Curriculum

The goal of this course is to provide students with the basic principles of analysis of variance, and with the basic principles of designing experiments. Topics will include reliability, validity, one-sample t-test, independent-samples t-test, simple and multiple regression, effect size indicators, analysis of variance (ANOVA), analysis of covariance (ANCOVA), case analysis, model assumptions, transformations, polynomial regression, simple mediation, and moderated mediation.

Credits: 4

Primary Domain: Statistics

Professor: Markus Brauer

Foundations in programming fundamentals, emphasizing tools and techniques as utilized in human behavioral data science.

Credits: 3

Primary Domain: Computational Skills

Professor: Gary Lupyan

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Credits: 3

Statistical power, paired-samples t-test, within-subjects ANOVA, mixed models, mediation in within-subjects designs, contrast analysis (= the analysis of categorical predictors with 3 or more levels), multilevel modeling, linear mixed-effects models, restricted maximum likelihood, signal detection theory, logistic regression, exploratory factor analysis, missing data.

Credits: 4

Primary Domain: Statistics

Discussion of current topics in human behavioral data science, including discussion of planned capstone projects, discussion of recent journal articles, training and practice in scientific communication to various stakeholders, and professional development activities.

Credits: 1

Primary Domain: Professional Development

Professor: Tim Rogers

Learning Outcomes:

1. Understand how central concepts in data science get applied to problems from industry, government, and nonprofit sectors
2. Develop skills for pitching your ideas to employers, funding agencies, other professionals, and the public
3. Understand different potential career pathways and how to prepare for each
4. Learn best practices for effectively communicating complex scientific ideas and outcomes

This course is designed to continue to prepare students to (1) understand how data-science concepts from other classes get applied in real-world contexts, (2) communicate these ideas to different stakeholders, (3) seek employment in relevant fields in industry, government, and nonprofit sectors, and (4) plan and discuss their capstone projects. Each week will focus on a different topic, with sessions involving a project research presentation, a presentation and
discussion with a guest speaker, a roundtable discussion, or a hands-on activity relevant to scientific communication or professional development. You will be asked to read papers or complete online tutorials/activities in preparation for each session, and will be responsible for presenting on a topic in at least one session.

Introduction to computational approaches in machine learning for the behavioral sciences.

Credits: 3

Primary Domain: Computational Approaches

Professor: John Curtin

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Credits: 3

This course is designed to provide students with knowledge and experience conducting large-scale behavioral data science projects, independently and in collaboration with others, using a variety of contemporary software tools and environments.

Credits: 3

Primary Domain: Computational Experimentation

Professor: Tim Rogers

Learning Outcomes:

1.  Use online crowd-sourcing platforms for collecting behavioral data, such as Amazon Mechanical Turk, and understand issues of design, sampling, and interpretation associated with such platforms.
2.  Use integrated tools for conducting, documenting, and publishing complex behavioral data analyses, including JuPyTeR notebooks and R Markdown.
3. Use the GitHub platform to conduct collaborative behavioral data science, including documentation, analysis development, versioning, forking and merging.
4.  Use the SQL database management system to manage large behavioral datasets
5. Use docker containers and related tools for ensuring fully transparent and replicable behavioral data science.
6.  Understand how to access and use high-throughput and high-performance infrastructure for computationally expensive jobs, and when use of these platforms is warranted.

This course provides directed independent study experience to prepare students to apply what they have learned to important real-world problems connecting data science and human behavior, laying the groundwork for their Capstone project in the Fall semester. Students will work in small teams to apply data science tools and concepts to a specific problem with real-world practical implications.

Credits: 5

Primary Domain: Professional Development

Professor: Tim Rogers

Learning Outcomes:

1. Conduct a literature review to understand the current state of the art in a specific problem domain
2.  Connect in-class learning experiences to real world datasets and problems.
3. Use online resources to acquire as-needed libraries, toolsets, and knowledge for solving data-science problems in the study of human behavior
4. Present a concise overview of an important problem and a proposed solution to different audiences
5. Develop code implementing a data-science workflow relevant to a specific problem
6. Write a scope-of-work proposal for partners in industry and government

Conduct applied capstone project, including identifying the project primary literature, relevant tools, and software and communicating these ideas and plans to relevant stakeholders. The goal of this course is for students to obtain hands-on work experience applying data-science tools and concepts from their courses to an applied and unsolved problem. To this end students will spend the bulk of their time each day working in small teams on specific real-world problems.

Credits: 3

Primary Domain: Professional Development & Applied Research

Professor: Tim Rogers

Learning Outcomes:

1. Have direct hands-on experience working in a data-science and human behavior setting
2. Understand how data-science tools and concepts from class get applied to large-scale real-world problems
3. Have experience communicating about science with workers and managers in industry, government, non-profit or lab-based settings
4. Complete a project from conception through design and execution
5. Pitch ideas for solutions to non-experts
6. Prepare for a diverse set of career paths.