Pennsylvania State University-Penn State Erie-Behrend College · Courses
DS
31 courses with the subject DS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DS 120Scripting for Data Sciences1
Introductory course in computer-based scripting languages for use in data analyses. DS 120 Scripting for Data Sciences (1) This introductory course aims to teach practical skills in data manipulation and preprocessing scripting, including the fundamentals of an interpreted programming language for use in the data sciences. The goal of the course is to provide an accessible (no pre-requisites) and brief (1 credit) introduction, preparing students for hands-on data analytics assignments in DS 200 Introduction to Data Sciences. This practical course teaches fast manipulation of datasets on the Unix command line, scripting in spreadsheets, and fundamental control structures and data manipulation in a modern interpreted programming language. It is expected that students gain an overview of the available tools and techniques that allows them to acquire basic proficiency in select techniques in the course of applications in most other courses in Data Sciences.
or STAT 200 Elementary Statistics Supporting Courses and Related Areas Select 6 credits from Computational Option List A in Appendix C Select 6 credits from Computational Option List B in Appendix C Students may apply up to 3 credits of ROTC as option list credi credits of ROTC as GHW credits. LIST OF COMPUTATIONAL DATA SCIENCES COURSES (https:// www.eecs.psu.edu/students/undergraduate/Data-Sciences.aspx) Undergraduate - The Pennsylvania State University 2026-2027 587 ext Statistical Modeling Data Sciences (DTSCS_BS, DTSAB_BS): 38 credits Only Available through the Eberly College of Science and Penn State
The course introduces students to the fundamentals of data models: organizing, managing, and using different types of data that arise in real- world applications. The course introduces students to several alternative data models and database solutions, emphasizing their strengths and limitations in the context of real-world applications. Topics covered include the relational databases, key-value stores, column-oriented databases, vector-space databases, graph databases, and distributed file systems together with their applications in solving real-world big data management problems. Upon completion of the course, the students will be able to choose an appropriate data model and database solution for a given application, and use the chosen database to organize, manage, and use data in the context of specific applications. Enforced Prerequisite at Enrollment: C or better in CMPSC 121 or CMPSC 131 Enforced Concurrent at Enrollment: CMPSC 122 or CMPSC 132
/Maximum of 18 Creative projects, including research and design, that are supervised on an individual basis and that fall outside the scope of formal courses.
Undergraduate - The Pennsylvania State University 2026-2027 2601 3 DS 330 Visual Analytics for Data Sciences 3 3 STAT 380 Data Science Through Statistical Reasoning and 3
This course teaches students how to formulate data science problems that arise in different applications that involve different types of data (tabular, sequence, network, matrix data); and introduces students to common strategies for formulating, and solving those problems. The course will cover (1) how to formulate data science problems (e.g., text analysis, biological sequence analysis, social network analysis, recommender systems) and how to evaluate alternative formulations, (2) common algorithmic methods and tools (as implemented in software libraries) for representing, processing, and sampling data, (3) how to apply the methods and tools to solve well-formulated data science problems. The course will also teach students how to understand and use results about the correctness, efficiency, and scalability of the techniques and tools, and they will learn to recognize when specific algorithmic tools can be used to improve the performance of their data science tasks. Through exercises, students will gain hands-on experience in problem formulation and solution development for data science problems that arise in applications. Enforced Prerequisite at Enrollment: Prerequisites: (CMPSC 122 or CMPSC 132 or IST 242) and (IST 230 or CMPSC 360 or MATH 311W)
Recommended Preparations: DS 310 Modern data-intensive applications (healthcare, security, public policy, science, commerce, crisis management, education, among others) increasingly call for integration of multiple types of data from disparate sources. This course introduces students to the principles and the practice of data integration, with particular emphasis on relational, knowledge-based, graph-based, and probabilistic methods. Carefully crafted assignments will help enhance the students' mastery of both the theoretical underpinnings as well as practical aspects of data integration. The students will work in teams to solve representative data integration problems drawn from real-world applications. Upon completion of the course, students should be able design, implement, and evaluate data integration solutions to support data intensive applications. Enforced Prerequisites at Enrollment: DS 220 and (STAT/MATH 318 or STAT/MATH 414 or STAT/MATH 418)
This course builds up the students' understanding of data sciences by discussing the fundamental principles in the context of real-world examples, and then shows specifically how the principles can provide understanding of many of the most common methods and techniques covered in previous data science courses. The course features three individual projects as well as a team project spanning the entire course. After taking this course, the students should be able to cover the entire pipeline of a data science project, from problem formulation to data science solutions. That is, start from a data driven problem, identify pertinent datasets to the problem and collect data, reason about the best techniques that should be used to solve the problem, implement algorithms and models, assess performance, and communicate actionable insights through both written reports and oral presentations. As one example, a fundamental principle of data science is that solutions for extracting useful knowledge from data must carefully consider the
/Maximum of 18 Creative projects, including research and design, that are supervised on an individual basis and that fall outside the scope of formal courses.
/Maximum of 9 This course exposes and trains students in the analysis of emerging trends in data sciences. DS 402 Emerging Trends in the Data Sciences (3) Data sciences is a rapidly evolving field affected by innovations in a variety of technical domains, including data generation, capture, storage, and processing. Staying abreast of new developments can be a daunting task but is critical for success. This course provides an in-depth analysis of a particular innovation, but starts with developing generally applicable skills for analyzing new technologies. In particular, the analytic framework considers the innovation's technical aspects and potential for widespread adoption, but also its social, organizational and policy implications. As a course focused on a new data sciences technology or analytic innovation, it is repeatable. As such, the course enables students to be exposed to the cutting edge of data sciences, supporting a forward looking view of the field for students across the university. Enforced Prerequisite at Enrollment: DS 220
Recommended Preparations: DS 310; CMPSC 448 This course introduces modern programming models and related software stacks for performing scalable data analytics and discovery tasks over massive and/or high dimensional datasets. The learning objectives of the course are that the students are able to choose appropriate programming models for a big data application, understand the tradeoff of such choice, and be able to leverage state-of-the art cyber infrastructures to develop scalable data analytics or discovery tasks. Building on data models covered in DS 220, this course will introduce programming models such as MapReduce, data flow supports for modern cluster computing environment, and programming models for large-scale clustering (either a large number of data samples or a large number of dimensions). Using these frameworks and languages, the students will learn to implement data aggregation a algorithms, iterative algorithms, and algorithms for generating statistical information from massive and/or high-dimensional data. The realization of these algorithms will enable the students to develop data analytic models for massive datasets. Enforced Prerequisites at Enrollment: (CMPSC 122 or CMPSC 132) and (DS 220 or CMPSC 221) Cross-listed with: CMPSC 410
Networks constitute a useful representational abstraction for data from many real-world applications, such as social networks, biomolecular networks, brain networks, among others. This course aims to cover the conceptual, algorithmic, and applied aspects of network analytics to prepare the students to analyze network data. Specific topics to be covered include different kinds, e.g., undirected, directed, weighted, unweighted, labeled, unlabeled, networks; network properties and network statistics; statistical models of static and dynamic networks; classes of networks, different classes of network structure, e.g., random, small-world, modular hierarchical; methods for community detection, information propagation, node and link classification, network embedding, and their applications in life sciences e.g., predicting protein function, social sciences, e.g., analyzing social ties, e-commerce e.g., recommender systems. Enforced Prerequisites at Enrollment: DS 220 and DS 310
This course explores social and ethical dimensions of data science. Datafication can be a powerful force for good, but it can also do harm- to individuals and society. Oriented primarily around case studies, the course investigates when, why, and how data is collected, analyzed, and used, and explores the ethical stakes of data-driven systems. In addition to diagnosing ethical problems-e.g., invasions of privacy, algorithmic bias, and lack of transparency and accountability-students are asked to think creatively and constructively about how the tools of data science can be used to realize shared ethical and social commitments. The course will be comprised of both "theory" and "lab" components. The former will contextualize ethical problems, introducing students to ethical theories and frameworks for addressing them. The latter will ask students
or DS 440W Requirements for the Option Select an option 38-47 Requirements for the Option Applied Data Sciences (DATSC_BS, DTSAB_BS): 47 credits Only Available through the College of Information Sciences and Technology and Penn State Abington Code Title Credits
The practices and foundations of access to textual and nontextual information using the principles of information retrieval and web search. Introductory course for for undergraduate students in the last year of their academic program and graduate students covering the practices, issues, and theoretical foundations of organizing and analyzing information and information content for the purpose of providing access to textual and nontextual information resources. Introduces students to the principles of information storage and retrieval systems and databases. DS 441 Information Retrieval and Organization (3) This is an introductory course for Information Sciences and Technology senior and graduate students covering the practices, issues, and theoretical foundations of organizing and analyzing information and information content for the purpose of providing access to textual and non-textual information resources. This course will introduce students to the principles of information storage and retrieval systems and databases. Students will learn how effective information search and retrieval is interrelated with the organization and description of information to be retrieved. Students will also learn to use a set of tools, such as search engines, and procedures for organizing information. They will become familiar with the techniques involved in conducting effective searches of information resources. Enforced Prerequisite at Enrollment: C or better in MATH 141 and DS 220 and (IST 230 or CMPSC 360 or MATH 311W)
This course provides an overview of the foundations, problems, approaches, implementation, and applications of, artificial intelligence. Topics covered include problem solving, goal-based and adversarial search, logical, probabilistic, and decision theoretic knowledge representation and inference, decision making, and learning. Through programming assignments that sample these topics, students acquire an understanding of what it means to build rational agents of different sorts Undergraduate - The Pennsylvania State University 2026-2027 3977 as well as applications of AI techniques in language processing, planning, vision. Enforced Prerequisite at Enrollment: CMPSC 221. Enforced Concurrent at Enrollment: CMPSC 465 Cross-listed with: CMPSC 442
/Maximum of 18 Creative projects, including research and design, that are supervised on an individual basis and that fall outside the scope of formal courses.
This course covers all aspects of multimedia analytics including the foundations, representations, implementations, and applications of extracting patterns from images, video, audio, and related context information for the purpose of prediction, automated discovery, and decision-making. Specific topics to be covered in the course include an overview of multimedia systems, multimedia data representation and compression, image and video analysis, audio analysis, multimedia information retrieval, and advanced topics in multimedia analytics. Emphasis will be placed on the integrative approach to multimedia analytics, in which the systems can benefit from the synergy of leveraging multiple sensing modalities, including audio, image, video, text and more. The objective of the course is to provide students with a wide range of models and applications that are relevant to their research and development in modern multimedia analytics systems. recommened prep: IST 510
The course will give students a comprehensive coverage of the theoretical underpinnings, and practice of causal inference from observational and experimental data. Topics to be covered include: pitfalls of standard machine learning algorithms when applied to observational data; causal inference in the absence of randomized control trials; causal effects and counterfactuals; eliciting causal effects from observations; the Causal Bayesian Network framework for causal inference - do-calculus, identifiability of causal effects from observations and experiments; the Potential Outcomes framework for causal inference - matching and propensity score-based methods and their advanced variants for counterfactual inference; the relationship between the Potential Outcomes and causal Bayes Networks; and learning causal models from observations and experiments. The course will give a principled treatment to confounders as well as practical approaches to cope with them. Additional topics to be covered include mediation analysis; advanced machine learning methods for causal effect estimation; causal transportability; selection bias; and meta- analysis. Finally, the course will include a laboratory component to provide students with hands-on experience with applications of causal inference to problems from several domains. Course projects will focus on applications of causal models and causal inference e.g., in science, public policy, and health. Recommended preparation for the course include basic proficiency in programming, elements of probability theory and statistics, discrete mathematics, and machine learning. Recommended Preparations: IST 510, Probability and statistics, differential calculus, linear algebra, programming proficiency (e.g., in Python), and machine learning
