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Courses - Spring 2020
BSOS
Behavioral and Social Sciences
BSOS233
Data Science for the Social Sciences
Credits: 3
Grad Meth: Reg, P-F, Aud
Restriction: Must be enrolled in a BSOS major; or permission of instructor.
An introduction to modern methods of data analysis for social scientists. This course emphasizes teaching students who have no previous coding experience how to analyze data and extract meaning in a social science context. Students will gain critical programming skills and learn inferential thinking through examples and projects with real-world relevance.
BSOS331
Python Programming for the Social Sciences
Credits: 1
Grad Meth: Reg, P-F, Aud
Prerequisite: Students must have completed a least one college-level statistics course.
Restriction: Must be enrolled in a BSOS major; or permission of instructor.
Cross-listed with: BSOS631.
Jointly offered with: BSOS331.
Credit only granted for: BSOS331 or BSOS631.
Python has become the most powerful programming language in advanced statistics and data analytics. It includes expansive packages for data handling and processing, including the latest developments in machine learning, and offers Integrated Development Environments (IDE) for code development, testing, debugging, and graphical representation. In addition, python is deployed on virtually all high performance computing clusters, taking advantage of multi-processing, large memory, and GPU enhanced computing environments. This course offers a thorough introduction to python and those packages that are fundamental to data processing and analysis, image processing, natural language processing, machine learning.
BSOS355
(Perm Req)
Social Sciences Internship Practicum
Credits: 3
Grad Meth: Reg, P-F
GenEd: DSSP
Restriction: Must have earned a minimum of 60 credits; and minimum cumulative GPA of 2.5; and must have completed at least 1 semester at UMD.
Credit only granted for: BSOS388I or BSOS355.
Formerly: BSOS388I.
BSOS 355 is an internship course open to all majors. It will enable students to articulate and apply the scholarship from the discipline related to their specific internship placement into a real-work environment.
BSOS388B
Behavioral and Social Sciences Special Topics; Innovation and Social Change: Do Good Now
Credits: 3
Grad Meth: Reg, P-F, Aud
GenEd: DSSP, SCIS
Cross-listed with HONR348D and PLCY388D. Credit will be granted for BSOS388B, HONR348D or PLCY388D.

A Fearless Ideas Course from the Academy for Innovation & Entrepreneurship (AIE): http://ter.ps/iamFEARLESS Click here for more information on the Fearless Ideas Courses.

Click here for more course information on the BSOS Solutions Lab.
BSOS388C
(Perm Req)
Behavioral and Social Sciences Special Topics; Innovate Maryland Consulting Practicum
Credits: 3
Grad Meth: Reg, P-F, Aud
BSOS388F
Behavioral and Social Sciences Special Topics; Innovation in the Public Sector
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisite: PSYC100, GVPT100 or ECON200. Cross-listed with GVPT388B. Credit will be granted for BSOS388F or GVPT 388B.

Restricted to BSOS majors.
BSOS388X
(Perm Req)
Behavioral and Social Sciences Special Topics; Innovation in the Public Sector II
Credits: 3
Grad Meth: Reg, P-F, Aud
Prerequisites: BSOS388F or GVPT388B

For permission to register, email bsos-ugdean@umd.edu. with your name, UID, and brief statement of why this course is of interest to you.
BSOS631
Python Programming for the Social Sciences
Credits: 1
Grad Meth: Reg, Aud, S-F
Prerequisite: Students must have completed a least one college-level statistics course.
Restriction: Must be enrolled in a BSOS major; or permission of instructor.
Cross-listed with: BSOS331.
Jointly offered with: BSOS331.
Credit only granted for: BSOS331 or BSOS631.
Python has become the most powerful programming language in advanced statistics and data analytics. It includes expansive packages for data handling and processing, including the latest developments in machine learning, and offers Integrated Development Environments (IDE) for code development, testing, debugging, and graphical representation. In addition, python is deployed on virtually all high performance computing clusters, taking advantage of multi-processing, large memory, and GPU enhanced computing environments. This course offers a thorough introduction to python and those packages that are fundamental to data processing and analysis, image processing, natural language processing, machine learning.