MEET THE GROWING DEMAND FOR SPATIAL DATA SCIENCE EXPERTS

Build expertise in spatial data science, GIS analytics and geospatial data visualization — 100% online and in just two semesters. As a leading education provider in this fast-evolving industry, Purdue University prepares you to seize emerging opportunities in data science for agriculture, land-use management and big data applications in many fields. 

Quick Facts

Program Length: 2 Semesters

Start Dates: 2 Times per Year

Learning Format: 100% Online

USE BIG DATA TO MAKE A BIG IMPACT IN AGRICULTURE AND NATURAL RESOURCES MANAGEMENT

Purdue’s online graduate certificate in Spatial Data Science provides students with vital skills in data science and helps them master the technologies they’ll use in the field. Learn core concepts of geographic information systems (GIS) and spatial data science, including data sources, projections, spatial data processing and analysis methods, data and metadata creation and a conceptual framework for solving spatial problems.

Students will also learn foundational skills in scripting languages, data types, databases, and data visualization and analysis. By the end of the program, students will be able to collect, analyze, interpret, and combine geospatial data to make informed decisions regarding natural resource management and solve problems in the environmental, agricultural and engineering sectors.

The certificate combines technical skills in data science with in-depth coverage of issues related to natural resource management, including remote sensing, threats to biodiversity locally and globally, field observation, and other skills that agricultural and resource management professionals need to be effective in the field.

Contact Information

Email: onlineadmissions@purdue.edu

Phone: 765-496-0990

Award
Quality

#1 in Agricultural / Biological Engineering Program

U.S. News & World Report, 2022
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Industry

#2 in Best College for Precision Agriculture

PrecisionAg.com, 2018
Scientific Microscope
Innovation

#8 Most Innovative University Nationwide

U.S. News & World Report, 2021
Award
Quality

#1 in Agricultural / Biological Engineering Program

U.S. News & World Report, 2022

Icon of Flower and Stem
Industry

#2 in Best College for Precision Agriculture

PrecisionAg.com, 2018

Scientific Microscope
Innovation

#8 Most Innovative University Nationwide

U.S. News & World Report, 2021

Plan of Study

The cross-disciplinary curriculum blends coursework from our Forestry and Natural Resources (FNR), Agricultural and Biological Engineering (ABE), Agricultural Systems Management (ASM) and Agronomy (AGRY) departments. Students are encouraged to take ASM 54000 and FNR 58700 in the fall semester and ABE 65100 and AGRY 54500 in the spring semester. Totaling in 12 credits.

Students can also choose to continue their education and add a Master's in Applied Geospatial Analytics to your plan of study.

This course provides an introduction to fundamentals of geographic information systems (GIS) for spatially analyzing problems related to environmental, agricultural and engineering domains. You will learn key concepts of GIS, including data sources, projections, spatial analysis methods, data and metadata creation and conceptualization framework for solving spatial problems.

GIS is a powerful tool and most students find it to be interesting and enjoyable. The course will use Esri ArcGIS Pro software. At the end of the course we expect you to be an informed GIS user, as well as being reasonably competent using ArcGIS Pro.

Introduction to the principles of landscape ecology and biogeography with a laboratory devoted to the analysis of spatial data using geographic information systems (GIS) and other database tools.

Landscape ecology focuses on the important relationships of landscape structure (pattern, heterogeneity) and ecological processes (movement of animals, hydrologic dynamics) and how this information is used for natural resource management. Biogeography examines ecological patterns and processes at larger scales (generally at subcontinental to global) for the purposes of managing plants and animals of global importance.

In the last 15 years, tremendous efforts have been made to create spatial databases that help support research and management of natural resources at various scales. The lab will focus on the use and application of these databases that are common in natural resource management settings.

This course will educate students in the use, manipulation and analysis of environmental data by introducing them to scripting languages (e.g., C shell, Python), data types (e.g., ASCII, binary, NetCDF), databases (e.g., XML, DBF) and data visualization software (e.g., GMT, ArcMap) as well as techniques for checking data quality, working with missing data and handling large diverse sources of time series and spatial data.

Students will manipulate, check and insert data from a variety of sources, use that data as input to distributed hydrologic model, analyze model output and learn methods for properly documenting their data use (creation of metadata) and long-term archival storage of those data. Skills learned should be applicable to most computer operating systems, but the majority of work for this class will be done within the Unix/Linux environment.

Students taking this course should have experience with one or more programming languages, including but not limited to C, Fortran, Perl, Python, Java, BASIC or two writing scripts or macros within programs such as MATLAB, S-PLUS, R or SAS.

This course introduces students to the principles of remote sensing and teaches methods for analysis and interpretation of remotely sensed data. The emphasis of the first half of the course is on passive optical technology and methodology for analysis of remotely sensed data.

The second half of the course introduces other sensing technologies and their application to the remote observation of soil, vegetation and water resources (together referred to as land resources) by airborne (manned and unmanned) and space-based sensors.

Students will be introduced to the latest developments in instrumentation and information technology in remote sensing and will learn how to utilize remotely sensed data to support research and decision making in agriculture, science and engineering.

Distinguished Faculty 

Purdue University College of Agriculture faculty members are nationally and internationally acclaimed researchers and teachers. Their accomplishments make Purdue Agriculture a leader at the local, state, national and international levels in food, agricultural, life and natural resources sciences. Our faculty include three World Food Prize laureates and many other distinguished professors.

Admissions Requirements

To be considered for admission into the Spatial Data Science Graduate Certificate program, you must have a bachelor’s degree from a regionally accredited institution with a GPA of 3.0 or higher. You will also need to submit the following items:

If you have a bachelor's degree that is not in agriculture, forestry, life sciences or a closely related field, a GPA below 3.0, you must also submit two letters of recommendation (one from a supervisor).

Tuition and Fees

Indiana Residents Out-of-State Residents
  Cost per Credit Hour $750 $775
  Credit Hours 12 12
  Total Cost* $9,000 $9,300


*Textbook fees are not included in the price. Tuition and fees are subject to change by the Purdue University Board of Trustees. Rates are for the 2022-2023 academic year.

 

Apply Today

Enrollment is open in Spring and Fall

Graduate Application