Data Science, Analytics and Visualization (DS) Courses | Chaminade University

Course Descriptions

Data Science, Analytics and Visualization (DS)

DS 100 Introduction to Technology and Innovation Past, Present, and Future (3)

This course is an introduction to data science that will cover data science topics. This course will include lectures, discussions, assignments, and a project that could be used for future classes and investigation. The goals of the course are to provide an overview of data science to familiarize the students with the depth and breadth of data science and prepare students for the next data science courses and data science practice. Students in this course will learn what is data science and why it is important; project management; data sets and big data; data curation; data ingestion and wrangling; databases, languages, and practices; data analytics, visualization, tools, and languages; data mining; interpretation and reporting results; cybersecurity, policies, and ethics; and machine learning and artificial intelligence.

DS 101 Data Lifecycle (3)

This course is an introduction to data lifecycle, data structures, and data analytics that will cover data topics. This course will include lectures, discussions, assignments, and a project that could be used for future classes and investigation. Students in this course will learn the data lifecycle; data planning process; data security, ethics, and policies; generation and collecting data; cleaning, formatting, and preparing data; data storage and structures; data management; data analysis, visualization, interpretation; communicating and storytelling data results; and sharing, publishing, and preserving data.

DS 122 Data Feminism (3)

Data is generated and used all around us, but not always inequitable ways. With examples that range from health and law to city planning and product design, this course will cover some of the ways women and other minorities are ignored and excluded from data and decision-making processes. Students will research and lead discussions about an issue that is important to them and ultimately design a campaign to raise awareness about the disparities.

DS 200 Data Ethics (3)

This course is an introduction to ethics and policies in data science and cybersecurity that will cover philosophy, ethics, policy topics on data science and cybersecurity. This course will include lectures, discussions, discussion papers, and a project. Students will develop, expand, and/or enhance their ethical compass, conceptual framework, and policy analysis abilities. Students in this course will use their culture and identity, philosophies, principles, and theories as tools to help in evaluating the ethical dilemmas and policies provided as case studies in this class.

DS 300 Ethics Seminar (3)

Seminar course following on and further developing concepts and skills in DS 200. Students will perform a service project in data ethics. Prerequisites: DS-200.

DS 302 Data Journalism (3)

Lecture course addressing the use and misuse of data and statistics in media. Data inputs into, and impact upon, the journalistic and communications process will be addressed through case studies. Data distillation techniques and storyboarding will be covered, with practical examples. Cross-listed with COM-302. Prerequisites: EN 102, COM 101, DS 200 and MA 331

DS 230 Foundations of Geospatial Thinking (3)

This course introduces geographic perspectives that are foundational to Geographic Information Systems, including: human-environment interactions, spatial thinking, and systems thinking. Students will learn about the power of maps as communicative tools, and the ethical issues in the field of cartography. Key theoretical topics will include the spatial side of: systems, processes, distributions, clusters, movement, and networks with special attention to how cultural, biological, and earth systems interact. Students will interact with existing geographic data portals to explore topics in a region of interest to them. No previous experience in geography or data science is needed. This course will prepare students for any GIS course with the ability to think and communicate from geographic perspectives. This course has no prerequisites.

DS 231 Cartographic Design (3)

This course provides an overview of design best practices in cartography (map-making). Students will learn how to choose: the best type of map, the appropriate scale and projection, and the most effective layout and format to represent the underlying data. Topics will include histories and cultures of cartography, how the human brain interprets maps, the importance of datum and projection, key elements of maps, and formatting design. Students will compare the purpose and functionality of interactive v. static maps. Students will evaluate existing maps for use of best practices, produce their own static map using GIS software, and make improvements to their map over multiple iterations with feedback from fellow students and the instructor. No previous experience in geography or data science is needed. This course will prepare students for any GIS course with the ability to produce accurate and effective maps. This course has no prerequisites.

DS 300 Ethics Seminar (1)

Seminar course following on and further developing concepts and skills in DS 200. Students will perform a service project in data ethics.
Prerequisites: DS 200

DS 301 Community-Engaged Computing: Decision Support & Stakeholder Engagement (3)

Lecture course addressing the use of data analytics, visualization and visualization for evidence- based decision support across diverse organizations, with special reference to the potential impact of data-science mediated decision support on community, grassroots and social advocacy groups. Students will design community impact strategies based on stakeholder engagement, develop tools such as dashboards and story boards using relevant data sets and present outputs to the community constituents for the course.
Prerequisites: EN 102, COM 101, DS 200

DS 302 Data Journalism (3)

DS 303 Modeling for Prediction (3)

This course provides an overview of the modeling and prediction process, including definition of goals for prediction, effective data preparation, algorithms, modeling methods and verification/validation. Students will learn iterative refinement of models based on a project in their special interest area.
Prerequisites: EN 102, COM 101, CS 201 or CS 202, MA 210, and MA 331

DS 330 GeoComputation (3)

Overview of Geographical Information Systems and geo-coded data and applications acrossnatural and social sciences, humanities, environmental studies, engineering, and management. Topics will include ArcGIS software package, spatial data acquisition, editingand QA/QC, metadata development, geo-database design, spatial query and display, spatial analysis and modeling, preliminary GIS application development, cartographic mapping anddynamic visualization, and GIS implementation basics. Students will use Google Earth, remotesensing and GPS, and common open source GIS tools. The prerequisites for this course are DS 230 and DS 231 or demonstrable experience GIS and Cartography.

DS 331 Digital Image Processing & Analysis (3)

The Digital Image Processing & Analysis course will provide students with foundational knowledge of how to process, describe, and analyze imagery. This class will primarily use satellite imagery, though working with aerial imagery will be covered. In the image processing section of this course, students will the fundamentals of scene selection, scene preparation, orthorectification, and atmospheric and radiometric correction. Once students can independently perform image processing, then the analysis portion of this course will cover image classification using essential indices (including Normalized Difference Vegetation, Wetness, Temperature, and Water Reflection indices at minimum), change detection analysis, and, time permitting, begin learning the fundamentals of object detection. This course will give students a strong foundation in the Remote Sensing & Imagery Analysis competency (Raabe and Stumpf, 2-14). The prerequisite for this course is DS 330.

DS 400 Bayesian Statistics (3)

This course will introduce the Bayesian approach to data analysis (including choice of prior distributions and calculation of posterior distributions) with an emphasis on practical applications. Topics to be discussed include: Bayes’ Theorem; prior distributions; inferences for discrete random variables and binomial proportion; inferences for continuous random variable and normal means; linear regression; analysis of variance; MCMC/Gibbs sampler; and model evaluation/comparison.
Prerequisites: EN 102, COM 101, MA 331

DS 401 Healthcare Informatics and Analytics (3)

This course examines foundations of health informatics including in terms of its context within the modern health care system and also an understanding of the competencies in relation to health informatics project management. Topics covered include the role of health informatics and analytics in relation to the Affordable Care Act, accountable care organizations, value- based care and population health. This course provides students with an overview of various clinical and administrative information systems and critical functions used in health care (electronic health records, computerized provider order entry, decision support, prescribing, telemedicine/telehealth, and revenue cycle).
Prerequisites: EN 102, COM 101, CS 201 or CS 202, MA 331

DS 402 Business Informatics, Marketing and Forecasting (3)

Business analytics uses data and models to explain the performance of a business and how it can be improved. This course discusses the benefits of employing analytics and a structured approach to problem-solving in management situations. Topics to be covered include data manipulation, predictive analytics, decisions under uncertainty, and decision analytics tools (linear and nonlinear optimization). Students will explore the capabilities and challenges of data-driven business decision making and explore linkages between analytics and business intelligence approaches.
Prerequisites: EN 102, COM 101, CS 201 or CS 202, MA 331

DS 403 Digital Humanities (3)

This course will explore emerging forms of humanities scholarly production and digital methodologies, such as digital exhibits, digital mapping, text analysis, information visualization, and network analysis.
Prerequisites: EN 102, COM 101, DS 100 or DS 101 and CS 201 or CS 202
OR 2 or more of the following: EID 200, EID 217, EN 201, HI 151, HI 152, HU 122, POL 111, POL 211, or RE 103

DS 404 Geo-Tagging and GIS (3)

Overview of Geographical Information Systems and geo-coded data and applications across natural and social sciences, humanities, environmental studies, engineering, and management. Topics will include ArcGIS software package, spatial data acquisition, editing and QA/QC, metadata development, geodatabase design, spatial query and display, spatial analysis and modeling, preliminary GIS application development, cartographic mapping and dynamic visualization, and GIS implementation basics. Students will use Google Earth, remote sensing and GPS, and common open source GIS tools.
Prerequisites: EN 102, COM 101, DS 100 or DS 101 and CS 201 or CS 202

DS 405 Advanced R (3)

Advanced skills and packages in R statistical analysis software.
Prerequisites: EN 102, COM 101, CS 201

DS 406 Advanced Python (3)

Advanced programming and applications of the Python programming language.
Prerequisites: EN 102, COM101, CS 202

DS 407 Data Science for Environmental Sciences (3)

Lecture and project-based course addressing applications of data science, data analytics and visualization to the environmental sciences. Decision support, data aggregation and predictive modeling will be applied to problems sets from conservation, natural resource management, monitoring and mitigation areas.
Prerequisites: EN 102, COM 101, DS 100 or DS 101 and CS 201 or CS 202, OR ENV 100 and ENV 201

DS 430 Spatial Statistics & Modeling (3)

The Spatial Statistics & Modeling class will build from the outcomes of the Bayesian Statistics course and provide students with the ability to accurately describe and model spatial data. This course will utilize a combination of lectures and experiential work. In the statistics section of this course, topics of basic and Bayesian Statistics will be reviewed before introducing them in a spatial setting. Global statistics, local functions and density analysis will be covered before moving on to correlation, linear regression, geographically weighted regression, and spatial regressions. As a part of the modeling section of this course, students will learn how to make programs to model these regressions based on real world data. The prerequisite for this course is DS 330.

DS 431 Remote Sensing & Machine Learning (3)

The Remote Sensing & Machine Learning course will combine the knowledge from the Digital Image Processing & Analysis and Spatial Modeling & Statistics course with that of the Machine Learning and AI experiential work. This course will teach students how to use machine learning for object detection within a selected platform. They will learn how to interpret results from this detection and how to analyze the accuracy of it. Students will apply these skills within different geographies from an area of interest to global object detections. Students will also learn how to train the object detection model to increase the accuracy of results and how to apply this training to different and distinct geographies. The prerequisite for this course is DS 430.

DS 433 Cloud & Server GIS (3)

This course will teach students how to conduct geocomputing in the cloud with server resources. Emphasis will be placed on using a cloud-based platform and the Esri web technologies (ArcGIS Server, Portal for ArcGIS, ArcGIS Online) commonly used by US government agencies and the military to disseminate geospatial information. Students will learn how to use REST APIs to access and analyze web services. The principles of server architecture as it relates to serving vector and raster data will also be a topic of concern. This course would teach students Geospatial Data Management skills (“GEOINT Essential Body of Knowledge”, 5). The prerequisite for this course is DS 431.

DS 434 Spatial Analysis (3)

This course looks at how things are organized on the Earth’s surface. Spatial analysis uses the data rich environment we now have available to understand how objects and entities relate to and interact with each other in space and time. Spatial statistics provide insights into explaining processes that create patterns in spatial data. In spatial analysis, spatial statistics such as point pattern analysis, spatial autocorrelation, and spatial interpolation will analyze the spatial patterns, spatial processes, and spatial association that characterize spatial data. Understanding spatial analysis will help you realize what makes spatial data special and why spatial analysis reveals a truth about spatial data. The prerequisite for this course is DS 430.

DS 480 Special Topics (1 to 3)

Selected topics in Data Science to be announced. May be repeated.
Prerequisites: EN 102, COM 101

DS 487 Internship (3)

Field work experience at an approved Data Science or data analytics organization or program. Students will be supervised by an on-site supervisor and course instructor. The students will be required to provide a final paper, poster, or presentation. This course provides competencies to meet the program outcomes to allow students to demonstrate an understanding of providing service to the community and preparing for careers in Data Science. Departmental approval is required prior to enrollment.
Prerequisites: EN 102, COM 101

DS 495 Data Science Directed Research (3)

This course is a research method and directed research course in data science. The course will include lectures, discussions, assignments used for the directed research project, and a semester long directed research project. The goal of the course has two parts: 1) students will be provided tools and techniques that will assist on assessing research designs and strategies to develop their data science directed research project and 2) students will execute and complete their data science directed research project. Students in this course will learn the different research methodologies; assess literature for a literature review; develop a research question or problem to analyze; learn data collection methods; learn sampling approaches; design and develop a proposal; apply knowledge, skills, and abilities from past data science courses; analyze and evaluate data; and produce a directed research product and communicate the project in front technical and non-technical audience.
Prerequisites: EN 102, COM 101, DS 301 and MA 331

DS 530 Capstone in Geospatial Intelligence (3)

This course constitutes a culminating experience in the Geospatial Intelligence Certificate program (GEOINT). The course includes a semester project that demonstrates a GEOINT student's skills and ability to apply advanced knowledge of geospatial principles. The project will involve a real-world problem using the GIS and remote sensing tools used in the curriculum. Students are encouraged to work collaboratively to develop and refine projects and to evaluate the ethical and professional aspects in this domain.