PSYC 496

Fall 2023 All Classes

All Classes

Adv Current Topics in Psych

SHOW FINAL EXAM SCHEDULE

Credit: 2 TO 4 hours.

Advanced treatment of current topics in the field of psychology.

2 to 4 undergraduate hours. 2 to 4 graduate hours. May be repeated to a maximum of 9 hours. Prerequisite: PSYC 100 and junior standing, or consent of instructor; particular sections may have additional 200-level and/or 300-level prerequisites.

My Favorites

My Schedule

Section Status updates every 10 minutes.

PSYC 496 class schedule data for fall 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
46958	Lecture-Discussion	AB1	8:30AM -9:50AM	TR	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Network Sci & its applications Section Info: Topic: Network Science & its applications Graduates should enroll in this section. Networks are ubiquitous and the emergence of complexity in biological and nonbiological systems is modelled using interactions among individual components of these systems. Network Science is an emerging discipline that deals with the scientific study of complex networks. In this course, students will be introduced to various types of networks in real world, will learn about the mathematical foundations of network measures, will be introduced to computational algorithms that are widely applied in network science, and will move on to learn statistical methods for analyzing network data. Students will acquire hands-on experience in manipulating, visualizing, and modeling the network data with various R packages. Students will also be introduced to applications of network science in various disciplines such as sociology, ecology, psychology/neuroscience, molecular biology and genetics, medicine, computer science, and information science. Prerequisites: Some familiarity with R language preferred. Restriction(s): Restricted to Graduate - Urbana-Champaign.
47772	Lecture-Discussion	AB2	8:30AM -9:50AM	TR	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 3 hours Section Title: Network Sci & its applications Section Info: Topic: Network Science & its applications Undergraduate students should enroll in this section. Networks are ubiquitous and the emergence of complexity in biological and nonbiological systems is modelled using interactions among individual components of these systems. Network Science is an emerging discipline that deals with the scientific study of complex networks. In this course, students will be introduced to various types of networks in real world, will learn about the mathematical foundations of network measures, will be introduced to computational algorithms that are widely applied in network science, and will move on to learn statistical methods for analyzing network data. Students will acquire hands-on experience in manipulating, visualizing, and modeling the network data with various R packages. Students will also be introduced to applications of network science in various disciplines such as sociology, ecology, psychology/neuroscience, molecular biology and genetics, medicine, computer science, and information science. Prerequisites: Some familiarity with R language preferred. Restriction(s): Restricted to Undergrad - Urbana-Champaign.
48376	Lecture-Discussion	ADG	2:00PM -3:20PM	TR	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Programming & Data Science w/R Section Info: Graduate Students should register for this section. In this course you will learn how to program in R and subsequently use R for effective data analysis and communication of results. The course will teach you the basics of R programming (such as data types and structures in R, writing functions in R, loops and iterations, etc.) and will expand to teach R packages to tidy, transform, visualize, and model your data. You will learn powerful visualization and transformation packages in R (ggplot2 and dplyr), and will also learn about interactive visualization packages in R. You will learn how to fit models to data with R packages and will move on to learn machine learning concepts/packages in R. This course will conclude by covering topics on tools for reproducible research. You will learn R packages such as R Markdown for integrating prose, code and results of data analysis. You will also learn version control with Git and GitHub, which will enable you to create and manage repositories of your code and share them for publication or collaborative purposes. This course will serve as a foundation course for any other advanced statistical analysis/modelling course in R that you may plan for later or in parallel. No prior programming experience is required for this course. Elective course for Intradisciplinary Psychology Concentration Restriction(s): Restricted to Graduate - Urbana-Champaign.
46959	Lecture-Discussion	ADH	2:00PM -3:20PM	TR	11 Psychology Building	Adhimoolam, B	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 3 hours Section Title: Programming & Data Science w R Section Info: Undergraduate students should register for this section. In this course you will learn how to program in R and subsequently use R for effective data analysis and communication of results. The course will teach you the basics of R programming (such as data types and structures in R, writing functions in R, loops and iterations, etc.) and will expand to teach R packages to clean up, transform, analyze and visualize the data set and to further communicate your results. You will be introduced to powerful visualization and transformation packages such as ggplot2 and dplyr in R. This course will also cover the concepts and tools for reproducible research with packages in R such as Markdown and knitr for integrating prose, code and results. You will be introduced to version control with Git and GitHub, which will enable you to create and manage repositories of your code (and data) and share them for publication or collaborative purposes. This course will serve as a foundation course for any other advanced statistical analysis/modeling course in R that you may plan for later or in parallel. There are no prerequisites for this course. No programming experience is required for this course. Elective course for Intradisciplinary Psychology Concentration Restriction(s): Restricted to Undergrad - Urbana-Champaign.