What Can You Do with a Master’s Degree in Data Science?

11 April, 2025

Many sectors have become extremely data-oriented as a result of advancing technologies. The practice of data science extracts knowledge from data, which can support organizational decision-making. Specifically, data science can help organizations to:

• Navigate change
• Improve performance
• Increase employee productivity
• Improve efficiency in operations
• Seek goals systematically
• Derive important insights from performance

Across many industries, data science professionals are tasked with important responsibilities. These professionals can hold a variety of titles, but typically work in roles that involve deriving directional insights from data that are used to impact an organization’s development.

In such a complex business landscape, professionals may find graduate education in data science to be a strong foundation for their skill development. Programs such as the UCLA Samueli School of Engineering’s online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering provide students with relevant and pragmatic skills required by a variety of industries.

Data science engineering master’s programs often focus on advanced technical skills, leadership competence and business strategy. These skills can be highly impactful in a variety of industries, and these degrees may prepare graduates to work in data science leadership positions.

What Is Data Science?

Data science is an interdisciplinary field encompassing a range of statistical, computational and machine learning techniques in order to extract knowledge from data. It is distinct from yet related to data analytics: where data analytics involves interpreting available data to inform decision-making, data scientists create the algorithms and models that allow data analysts to do their work. Data science also typically involves developing innovative solutions and predictive models through the use of:

• Big data
• Machine learning
• Programming
• Software tools
• Advanced mathematics

The interdisciplinary nature of data science means that it can be applied to a variety of industries, such as:

• Technology
• Finance
• Health care
• Energy

The Importance of Data Science

As businesses, nonprofits, learning institutions and other organizations seek to make better and more efficient decisions, data science has become critical. Data science technologies enable organizations to better organize, safeguard and analyze vast amounts of data. These data can be used for everything, from better understanding external landscapes to making impactful improvements within an organization.

Data science is helping solve some of the world’s largest challenges, such as climate change. The California Air Resources Board is working with organizations such as NASA’s Jet Propulsion Laboratory, Carbon Mapper and Planet to track climate change using satellites. This partnership uses data visualization and other interdisciplinary data science techniques to support climate control measures that could lower methane emissions.

Leveraging data-backed intelligence can also support an organization to more effectively reach their goals through strategic asset allocation. Employing a more competitive operational strategy in fiercely contentious markets can improve overall performance and reduce waste.

Merging Technical and Analytical Skills

Practicing data science requires knowledge in multiple distinct technical disciplines, in addition to general business and analytical knowledge. Technical skills are required to navigate data tools and softwares, including:

• Database software
• CRM software
• Deep-learning libraries such as PyTorch and TensorFlow
• Distributed computation systems such as MapReduce

Data scientists also will need to leverage communication, leadership and analytical skills in order to share insights with stakeholders, disseminate important information and support data analysts who are proposing data-driven decisions.

A master’s degree with a specialization in data science will provide students with vital education in data science topics, while also infusing business acumen and leadership skills into the curriculum. The curriculum of UCLA Samueli’s online M.S. in Engineering with Certificate of Specialization in Data Science Engineering offers a unique interdisciplinary approach that teaches students how to use data to solve real-world problems, optimize efficiency across industries and engineer positive change that improves lives.

Potential Data Science Careers

Data science is an interdisciplinary area requiring a broad set of skills, it can be used (and is in fact needed) across nearly every industry. For data science graduates, this means that there may be a wide variety of career paths to explore, ranging from highly technical engineering jobs to high-level leadership roles.

*Job titles, job postings and salary information are sourced from Lightcast, whose data come from millions of job postings from 2022 to 2024. Please note that salary will vary by experience, responsibilities, location and other factors. UCLA Samueli does not project job placements and salaries for its graduates.

Data Scientist

Median Annual Salary: $129,800 (Per Lightcast)

Each organization’s data scientists will have distinctive responsibilities and tasks. These can also vary by organization. However, many organizations rely on data scientists for tasks such as:

• Collecting and analyzing data
• Creating algorithms and data models for forecasting
• Leveraging machine learning techniques to improve the quality of data or product offerings
• Communicating recommendations to other teams and stakeholders
• Deploying data tools such as Python, R, SAS or SQL in data analysis

Data scientists leverage advanced technical skills in mathematics, programming and more to create new ways to obtain and analyze data.

Machine Learning Engineer

Median Annual Salary: $171,800 (Per Lightcast)

Machine learning (ML) engineers are responsible for designing, developing and modifying machine learning algorithms. Creating software that can adapt to data patterns requires strong skills in domains such as programming, mathematics, statistical analysis and computer science. ML engineers often use data science techniques such as deep learning and statistical modeling in their work.

Data Analyst

Median Annual Salary: $85,400 (Per Lightcast)

Data analysts operate in a role similar to data scientists. As such, there may be some overlap between the two roles. But in many businesses, data analysts are typically focused exclusively on analyzing existing data and providing actionable insights to business leaders based on those data.

Business Intelligence Analyst

Median Annual Salary: $94,100 (Per Lightcast)

Business intelligence analysts are specialized data analysts who primarily focus on sound decision-making in business. Business intelligence is varied in nature, but it typically encompasses data that focuses on success metrics and key performance indicators, such as:

• Revenue
• Sales
• Customer engagement
• Market trends

Software Engineer

Median Annual Salary: $129,900 (Per Lightcast)

Software engineers are tasked with improving, creating and implementing software. This requires software engineers to program software capable of meeting specifications outlined by stakeholders in a way that aligns with broader operational, organizational or mission-focused business goals.

Software engineers may leverage data science skills to create more effective code that is informed by user data, performance metrics or other relevant data. When collaborating with data scientists and analysts, software engineers can also leverage their understanding of data science to create systems that enable more effective data collection and analysis.

Chief Data Officer (CDO)

Median Annual Salary: $170,900 (Per Lightcast)

Chief data officers are executives who oversee how an organization collects, handles, stores, processes and analyzes data. This leadership role may involve leveraging management competencies and business knowledge to drive positive objectives for the organization. CDOs are also sometimes tasked with ensuring regulatory compliance with data security regulations. They may develop processes and parameters that enable the organization to adhere to relevant data privacy and security regulations.

The exact responsibilities of a CDO vary between organizations and is contingent upon what types of data are used by the organization, in addition to organizational goals and data infrastructure.

Big Data Engineer

Median Annual Salary: $135,900 (Per Lightcast)

These engineers work with big data, which is the designation given to datasets that are extremely large and/or diverse. Given the vastness of these datasets, big data engineers build effective and efficient systems that collect and maintain data in order to make it useful for organizations.
Big data engineers can have a direct impact on organizational success and profits. As such, they have important responsibilities such as:

• Designing, implementing and managing software systems and data architecture
• Creating algorithms to collect and process data
• Using programming and software tools to develop integrated data solutions
• Mining data from multiple areas to support efficient business models
• Collaborating with data analysts and scientists to develop actionable, data-driven insights

Data Science Industry Outlook

The career outlook for professionals in data science is very promising. The U.S. Bureau of Labor Statistics projects the employment of data scientists to grow 36% through 2033, which is significantly faster than the average for all occupations (4%).

The demand for data scientists is increasing for three main reasons:

• Organizations and individuals are producing — and collecting — more data than ever, meaning that they need experienced professionals to develop systems to organize data.
• Data-driven decision-making is a hot topic: Companies want to reduce wasted time and resources, so they need data scientists to support their strategy development.
• In order to leverage technologies such as AI and machine learning in support of data analysis, organizations need data science teams that deeply understand how to create, use and implement these tools.

The future of data science is bright, and those with a master’s degree in data science engineering will develop the relevant and pragmatic skills needed to succeed in data science roles across industries. Data science master’s degrees incorporate business strategy and leadership competencies in addition to technical knowledge, making education in this area highly applicable across various industries, including technology, finance, health care and energy.

Why Choose UCLA Samueli for Your Data Science Education?

The UCLA Samueli School of Engineering offers an online Master of Science in Engineering with Certificate of Specialization in Data Science Engineering (MSOL: DATA SCIENCE ENGR) that prepares students to solve problems and harness data ethically using responsible artificial intelligence through a curriculum of five core courses and four electives.

The UCLA Samueli School of Engineering is a tightly knit community of nearly 200 full-time faculty members, more than 6,500 undergraduate and graduate students, as well as 40,000 active alumni. Known as the Birthplace of the Internet, UCLA Samueli is also where countless other fields took some of their first steps — from artificial intelligence to reverse osmosis, from mobile communications to human prosthetics. In 2021, UCLA became the first university to win an XPRIZE, with a UCLA Samueli team awarded a $7.5 million grand prize in the NRG COSIA Carbon XPRIZE.

Times Higher Education lists UCLA among the top 10 universities for engineering in the U.S. and top 20 in the world. U.S. News & World Report has ranked the overall Master of Science in Engineering Online No. 1 in the nation.

Request a program brochure to learn more, or start the application process today.