Big data keeps getting bigger. By 2025, its volume worldwide will hit 181 zettabytes, or 181 billion trillion bytes, according to the market and consumer data service Statista.
Contained within that data are insights that can impact and influence operational strategies across many industries. However, to unlock those insights, companies need data science. Its combination of mathematical, programming, analytical, and computer science concepts can be used to derive actionable intelligence from a wide range of data points.
To comprehensively understand and apply those concepts requires an advanced education. A program like a Master of Science in Engineering (MSE) with a specialization in data science can help individuals develop the knowledge and skills to pursue a host of intriguing career possibilities in a field that’s central to modern business success.
With the U.S. Bureau of Labor Statistics (BLS) projecting that employment in the data science sector will grow 36% by 2031, creating more than 40,000 new job opportunities, there’s never been a better time to explore what you can do with a data science degree.
Why Is Data Science Important?
The expression “big data” refers to data sets that are too large and complicated to be analyzed by conventional means. Data sets in fields such as social media and health care require computing power, sophisticated software, and specialized expertise to analyze and produce useful outputs.
Data science is the missing link that provides those results and makes them useful. It’s interdisciplinary, integrating math, statistics, programming, analytics, and artificial intelligence to uncover actionable insights that might otherwise go unnoticed.
Its uses aren’t confined to technology companies. Virtually any kind of business or organization can employ data science in a wide variety of ways: for products and services, design, decision-making, and strategy. These various applications underscore why data science is important to growth in many industries.
Offering Products or Services
Users of platforms such as browsers, social media, and digital entertainment generate a steady stream of information about their likes, dislikes, interests, and habits. Analyzing that data can help those platforms deliver products and services tailored to their consumers’ needs.
- A streaming music service selects songs that listeners are likely to enjoy, based on music they’ve listened to in the past.
- An e-commerce site reviews consumers’ past purchases and products they’ve viewed to recommend other items that are likely to appeal to them.
Incorporating sensors into equipment can yield large amounts of data, enabling engineers to create devices that are both more efficient and safer.
- Automated vehicle technology combines video, radar, and lidar sensors with detailed maps to help drivers navigate, stay in lanes, and maintain safe distances.
- Aerospace engineers collect data such as speed, stability, and temperature while testing experimental rockets. They use that data to model and build new (and improved) designs.
Companies and organizations gather and analyze big data to make more evidence-based and effective decisions.
- Police departments analyze incident reports to understand where specific kinds of crimes are occurring and what resources they should deploy to those locations.
- Medical researchers analyze the medical records of stroke patients to classify them and learn which kinds of treatments have the best outcomes.
Businesses use big data to understand their customers and their own strengths and weaknesses, with the goal of charting effective strategies for current operations and future growth.
- Ride-hailing, or ride-booking, companies analyze transportation preferences, options, and costs in strategizing how much to charge and how many drivers they need at various times of the day and week.
- Restaurant chains combine data on sales trends, demographics, real estate transactions, and consumer surveys to determine where to open new locations or close underperforming ones.
The Role of Data Scientists
The importance of data science underscores the importance of data scientists. A big data set is only as good as the quality of its data, design, and analysis.
That’s why the human element is critical. Data scientists perform numerous roles in the collection and use of data, including.
- Determining which data to collect and how
- Creating and testing models for analyzing data
- Designing algorithms that make automatic decisions based on data
- Presenting findings through visualization software, such as dashboards
- Translating findings into strategic recommendations
Is Data Science a Good Career?
Because it’s used in so many industries, big data has become big business. Statista projects that by 2029, the market for big data analytics will be $655 billion worldwide.
It’s no surprise, then, that the demand for data scientists exceeds the supply. The BLS lists data scientist as the sixth fastest-growing occupation, projecting that data science jobs will grow by 36% between 2021 and 2031. By contrast, the overall job market is projected to grow by just 5%.
The BLS notes other key facts about data scientists.
- They’re well paid. Salaries in the field are well above average. The median annual salary for data scientists was $100,910 in 2021, compared with $45,760 for all occupations.
- They’re diverse. The possibilities for what you can do with a data science degree are many and varied. Computer companies represent only 15% of jobs in the field. Ten percent of data scientists work in management in various industries, while 9% work in insurance and other financial services.
- They’re well educated. Among data scientists, 86% have at least a bachelor’s degree, while 49% have a master’s degree or higher.
However, employment projections and salary figures aren’t the only reasons that data science is a good career. The profession also offers the opportunity to produce paradigm-shifting results within a given field. The following are examples of how data science stands on the cutting edge of many industries.
Computer vision technology analyzes data in the form of photos, videos, and graphics to take real-time action. It’s a feature of self-driving cars, and also has many industrial uses. BMW uses computer vision to inspect parts on its production line, saving time and money by identifying manufacturing errors.
Natural Language Processing
Natural language processing (NLP) treats speech and text as data, analyzing vast quantities far faster than human beings can read.
- Customer support teams use NLP to translate while communicating with consumers in other countries and other languages.
- Law firms employ NLP to proofread legal and corporate documents, making sure that the language complies with laws and regulations.
Deep learning is a subset of machine learning that uses multiple layers of algorithms to mimic the layers of the human brain. It enables more accurate insights and more nuanced decision-making than conventional machine learning.
It’s used in systems meant to operate without human intervention, such as self-driving cars. It also appears in digital assistants like Siri and Alexa to enhance their understanding of speech and make their responses more humanlike.
What You Can Do With a Data Science Degree: 4 Careers You Can Pursue
For individuals with a data science degree, big data offers big career opportunities. The wide range of technologies and industries that use data science means that what you can do with a data science degree is broad and diverse.
The wide range of job options allows individuals to choose the career that best aligns with their interests and skills. A look at some common jobs for data scientists illustrates both their variety and their earning potential.
Data Science Career Path No. 1: Social Media Data Analyst
In the age of Facebook, Twitter, and Instagram, social media has evolved into a crucial business marketing channel, thanks to the amount of user data these platforms generate.
Interpreting that data and using it to build a business and a brand is the job of a social media data analyst. An analyst may work for a social media platform or for a business that uses social media in its marketing.
What They Do
Typical duties for social media data analysts include the following:
- User engagement: tracking and interpreting user interactions with the platform to forecast trends
- Web analytics: reporting on user engagement metrics
- Strategy: using findings from analytics to develop and help implement marketing strategies
- Social media research: analyzing other social media platforms for ideas that can be applied to their own platform
- Policy: creating and documenting rules and standards for the organization, its employees, and its users
Social media data analysts may use these key skills:
- Data analysis: collecting and interpreting user data to drive business decisions
- Market research: monitoring broader social media trends and using them to help interpret internal data
- Computer literacy: being fluent in business software and online tools such as text and videoconferencing apps
- Search engine optimization: using keywords to enhance marketing through higher placement in search results
- Project management: working with multiple platforms at once and coordinating marketing efforts on those platforms
According to Payscale, the median annual salary for social media analysts was approximately $57,000 in February 2023, with the highest paid earning as much as $80,000.
Data Science Career Path No. 2: E-Commerce Data Specialist
In 2022, 15% of retail sales in the U.S. — totaling $266 billion — happened online, according to the U.S. Census Bureau. E-commerce data specialists analyze data on sales, customers, and shopping habits to develop marketing strategies for online selling platforms.
What They Do
The typical duties of e-commerce data specialists include the following:
- Data analysis: collecting metrics on an e-tailer’s customers and integrating them with data from other sources to personalize marketing pitches
- Market research: keeping up with general e-commerce developments, while gathering information on target consumers and how well the e-tailer’s products meet their needs
- Web design: using data insights to shape a company’s website and make it visually appealing, interactive, convenient to navigate, and easy to use
E-commerce data specialists may use these key skills:
- Data analysis: collecting and interpreting data on customers to market to them effectively and encourage repeat purchases
- Adaptability: recognizing sudden shifts in markets or customer habits and altering marketing strategies to take advantage of them
- Leadership: managing a team, which may include marketers, content writers, and web designers
- Communication: giving oral and visual presentations of analytics insights, marketing goals, and strategies to nondata professionals
According to Payscale, the median annual salary for social media analysts was about $56,000 in February 2023, with the highest paid earning as much as $78,000.
Data Science Career Path No. 3: Data Architect
Just as an architect designs a building to house people, a data architect designs data frameworks to house information. A framework may include multiple databases, and it may be for an entire company or for a specific app.
Representing one of the more advanced roles one can pursue with a data science degree, the data architect position is a high-level, big-picture role that’s responsible for creating the blueprints for data management systems. An architect works with others to execute the details of those plans, including data engineers, who build the systems, and database administrators, who run them.
What They Do
Typical duties for data architects include the following:
- Technical research: investigating business requirements and translating them into technical specifications
- Model building: constructing and coding models for databases
- Integrating: incorporating a company’s existing databases into a new system
- Testing: ensuring that a data system works as intended and correcting errors and inefficiencies
- Maintenance: revising and upgrading systems as data needs evolve
Data architects may use these key skills:
- Needs assessment: evaluating the kinds of information that a company needs and how it wishes to use them
- Design expertise: being familiar with various types of data systems and how to build and operate them
- Programming: writing code to translate a design into a database
- Creative thinking: coming up with novel solutions
- Communication: interviewing department heads to learn about their needs and educating co-workers about how to operate a data system
According to Payscale, the median annual salary for data architects was approximately $128,000 in February 2023, with the highest paid earning $167,000.
Data Science Career Path No. 4: Chief Data Officer
As big data has become central to business operations, a growing number of firms have moved it into the executive suite, alongside chief financial officers (CFOs) and chief operations officers (COOs). A chief data officer (CDO) sits at the top of a firm’s data operations, overseeing it and helping the company create value from it.
CDO is a relatively new corporate position, one that emerged within the last couple decades. A 2021 survey by management consulting firm PwC found that only 21% of the 2,500 largest companies had CDOs.
What They Do
The typical duties of chief data officers include the following:
- Governance: developing policies for data collection and storage
- Operations: managing data systems to make them useful, available, and efficient
- Quality: ensuring that data is accurate, complete, and consistent
- Analytics: interpreting data to derive business intelligence
- Security: protecting systems from unauthorized access, theft, or alteration of data
- Strategy: collaborating with others in management to incorporate analytics insights into company planning
CDOs may use these key skills:
- Management: running a department and teams while collaborating with other departments
- Communication: interacting with a wide variety of people, from technical experts to marketers and executives with no technical background
- Data analysis: exhibiting a deep understanding of how to collect and use data, identify high-level insights, and direct subordinate analysts
- Project management: coordinating multiple data system projects and schedules at the same time
According to Payscale, the median annual salary for CDOs was about $171,600 in February 2023, with the highest paid earning $234,000.
Prepare for a Career in a Game-Changing Field
The central role of data in today’s businesses has created both high demand for data science professionals and a wide variety of job opportunities. A degree program like the University of California, Riverside’s online Master of Science in Engineering and its data science concentration can equip a student with the advanced skills needed for a broad range of career paths.
The curriculum mixes core engineering classes with specialized data science classes, such as data mining, machine learning, and data visualization. Its online framework allows students to participate from any location while keeping their current jobs. Learn more about what you can do with a data science degree and how the University of California, Riverside can help you advance your career.