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.

This course focuses on becoming a better research producer and a research consumer from a psychological science perspective. Students will learn to think critically about media claims and accurately summarize primary source articles about behavior. Students will learn to use statistical software to accurately describe data. Students will learn to communicate effectively about research through written and oral work and make ethical judgments informed by APA ethical standards. Students will design and execute their own individual research studies.

4 CreditsS, CW, QS 

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 

Students will understand how to access various data types and sources, from flat file formats to databases to big storage data architecture. Students will perform transformations, cleaning, and merging of datasets in preparation for data mining and analysis. 

3 CreditsNPRE-REQ: CS 110 and DS 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, or EB- 211. 

This course considers the various aspects of presenting digital information for public consumption visually. Data formats from binary, text, various file types, to relational databases and web sites are covered to understand the framework of information retrieval for use in visualization tools. Visualization and graphical analyses of data are considered in the context of the human visual system for appropriate information presentation. Various open-source and commercial digital tools are considered for development of visualization projects. 

3 CreditsN,CTDH,CTGESPrerequisite: IT 110, IT 111, IM 110, DS 110, or CS 110 or permission. 

A class in multivariate statistical techniques including non-parametric methods, multiple regression, logistic regression, multiple testing, principle analysis.

3 CreditsN, QSPrerequisites: An introductory statistics course ( MA220 or BI305 or PY214 or EB211) and linear algebra (MA 160) or Calculus 1 (MA 130).

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