Course Descriptions

Data Science, Analytics and Visualization (DS)

DS 100 Introduction to Computation Systems and their Application (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 Structures, Data Analytics and the 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 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.

CS 200 SQL and Relational Databases (3)

This course is an introduction to Database used in data science that will cover topics on database systems and a related data science database language called SQL. This course will include lectures, discussions, assignments, hands-on experiences, and a project. The goal of the course will be to provide students with knowledge, techniques, and skills on database systems, programming in SQL, and revisit R and Python. Students in this course will learn the various databases and concepts in data science, database design, the SQL language, using SQL to develop a database, and querying in SQL.

CS 201 Programming in R (3)

This course is an introduction to R that will cover the R topics and language. This course will include lectures, discussions, assignments, hands-on experiences with real data, and a project that could be used for future classes and investigation. This course will prepare students for the next data science courses and practice by providing students with knowledge, techniques, skills, and a data science mindset. Students in this course will learn the data science process of collecting, storing, and curating data; ingestion and wrangling data; R language; R used for database systems; analyzing data using R; visualizations; and reporting the results of the analysis. Prerequisites: DS 100, MA103.

CS 202 Programming in Python (3)

This course is an introduction to Python that will cover the python topics and language. This course will include lectures, discussions, assignments, hands-on experiences with real data, and a project that could be used for future classes and investigation. This course will prepare students for the next data science courses and practice by providing students with knowledge, techniques, skills, and a data science mindset. Students in this course will learn the data science process of collecting, storing, and curating data; ingestion and wrangling data; Python language; Python used for database systems; analyzing data using Python; visualizations; and reporting the results of the analysis. Prerequisites: DS 100, MA103.

CS 203L Machine Learning and AI Lab (2)

This course is a machine learning and AI lab. This course will include lectures, discussions, assignments, hands-on experiences, and a project. The goal of the course, it will prepare and provide students with machine learning and AI knowledge, techniques, skills, and a data science mindset. Students in this course will learn Python and various machine learning algorithms, such as trees, models, clustering, and networks. Prerequisites: CS 201 or CS 202.

CS 204L Visualization and Analytics Lab (2)

This course is a visualization and analytics lab. This course will include lectures, discussions, assignments, hands-on experiences, and a project. The goal of the course, it will primarily focus on visualization and analytics of data science by providing and preparing students with the necessary knowledge, techniques, skills, and a data science mindset. Students in this course will use both R and Python, and will learn the planning, development, evaluation, and interpretation process of various graphs with different levels of development difficulty. Prerequisites: CS 201 or CS 202.

DS 300 Ethics Seminar (1)

One credit seminar course following on and further developing concepts and skills in DS 200. Students will perform a service project in data ethics.

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: 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. Prerequisites: CS 204L. CS 203L, CS 204L, MA 210, MA 331 are prerequisites for all courses from DS 303 and above. . Prerequisites: DS 200 and MA 331.

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: CS 201 or CS 202 and MA 331.

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: DS 303 and MA 210

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: CS 201 or CS 202 and MA 331.

DS 402 Business Analytics, 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: CS 201 or CS 202 and 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: DS 100 or DS 101 and CS 201 or CS 202.

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: 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: CS 201 or CS 202 and MA 331.

DS 406 Python II (3)

Advanced programming and applications of the Python programming language. Prerequisites: CS 201 or CS 202 and MA 331.

DS 407 Data Analytics and Visualization 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: CS 201 or CS 202 and MA 331.

DS 480 Special Topics (1 to 3)

Selected topics in Data Science to be announced. May be repeated. Prerequisites:  Vary according to topic.

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:  Approval of course instructor.

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: DS 301 and MA 331