Data Science Programs:
Solving data-intensive, large-scale, location-based problems
Geospatial data accessibility, spatial decision support systems, and geospatial problem-solving environments are revolutionizing most industries and disciplines, including health care, marketing, social services, human security, education, environmental sustainability, and transportation.
In order to solve data-intensive, large-scale, location-based problems, spatial data science professionals draw upon engineering, computer science, math, and spatial science principles
Career Opportunities in Spatial Data Science
Glassdoor users rated "data scientist" as the most satisfying job in the in “Data science and Business Analytics” field; with an average base pay of $121,000/year and 4,100+ openings.
As an example of the growing importance of spatial data science, the Southern California Association of Governments (SCAG) is hosting data science students from regional universities to support a new region-wide initiative around open and big data in the coming years. Through a generous contribution by Randall Lewis, the fellows selected will play an important role in supporting governments across the region to become more data driven and effective in the provision of their services. For more information about the Randall Lewis Data Science Fellowship, click here.
Meet Alumni of Master of Science in Spatial Data Science Program
The USC Master of Science in Spatial Data Science provides students with the knowledge and skills to:
- Understand and contribute toward the significant technical and societal challenges created by large location-based data environments, including architecture, security, integrity, management, scalability;
- Understand how spatial data can be acquired and used to support various forms of analysis, modeling and geo-visualization in large data environments; and
- Understand how artificial intelligence, machine learning, and data mining can be used to augment the typical geographic information science (GIS) concepts and workflows to intelligently mine data to provide enterprise-centric solutions for a variety of societal challenges and issues spanning the public, private and not-for-profit sectors.
Upon graduation, students will have not only data science skills but will be uniquely qualified to lead data science teams in companies and organizations working with geolocated information, conducting data analytics in startups and tech companies with location-based data, and getting involved with emerging technologies revolving around spatial data.
Students complete a core set of courses to provide a foundation in information engineering, spatial analysis and thinking with their choice of electives to optimize their preparation for their preferred career path and unique professional opportunities.
Students will understand the overall field of data science, the role of the analyst and/or data scientist, and the domains where spatial data science skills can be applied to critical organization missions. They will understand how data management, data visualization and artificial intelligence techniques (specifically data mining and machine learning) are critical to the spatial analysis process and how these can be applied to real world challenges. Throughout their course work, students will assemble a digital portfolio of work product which is intended to help them demonstrate their capabilities and skills for the job market.
An Inter-Disciplinary Program
The Master of Science in Spatial Data Science is a joint degree program offered by the Viterbi School of Engineering and the Dornsife College of Letters, Arts and Sciences. Students must be admitted by both the Viterbi School of Engineering and the Dornsife College of Letters, Arts and Sciences.
A total of 32 graduate units is required for the M.S. in Spatial Data Science program. A minimum cumulative GPA of 3.00 is required for graduation.
Required Courses: (6 courses/24 units)
Foundation (take both courses):
Spatial Core (take both courses):
Data Science Core (take both courses):
Recommended Preparation: Basic understanding of engineering and/or technology principles; basic programming skills; background in probability, statistics, linear algebra and machine learning.
Spatial Elective Courses: (Take 4 units)
Data Science Elective Courses: (Take 4 units)
Requirements for graduation, course offerings, course availability, track offerings and any other degree requirements are subject to change. Students should consult with an academic advisor in the Viterbi School of Engineering or in the Spatial Sciences Institute prior to registering for any classes.
- An undergraduate degree in STEM (science, technology, engineering, and math) or related social science from a regionally-accredited university.
- Satisfactory cumulative undergraduate GPA (grade point average).
- Satisfactory GRE test scores. All scores must be officially reported to the University directly by ETS.
Programming experience or at least a year of calculus is required for admission.
The curriculum is designed to be accessible to students with any background, including students with a spatial sciences background and no computer science knowledge as well as students with a computer science background and no spatial sciences knowledge. Students with undergraduate degrees in computer science, engineering, science or mathematics will acquire the necessary knowledge of spatial sciences through the curriculum, and can request to replace introductory data science courses with more advanced ones. Students with undergraduate degrees in spatial sciences, geography, or social sciences will acquire formal and practical data science skills, and can request to substitute introductory courses in spatial sciences with more advanced ones.
Students must be admitted by both the Viterbi School of Engineering and the Dornsife College of Letters, Arts and Sciences. Applications are accepted for admissions in the fall and spring semesters.
Data Science Programs Application Materials
- All applicants must complete and submit the USC Online Application.
- Transcripts: Official transcripts from all colleges and universities attended, sealed by the institution. Mail transcripts to: USC Graduate Office of Admission University Park Campus Los Angeles, CA 90089-0915 If sending via a private carrier (e.g., UPS, FedEx, or DHL), send transcripts to: University of Southern California Office of Admission & Financial Aid 3601 South Flower Street Tyler 1 Los Angeles, CA 90089-0915
- GRE General Test: Satisfactory scores less than five years old. Official scores must be reported from ETS directly to USC using ETS school code 4852. A department code is not required.
- Letters of Recommendation (Optional): Letters of recommendation should be from faculty or others (supervisors, professional colleagues, etc.) qualified to evaluate your potential for graduate study. They should be submitted through the online graduate application.
- Statement of Purpose (Optional): The statement of purpose should describe succinctly your reasons for applying to the proposed data science program, your preparation for this field of study, study interests, future career plans, and other aspects of your background and interests which may aid the admissions committee in evaluating your aptitude and motivation for graduate study.
Additional Application Materials for International Applicants
English Language Proficiency: In addition to the general admission criteria listed above, international students whose first language is not English are required to take the TOEFL or IELTS examination to be considered a candidate for admission. There is no minimum TOEFL or IELTS score required for admission to the Viterbi School and Dornsife College. For possible exemption from additional language requirements, you must achieve an Internet Based TOEFL (iBT) score of 90, with no less than 20 on each section or an IELTS score of 6.5, with no less than 6 on each band score. For more details on English Proficiency Criteria for the University of Southern California, please visit USC Graduate Admission - Proficiency in English.
Want to learn more? Email Ken Watson, Academic Programs Director, at email@example.com.