CMSE 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits:
Credits: 4 Lecture/Recitation/Discussion Hours:2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects on realistic, large-scale data.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS19 - FS22
CMSE 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits:
Credits: 4 Lecture/Recitation/Discussion Hours:2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects working with real-world data in collaboration with client/company sponsors. Practice in software development, data collection, curation, modeling, scientific visualization and presentation of results. Students may be required to sign a non-disclosure agreement (“NDA”) or an assignment of intellectual property rights (“IP Assignment”) to work with some project sponsors.
Interdepartmental With:
Computer Science and Engineering, Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
SS23 - Open
CSE 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits:
Credits: 4 Lecture/Recitation/Discussion Hours:2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects working with real-world data in collaboration with client/company sponsors. Practice in software development, data collection, curation, modeling, scientific visualization and presentation of results. Students may be required to sign a non-disclosure agreement (“NDA”) or an assignment of intellectual property rights (“IP Assignment”) to work with some project sponsors.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
SS23 - Open
CSE 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits:
Credits: 4 Lecture/Recitation/Discussion Hours:2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects on realistic, large-scale data.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Statistics and Probability
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS19 - FS22
STT 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits:
Credits: 4 Lecture/Recitation/Discussion Hours:2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects on realistic, large-scale data.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Computer Science and Engineering
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
FS19 - FS22
STT 495 Experiential Learning in Data Science (W)
Semester:
Fall of every year, Spring of every year
Credits:
Total Credits:
Credits: 4 Lecture/Recitation/Discussion Hours:2 Lab Hours: 4
Prerequisite:
(CSE 232 and CMSE 382) and completion of Tier I writing requirement
Restrictions:
Open to seniors.
Description:
Team-based data science projects working with real-world data in collaboration with client/company sponsors. Practice in software development, data collection, curation, modeling, scientific visualization and presentation of results. Students may be required to sign a non-disclosure agreement (“NDA”) or an assignment of intellectual property rights (“IP Assignment”) to work with some project sponsors.
Interdepartmental With:
Computational Mathematics, Science, & Engineering, Computer Science and Engineering
Administered By:
Computational Mathematics, Science, & Engineering
Effective Dates:
SS23 - Open