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M.S. in Data Intelligence and GeoAnalytics

Saint Mary’s University of Minnesota has long been a leader in providing students with the educational foundation and in-field experience necessary for successful careers and leadership positions in Data Intelligence and GeoAnalytics. 

Interest in the field has increased significantly with the U.S. Department of Labor predicting an annual growth rate of 35 percent for the geospatial technology industry. In addition, analytical technologies are predicted for current and future growth (NMC Horizon Report, 2018) and according to IBM (2017), by 2020, there will be approximately 2,720,000 jobs available in fields served by data professionals. 

Post-Degree Opportunities

Career paths for students who obtain their Master of Science in Data Intelligence and GeoAnalytics are quite varied and diversified including careers in information technology, data science, business analytics, asset and natural resource management, market analysts, urban planning, cloud/network administrators, public safety and emergency management, and surveying/drone data collection to name a few. Regardless of your profession, your Saint Mary’s education will give you:

  • A comprehensive understanding of effective research strategies
  • Real-world examples and hands-on experience to promote the efficient use of technology analytics and intelligence frameworks used in decision making
  • Thorough knowledge of data development techniques, analysis, and evaluation
  • Applied in-depth experience with geospatial technology in multiple fields ranging from health, natural resources, and business to urban planning, emergency management, and public policy
  • Experience in connecting concepts of data collection, data engineering, data science, and geoanalytics
  • Learning integrated technologies such as Python, R Programming/Scripting, SQL, JavaScript and web API’s, cloud architecture, data management via mobile devices, drones/sensors via IOT, and geo-artificial intelligence and data classifications.

In addition to the classroom environment, Saint Mary’s provides the option for applied learning experience through the university’s unique GeoSpatial Services affiliate, which gives students the opportunity to work in a mentored environment on active client projects. Saint Mary’s real-world experience, combined with networking opportunities with potential employers and alums, no doubt gives its students an advantage when beginning the career search.

GIS Graduate Certificate

For those students who are looking for a thorough background in GIS theory and use but are not seeking a degree, we encourage you to learn more about Saint Mary’s Graduate Certificate in Geographic Information Science. Successful completion of the certificate program will allow you to apply for professional GIS certification through the GIS Certification Institute.

Salary Survey
2017-2018

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Job Placement Information

Job Placement Information

Projected Job Growth: 2016-2026

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From Start to Finish

  • You can earn your M.S. in Data Intelligence and GeoAnalytics degree in less than two years.
  • Cohorts begin each fall, spring, and summer.

Applicants must submit the following:

  1. Completed application form with the nonrefundable application fee (fee not required for alumni or students seeking readmission or veterans and active military personnel), and
  2. An official transcript issued to Saint Mary’s University of Minnesota from the institution posting the applicant’s completed bachelor degree and other relevant transcripts documenting program prerequisites and potential transfer credits.(An official transcript is one that is sent to the university by the credit-granting institution. Transcripts from countries other than the U.S. must be evaluated by a university accepted evaluation source, such as World Education Services, Educational Credential Evaluators, Educational Perspectives, or One Earth International Credential Evaluators and be deemed equivalent to accredited U.S. university standards).
  3. A reflective essay which includes the following:
    • brief description of the applicant’s background, training, and experience; and
    • statement indicating the career goals of the applicant and his or her reasons for seeking admission to the program; and
    • description of the areas the applicant considers to be his or her strengths and areas in which the applicant wishes to develop greater strengths and abilities; and
    • personal information the applicant wishes to share.
  4. Two letters of recommendation that verify professional and/or volunteer experience and academic ability; and
  5. A current résumé listing educational background and work experience.
  6. Applicants with international transcripts may require an English language proficiency exam (TOEFL, IELTS, PTE or MELAB accepted.)

Please Note: Application materials should be sent to the attention of the Office of Admission on the Twin Cities campus.

Saint Mary’s University of Minnesota
Office of Admission
2500 Park Avenue
Minneapolis, MN  55404

Locations

This program is offered at our Twin Cities and Winona locations.

Degree Requirements

Degree Requirements

Required Core Program Courses 33 cr.
Contextual Application Elective Courses 3 cr.
Total 36 cr.

 


Required Core Program Courses: 33 cr.

DIGA605 Fundamentals of Geographic Information Systems (GIS) (3 cr.)

This course introduces the concepts of spatial data creation, editing, and analysis using GIS software. Emphasis is placed on spatial concepts and understanding and utilizing standard operating procedures. Topics covered include coordinate systems, data creation, derivation, editing, metadata, proximity and overlay analysis, and cartography. Technical proficiency is a primary objective of the course, reinforced by significant practical exercises utilizing GIS software. Examples of how the geospatial industry provides location intelligence to a variety of disciplines are explored.

Upon completion of this course students are expected to be able to do the following:

  1. Apply knowledge of principles, theories, and concepts of spatial data analysis.
  2. Demonstrate standard techniques for creating, editing, storing, querying, and analyzing geospatial data.
  3. Uses cartographic design principles for visual storytelling and effective communication.
  4. Implement practices to promote spatial data integrity based on an understanding of sources of error in spatial data.

DIGA610 Relational Database Design and Administration (3 cr.)

This course follows a logical progression from basic data types into normalization and relational geodatabase design.  The course explores the role of various tabular structures, from simple flat files to the relational geodatabase.  The course explores implementing methods to promote spatial and tabular data integrity and facilitate analytical workflows. Standard query language (SQL) for querying, modifying, and managing data is also covered. The course includes enterprise geodatabase topics such as permissions, versioning, replication, and archiving.

Upon completion of this course, students are expected to be able to do the following:

  1. Demonstrate knowledge of database terminology, design techniques, and data issues.
  2. Collect, format, manage, and implement both spatial and tabular data within a GIS.
  3. Design and develop geodatabases that promote data integrity and usability.
  4. Demonstrate a basic knowledge of relational database management systems.
  5. Use basic standard query language to manage and query databases.
  6. Plan and implement databases to meet specifications of various stakeholders.

DIGA615 Data Acquisition and Location of Things (3 cr.)

This course introduces methods centered around data collection in a geo-relational context. Data collection topics include applications centered around mobile global positioning systems (GPS), land and parcel data, sensors and drone data acquisition, and data generated within the Internet of Things (IOT). The course discusses concepts in understanding workflow, critical appraisal of data, and applications for various industries.

Upon completion of the course students are expected to be able to do the following:

  1. Create data suitable for designing projects.
  2. Utilize approaches for data validation.
  3. Apply best practice for capturing, utilizing, and automating geospatial data.
  4. Evaluate multiple technology options to collect data for projects or research.
  5. Communicate effectively with data, graphics, and technical reports.

DIGA620 Data Engineering (3 cr.)

The course utilizes data processing requirements necessary to implement technology-based analytics. The course explores strengths and limitations of various data formats to make better decisions. The importance of structured and unstructured data formats as well as performing methods of data extraction, transformation, and loading are covered. Data wrangling methodologies explore constructing custom data pipelines to support efficient analysis. These methods include cleaning, filtering, standardizing, and categorizing data. Processes to review data for accuracy, consistency, and completeness are covered as well as techniques to mitigate error and improve data integrity.

Upon completion of the course students are expected to be able to do the following:

  1. Perform extract, transform, and load (ETL) processes using structure and unstructured data formats.
  2. Assess data for error and implement techniques to improve data integrity.
  3. Determine appropriate data formats for given situations.
  4. Design and document processes for converting raw data into a product suitable for analysis.

DIGA625 Python Programming for Technology Applications (3 cr.)

The course focuses on core programming concepts such as classes and objects, controlling flow, user input, batch processing, and error handling while working in the context of data processing, analysis, and visualization. The course explores a variety of Python packages and integration for project development. Custom workflows and visualizations for both spatial and tabular data are also discussed.

Upon completion of the course students are expected to be able to do the following:

  1. Develop custom visualizations that communicate data and results of an analysis.
  2. Respond to specific scripting requirements to address analytical problems and improve workflows.
  3. Apply the concepts and logic of data science principles for object-oriented, event-driven programming.

DIGA630 Advanced GeoSpatial Data and Location Analytics (3 cr.)

This course promotes exploration and utilization of advanced functionality of GIS technology. Substantial effort is directed toward developing proficiency in understanding data at complex levels with an emphasis on advanced raster and spatial analysis. The course covers advanced GIS analysis, image analysis techniques, and geospatial topic-specific areas of study.

Upon completion of this course, students are expected to be able to do the following:  

  1. Analyze geospatial data through principles, theories, and concepts.
  2. Understand basic and advanced GIS analysis techniques applied to various industries.
  3. Identify benefits and disadvantages to working with diverse data sets.
  4. Use cartographic design principles for visual storytelling and effective communication.
  5. Evaluate imagery and remote sensing techniques for data generation.

DIGA635 Data Modeling and Forecasting with Geo-AI (Artificial Intelligence) (3 cr.)

This course promotes problem solving, data modeling, and critical thinking related to data intelligence, data classifications, and predictive analysis. The course explores designing conceptual models to effectively explore and forecast data unique to spatial and other analytical challenges. The course addresses challenges in various disciplines. Problem-solving approaches are accomplished using an array of technology and software options.

Upon completion of the course, students are expected to be able to do the following:

  1. Apply principles, theories, and concepts to various data analyses.
  2. Develop programming scripts and interfaces.
  3. Utilize a variety of diverse data, software, and technology for visual and data intelligence.
  4. Assess data, designs, and outcomes for decision making.

DIGA640 Technical Research Writing, Design, and Ethics (3 cr.)

This course examines effective research methodologies used in understanding requirements and expectations associated with the capstone project. This includes formatting and graphic requirements, literature review development, proposal design, and expectations of the final project paper.  Additional emphases include applied ethics of technology use and research design. The course prepares and provides progress for students on their capstone project.

Upon completion of the course students are expected to be able to do the following:  

  1. Evaluate and synthesize research to prepare a literature review.
  2. Develop a project proposal that can be addressed using appropriate forms of analytics.
  3. Apply ethical frameworks for decision-making in technology use and research design.
  4. Plan processes for managing technical projects.
  5. Demonstrate ability to create and follow project specifications

DIGA645 R Programming for Technology Applications (3 cr.)

The course focuses on R programming concepts for analytical and statistical applications involving spatial data and non-spatial data used in various technology fields. The course features scripts and data model applications useful in promoting decision making, integrating R into technology software platforms, and focusing on effective communication through visual intelligence.  

Upon completion of this course, students are expected to be able to do the following:

  1. Develop general proficiency in using R programming for data analysis.
  2. Articulate the value of R programming for data modeling in various industries.
  3. Develop applications for data analytics and visualization.
  4. Recognize various data types used in R programming.

DIGA694 Capstone Project (3 cr.)

In this course, students implement the project proposal created in DIGA640, write the capstone paper, and present findings. Research Project tasks may include, but are not limited to collecting or mining data, developing appropriate evaluations of data and/or technology, and to infer meaningful outcomes of the project goals/results.  Research Project findings are written in a scientific journal-style project paper that conforms to the program handbook.

Upon completion of the course students are expected to be able to do the following: 

  1. Implement an effective analytics methodology for a technology or data-centered project.
  2. Apply ethical principles and frameworks for decision making.
  3. Implement processes for managing technical projects.
  4. Demonstrate ability to create and follow project specifications.
  5. Defend project methods and outcomes using both oral and written means according to program recommendations and standards.

GM630 Quantitative Methods (3 cr.)

This course focuses on statistical analysis of data for professional applications or research with an emphasis on quantitative methodologies. The course covers populations, sample selection, and descriptive and inferential statistics. Significance, Chi Square, correlations, analysis of variance and simple regression, and concepts of reliability, validity, and levels of measurement are addressed.

Upon completion of this course, students are expected to be able to do the following:

  1. Apply statistical ideas and practicalities to real-world quantitative situations within organizations.
  2. Read and interpret the statistical content of literature relating to management of people and resources.
  3. Analyze statistics through performing basic computation both by hand and with computer software.
  4. Determine and apply the appropriate inferential analysis for different types of data and derive correct conclusions.

Contextual Application Elective Courses: 3 cr.

DIGA607 Public Health Analytics (3 cr.)

This course explores the role of location analytics and its impact in public health to address and explore health-related issues and their geo-centered relationships. Course discussions focus on understanding tabular and spatial data, as well as organizational data strategies organizations can follow for improved awareness of public health needs and analytics. Applications may include patient care location-based information, demographics, industry effectiveness in reaching populations in need, insurance, community planning, competition of resources, etc. The course utilizes processes, software, and data requirements necessary to implement technology-based analytics.

Upon completion of the course students are expected to be able to do the following:

  1. Apply knowledge of principles, theories, and concepts of health data to leverage locational intelligence.
  2. Articulate the role of data and demographics used in decision-making.
  3. Analyze visual and spatial patterns using statistics and spatial data.
  4. Implement technical strategies to apply data in various applications related to the health industry.

DIGA608 Cloud Architecture, Web-Programming, and Visualization Analytics (3 cr.)

This course introduces students to effective use of the internet and technology for sharing spatial and non-spatial data, visualizations, and interactive applications via the web. The course examines cloud data architecture for managing, analyzing, and serving data over the Internet. Students are introduced to web-based programming languages and interfaces (APIs) for presentation, visual analysis/intelligence, and communication/presentation of data via the web.

Upon completion of this course students are expected to be able to do the following:

  1. Develop custom web applications and functional user interfaces.
  2. Use cloud technologies for leveraging data management and analysis principles.
  3. Apply visualization strategies to present and communicate information for target audiences.
  4. Create workflow designs for web and cloud architecture to improve performance of services and applications.

DIGA609 Policy and Political Analytics (3 cr.)

This course explores integrating location-based analytics with institutional policy-making impacts on stakeholders or public demographics. Demographic and policy analysis is key for understanding decision-making efforts on local, state, or national levels. Course applications and discussions include various topics to include but are not limited to political and policy strategies and impacts on participation in policies or programs, statistical and qualitative analyses of elections, registration, and voter turnout, understanding relationships and impacts of various local, state, and federal agencies, grassroots movements, and advocacy organizations. The course utilizes processes, software, and data requirements necessary to implement technology-based analytics.

Upon completion of the course students are expected to be able to do the following:

  1. Apply knowledge of principles, theories, and concepts of demographic data to leverage locational intelligence.
  2. Articulate the role of political, policy, and demographic data used in decision making.
  3. Analyze visual and spatial patterns using statistics and spatial data.
  4. Implement strategies to apply data in various applications related to local, state, and federal initiatives.

Connect With Us

Nicole Coppersmith, M.A.

SGPP Admission - Senior Enrollment and Transfer Counselor

Oakdale Center, OC

Campus Box: # 28

(612) 238-4561

ncoppers@smumn.edu

Nicole Coppersmith M.A.
Carlie Derouin

SGPP Admission - Enrollment Counselor, Graduate School of Business and Technology

LaSalle Hall-TC Campus, LSH112

Campus Box: # 28

(612) 728-5198

cderouin@smumn.edu

Carlie Derouin
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