Everyday tasks include:
- Finding the proper data sets and variables needed to research an issue
- Collecting large sets of data from different sources
- Using data techniques to find solutions to business problems
- Collaborating with IT and business teams
- Searching for patterns and trends in data that could affect the business’s direction
Salary: The average salary for a Data Scientist is $115,240, according to the U.S. Bureau of Labor and Statistics (BLS).
Demand: The need for individuals skilled in computer and information research is expected to grow by 15% through 2029.
Learn more about the typical day for a data scientist.
2. Machine Learning Engineer
Machine Learning Engineers create and run automated software programs capable of building predictive models from large data sets. The programs “learn” from the information collected, helping them develop more accurate predictive models.
Common applications built by Machine Learning Engineers include:
- Image and speech recognition software that performs tasks like auto-tagging images or converting text to spoken conversations
- Customer insight applications that continuously learn about the habits of consumers and craft recommendations based on their preferences
- Algorithms capable of spotting fraud in real-time or analyzing large sets of historical data to come up with financial predictions that lower the risk for organizations
The average national base pay for a Machine Learning Engineer is around $142,956, per ZipRecruiter.
Demand: With so many companies looking to benefit from the power of machine learning, engineers will continue to be in high demand for years to come.
3. Machine Learning Scientist
Machine Learning Scientists concentrate on creating algorithms used in data models. They also work on the software engineering used in their implementation.
Skills typically include:
- Ability to conduct in-depth research
- Understanding of distributed signals
- Knowledge of C or C++ coding and other data science programming languages
- Understanding of AI
- Building and running automations
- Deploying data models
Salary: According to Glassdoor, Machine Learning Scientists earn an average salary of $142,956.
Demand: Demand continues to grow for individuals capable of building and refining the algorithms used in predictive data models.
4. Artificial Intelligence Engineer
Artificial Intelligence Engineers use traditional machine learning techniques to create models that power applications based on AI. While data scientists look at things from a higher-level business perspective, AI Engineers work at a deeper process level.
Skills often used by AI Engineers include:
- Programming
- Knowledge of Python and R
- Sound grasp of math concepts
- Understanding of statistics to determine the accuracy of models
Salary: AI Engineers earn an average of $156,648, according to ZipRecruiter.
Demand: You can find numerous job postings for AI engineers from various industries, making this role a highly desired commodity.
5. Data Engineer
Data Engineers look for trends in large data sets. They also build algorithms that help organizations mine useful information from raw data.
Primary responsibilities often include:
- Developing processes for data sets
- Getting data ready for modeling (prescriptive and predictive)
- Looking for patterns hidden inside data
- Providing feedback to business stakeholders about data findings
Salary: The average national salary for Data Engineers is around $122,622, per ZipRecruiter.
Demand: Companies are on the lookout for skilled Data Engineers to use in various industries.
6. Data Architect
Data Architects provide the vision that turns basic business requirements into technological solutions. They’re also responsible for coming up with the data standards and principles used in developing and using data models.
Skills typically include:
- Ability to design data processing models used to implement business models
- Creating diagrams of key data entries and any relationships
- Good communication
- Ability to work with data professionals at all levels
- Documenting the components needed for a design system
Salary: Data Architects earn an average salary of $135,570, per ZipRecruiter.
Demand: Companies need someone capable of bringing order to the chaos resulting from having large amounts of data available.
7. Enterprise Architect
Enterprise Architects help organizations establish their technology infrastructure. They’re also responsible for the maintenance and upkeep of IT systems, networks, and services. You must remain aware of new trends that might make company processes more efficient.
Skills often called upon include:
- Leadership
- Ability to communicate with individuals with different technical abilities
- Problem-solving
- Working with different teams to implement solutions
Salary: Enterprise Architects earn an average salary of $152,015, per ZipRecruiter.
Demand: Many companies need someone with the foresight and skills to create an IT infrastructure that can handle future demands.
8. Infrastructure Architect
Infrastructure Architects are responsible for designing and implementing IT systems capable of supporting the enterprise technology infrastructure. They also address security and performance concerns that might impact the organization.
Responsibilities include:
- Providing end-to-end troubleshooting for systems, networks, and applications
- Assisting with backup and recovery processes
- Updating network analysis documentation
Salary: Infrastructure Infrastructure Architects earn an average salary of $136,287, per ZipRecruiter.
Demand: IT Architects continue to be in high demand in companies with more complex IT infrastructure.
Future outlook: Jobs for infrastructure architects and similar roles are expected to increase by 4% through 2031, per the BLS.
9. Business Intelligence Developer
Business Intelligence Developers leverage software tools to manipulate data into a format that’s understandable for non-technical users. Companies use this to get a sense of the current state of their business.
The role requires the following essential skills:
- Coding experience with a language like C# or JavaScript
- Experience working with SQL and various relational databases
- Understanding of data warehousing
- Knowledge of Power BI and security rules
- Report creation
- Experience with platforms like SSIS, SSRS, and SSAS
Salary: According to ZipRecruiter, the average salary for a BI Developer is $105,992.
Demand: There’s high demand for individuals with business intelligence skills.
10. Applications Architect
Applications Architects assume responsibility for monitoring an entire system. They must thoroughly understand how every component interacts and affects business processes.
The essential skills of an applications architect include:
- Experience modeling systems
- Understanding application integration
- Security
- Object-oriented design and analysis
- Application development
Salary: According to ZipRecruiter, Application Architects make an average of $143,782 per year.
Demand: Applications Architects fit into organizations like corporate IT or software development companies.
11. Statistician
Statisticians use different statistical models and methods to analyze real-world issues and interpret the data. Companies use their input as part of their decision-making process.
Statisticians are responsible for:
- Coming up with design processes for collecting data
- Processing data using computers for statistical modeling and graphic analysis
- Creating sampling techniques and experimental designs
Salary: Statisticians earn an average of $96,770 per year, according to ZipRecruiter.
Demand: Industries like healthcare, government, and physical science are always on the lookout for a qualified Statistician.
Future outlook: According to the BLS, employment opportunities for Statisticians and Mathematicians are expected to grow by 31% through 2029.
12. Data Analyst
Data Analysts translate data into an accessible format for companies to give them a snapshot of their current performance. Business leaders use their input to make tactical decisions about their organization’s future direction.
Responsibilities typically include:
- Coordinating with management on how to prioritize informational needs
- Using statistical methods and techniques to produce reports
- Locating information in various source systems
- Filtering and refining data for use in statistics and reporting
Salary: According to ZipRecruiter, the average salary for a Data Analyst is $71,034.
Demand: As organizations look to expand the use of analytics, Data Analysts continue to be a highly sought-after commodity.
Future outlook: According to the BLS, the need for individuals capable of working in a Research Analyst role will grow by 23% by 2031.
Further reading: How to become a Data Analyst
How do I get into data science?
There are several paths to starting a career in data science, even without a data science degree. You can go to a data science bootcamp, go back to college, or teach yourself. No matter which one you choose, you will need data science training or education to get a job in data science, even at the entry level. There are pros and cons to each route, which we will cover below.
Bootcamps
Pros:
- Quickly get acclimated to a new role
- Develop a professional network with other attendees
- More affordable than college
- Can complete in a short time
- Opportunity to attract employers’ attention
Cons:
- Difficult to attend a full-time bootcamp and hold a regular job
- May have to pay the entire cost at once, though you can apply for financing options.
College
Pros:
- Access to financial aid to offset the costs
- A college degree may be more appealing to employers
Cons:
- Tuition costs may be higher
- Longer time commitment
- The curriculum may not match the changing needs of the professional data science world
Self-learning
Pros:
- Work at your own pace
- Pick and choose your courses
- No need to attend class in person
Cons:
- No feedback on your work
- May move at a slower pace without a structured curriculum
- No opportunity for collaboration with other students
Start your data science career with Flatiron School
Get the skills you need for your new career path in as little as 15 weeks by signing up for our full-time data science program. Need more flexibility? Our part-time program works around your schedule and can be completed in 40 weeks**. Plus you’ll get individualized career coaching to help launch your job search.
Start the application process with Flatiron School today.
**finish as early as 20 weeks and up to 60 weeks
