Leverage the Power of Data to Engineer Positive Change

The art of data science has been around since the beginning of human existence. People make observations about their surroundings, create a model and make predictions to manage uncertainty. As technology has advanced, so has the amount of data that can be mined for insights and optimizations, and with that, the tools needed to procure, visualize, analyze and predict have also revolutionized. With the extensive transformation the field has undergone over the last decade, comes the responsibility to leverage the power of data for positive change in society.

Yes, this is a part-time, fully online degree program intended for working professionals. Classes can be accessed asynchronously from anywhere in the world. However, students are required to meet regular deadlines for submitting assignments, and exams are proctored in person at their local testing centers. Optional live components include orientation, faculty office hours, bonus lectures and graduation.

• Online application
• Official transcripts from all previously attended colleges or universities
• Résumé
• Three professional or academic letters of recommendation
• Personal statement
• Statement of purpose
• GRE general score*
• TOEFL/IELTS test scores (international students only)
• Application fee**
• $135 for U.S. citizens and permanent residents, $155 all other applicants

*The GRE requirement may be waived for applicants who have highly competitive holistic portfolios and such requests will be reviewed on a case-by-case basis. Contact an enrollment specialist for more information.

**Application fee waivers are available for applicants who are current or former participants in certain programs or who demonstrate financial need. Contact an enrollment specialist for more information.

See the admissions page for details.

Data science job growth has been much faster than the average profession in the nation — and for good reason. MarketsandMarkets recently reported that the U.S. Big Data market size is projected to grow from $162.6 billion in 2021 to $273.4 billion in 2026 and accounted for 51% of the big data market spending in 2021. With an endless supply of large-scale data and organizations wanting to find ways to continually optimize, it’s no wonder that the need for knowledgeable data scientists is at an all-time high.

However, scientists and engineers in this program learn to do much more than optimize efficiencies in their organization. Students gain the skills needed to help find solutions for societal challenges including the need for accessible health care and quality education, the lack of a sustainable environment and responsible artificial intelligence.

Our program can be beneficial to your current role, while also opening the door to many careers in data science. Some of the top jobs with a data science degree according to Lightcast include:

• Data analyst
• Data scientist
• Data engineer
• Database architect
• Network engineer
• Machine learning engineer
• Quantitative analyst

In general, data science and data analytics are interconnected fields that serve different purposes.

• Data science is an interdisciplinary field that combines statistical, computational and machine learning techniques. It is used to develop technologies that extract knowledge from data.
• Data analytics is the use of theories, tools and technology to extract insights from data. It is used to inform business decision-making processes.

Data science programs are typically more mathematically rigorous, emphasizing programming, statistics and machine learning. Data analytics programs may focus more on business acumen, data visualization and communication skills. However, individual programs will vary in terms of their focus and curriculum.

For a more detailed explanation, review the article, “Demystifying Data: A Guide to Choosing Between Data Science vs. Data Analytics.”

Data science and data engineering are distinct but complementary disciplines:

• Data science involves developing models, performing statistical analysis and applying machine learning to extract insights from data. According to IBM, data scientists study large datasets using advanced statistical analysis and machine learning algorithms to identify patterns that drive business insights.
• Data engineering involves building the infrastructure and systems that collect, store and process data at scale. Data engineers construct robust data pipelines and create efficient data warehouses, according to the IEEE Computer Society.

Data science programs typically emphasize statistics, mathematics and machine learning algorithms. Data engineering programs focus on software development, distributed systems and database architecture.

The fields often work in tandem: engineering builds the systems that enable analysis. The UCLA MSOL: DATA SCIENCE ENGR program focuses on the data science side of that relationship, while coursework in database systems and distributed computing prepares students to collaborate with engineering teams or participate in engineering projects.