Data Science

Introduction to traditional statistical concepts including descriptive statistics, binomial and normal probability models, confidence intervals, tests of hypotheses, linear correlation and regression, two-way contingency tables, and one-way analysis of variance.

4 CreditsN, QS, WK-SPPrerequisite: FYC-101 or EN-110 or EN-109

This course covers basic descriptive and inferential statistics, normal curve and z-score computations, and addresses hypothesis testing using Chi-Square, T-Test, ANOVA, and linear regression modelling.

3 Credits QS,S

This course deals centrally with quantitative and statistical methodology in the biological sciences. It includes experimental design and the conventions of generating, analyzing, interpreting and presenting biological data. Counts as a math course for graduate and professional school requirements.

4 CreditsN, QS, CTGESPrerequisites: BI106 or ESS100

This course is a survey of the various visual, statistical, and modeling approaches commonly used in the analysis of environmental data. The course covers: (1) visual literacy from exploratory data inquisition to poster creation; (2) elementary group comparison such as t-test and ANOVA and their non-parametric analogs;(3) basic systems modeling; and (4) regression modeling techniques based on the generalized linear model framework.

3 CreditsN, QS, CTGES, CTGISPrerequisites: Sophomore standing and permission of the instructor.

A first course in econometrics with forays into regression, optimization, and modeling.

2 CreditsN, QPrerequisites: Introductory economics course.

Introduces the methodological skills necessary for conducting research and for becoming a better consumer of psychological science. Students will learn to think critically about claims and accurately summarize primary source articles about behavior. Students will learn statistical concepts commonly used to evaluate data, how to effectively communicate research, and make ethical judgments informed by APA ethical standards.

4 Credits Prerequisite: PY-101

The second part of an integrated course sequence applying the scientific process to the fields of Social Work and Sociology, emphasizing key research concepts, commonly used quantitative and qualitative methods, and the ability to communicate effectively about research with written and verbal skills. The course teaches students not only to conduct research but also to consume and utilize research.

3 CreditsS 

Bioinformatics is the science of collecting and analyzing complex biological data. It is an interdisciplinary field that develops and applies methods and software tools for understanding biological data. 

4 CreditsN,CTGESPre-req: BI-101 or BI-105, BI-102 or BI-106, CH-142, CH-143, CH-144, CH-145

The study and analysis of algorithms, their complexity and supporting data structures. Topics include searching, sorting, mathematical algorithms, tree and graph algorithms, the classes of P and NP, NP-complete and intractable problems, and parallel algorithms. 

4 CreditsCW,NPrerequisites: CS240 and MA116. 

This course begins with an introduction to fundamental concepts in Scientific Computing and concludes with domain-specific projects in areas like Bioinformatics, Data Science, Physical Systems, and Numerical Analysis. The common content will include command-line interfaces (Linux), programming languages (Jupyter/Python), numerical and graphical libraries (NumPy and Matplotlib), version-control (Git/Github), and relational databases (SQL).

3 CreditsNPre-Req: CS-110

This course considers the use of machine learning (ML) and data mining (DM) algorithms for the data scientist to discover information embedded in datasets from the simple tables through complex and big data sets. Topics include ML and DM techniques such as classification, clustering, predictive and statistical modeling using tools such as R, Matlab, Weka and others. Simple visualization and data exploration will be covered in support of the DM. Software techniques implemented the emerging storage and hardware structures are introduced for handling big data. 

3 CreditsNPrerequisite: CS-110, DS-110, and an approved statistics course from this list: MA-205, MA-220, BI-305, PY-214, PY-260, PY-366, or EB- 211. 

This course considers the management and processing of large data sets, structured, semi-structured, and unstructured. The course focuses on modern, big data platforms such as Hadoop and NoSQL frameworks. Students will gain experience using a variety of programming tools and paradigms for manipulating big data sets on local servers and cloud platforms. 

3 CreditsNPrerequisites: DS 110 Intro to Data Science and CS 370 Database Management Systems 

Under the direction of their advisor, students will complete an original, independent research project in Data Science. A written report and oral presentation summarizing their research experience and results will be prepared. This course is a requirement for students who are candidates for distinction in Data Science. Instructor permission required.

1-5 Credits

This course is an introduction to a Geographical Information System (GIS), and the course objective is that students gain a basic, partial understanding of GIS concepts, technical issues, and applications using Arc View GIS. It encourages thinking in spatial context. A diverse array of hands-on computer applications and projects are used to understand how geographical data can be analyzed spatially. Students explore analysis techniques in a problem basis learning approach using small team projects.

4 CreditsCTGISNote: A special course fee is assessed. Prerequisite: ESS100.

The goal of the course is to advance student understanding of a broad range of numerical and graphical techniques used to analyze complex data sets encountered in the environmental sciences. Students will learn the context to properly apply these techniques to address research questions. The purview is ecological, but is applicable to all other quantitative endeavors. The course emphasizes conceptual understanding, relevant applications, and proper interpretation rather than gory, though interesting, statistical theory. Students will apply the R language and environment for statistical computing to tailor analyses to specific circumstances. 

4 CreditsQS(Lec/Lab; 4 cr hr; Spring years; pre-req ESS 110, ESS 230-Environmetrics, or consent)

This course reviews and applies project management processes and techniques such as project life cycle, project selection methods, work breakdown instructions, network diagrams, cost estimates, and more. 

3 CreditsS,CW,CS,SW-LEPrerequisites: IT210 and Jr or Sr standing or permission of the instructor. Corequisite: IT308. 

This lab will require a team of students to function as a project development team for an IT- related business. The students will be exposed to many aspects of systems analysis, design, development and implementation, as well as project management tools and techniques. Students will be required to learn in a just-in-time mode using on-demand educational resources. 

1 CreditsSPrerequisites: IT210 and Jr or Sr standing or by permission of the instructor. Corequisite: IT307. Note: This course will have appointed class times for projects other than the times listed on the schedule. 

This course begins with an introduction to fundamental concepts in Scientific Computing and concludes with domain-specific projects in areas like Bioinformatics, Data Science, Physical Systems, and Numerical Analysis. The common content will include command-line interfaces (Linux), programming languages (Jupyter/Python), numerical and graphical libraries (NumPy and Matplotlib), version-control (Git/Github), and relational databases (SQL).

3 CreditsNPre-Req: CS-110