{"id":2517,"date":"2023-08-30T11:44:04","date_gmt":"2023-08-30T16:44:04","guid":{"rendered":"https:\/\/uwm.edu\/graduateschool\/?page_id=2517"},"modified":"2026-04-01T14:48:38","modified_gmt":"2026-04-01T19:48:38","slug":"msds-current-classes","status":"publish","type":"page","link":"https:\/\/uwm.edu\/graduateschool\/msds-current-classes\/","title":{"rendered":"MS Data Science Current Classes"},"content":{"rendered":"\n

Below is a list of current classes available for the MS Data Science and Applied Artificial Intelligence degree. For a complete listing of all classes related to the degree please see the Requirements tab <\/a>of the Data Science and Applied Artificial Intelligence degree in our academic catalog.<\/p>\n\n\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Fall 2026 MSDSAAI Courses<\/strong><\/th><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n<\/thead>\n
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<\/td><\/td><\/td><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
Developing Insights from Data for Applications (1 Course\/3 credits required)<\/u><\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Title<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 425G<\/td>Introduction to Data Mining<\/td>16177<\/td>Monday\/Wednesday 10-11:15 am<\/td><\/td><\/td>Zeyun Yu<\/td>3<\/td>Junior standing, COMPSCI 251(P), and one of the following: MATH 212(P), MATH 221(P), or MATH 232(P).<\/td><\/td><\/td><\/td>\n<\/tr>\n
INFOST 582G<\/td>Introduction to Data Science
\n<\/td>		15020<\/td>Online<\/td>Online<\/td><\/td>Margaret Kipp<\/td>3<\/td>Jr st; or cons instr.<\/span><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
Organizing and Maintaining Large Data Sets (1 course\/ 3 credits required)<\/u><\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Title<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 557G<\/td>Introduction to Database Systems<\/td>12974<\/td>Section 001 - Tuesday\/Thursday, 2:30 \u2013 3:45 p.m.<\/span><\/td><\/span><\/td><\/span><\/td>Ayesha Siddika Nipu<\/td>3<\/span><\/td>Junior standing and COMPSCI 251(P); or graduate student; or special student.<\/span><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/span><\/td><\/span><\/td><\/span><\/td><\/td><\/span><\/td><\/span><\/td><\/td><\/td><\/td>\n<\/tr>\n
HI 744 (*can be counted as either an elective or in Organizing and Maintaining Large Data Sets category but not both)<\/td>Text Retrieval and Its Applications in Biomedicine<\/td>15376<\/td>Section 001- Thursday, 2:30 -- 5:10 pm<\/td><\/td><\/td>Lu He<\/td>3<\/td>Graduate standing or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
15377<\/td>Section 202- ONLINE<\/td>Online<\/td><\/td>Lu He<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/u><\/em><\/td><\/u><\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/span><\/td><\/td><\/td><\/td>\n<\/tr>\n
AI and Machine Learning to Extract Insight from Data (1 course\/3 credits required)<\/u><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Name<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
BUS ADM 767<\/td>Ideas and Applications of Data Science Different Fields<\/td>12902<\/td>Section 001- Thursday, 5:30 \u2013 8:10 p.m. (20-50% online)<\/td><\/td>Evening<\/td>Purushottam Papatla<\/td>3<\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 715<\/td>Programming for Machine Learning<\/td>15660<\/td>Section 001- Monday\/Wednesday, 4:00-5:15 pm<\/td><\/td><\/td>Tian Zhao<\/td><\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
Probabilistic Methods to Analyze Uncertainty in Data (1 course\/3 credits required)<\/u><\/strong><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Name<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites:<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
BUSMGMT 709<\/td>Predictive Analytics for Managers<\/td>14954<\/td>Section 001\t- Wednesday, 5:30 \u2013 8:10 p.m.<\/td><\/td>Evening<\/td>Amir Alwan<\/td>3<\/td>Graduate standng<\/td><\/td><\/td><\/td>\n<\/tr>\n
11470<\/td>Section 203 - ONLINE<\/td>Online<\/td><\/td>Layth Alwan<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 720<\/td>Computational Models of Decision Making<\/td>12975<\/td>Section 001\t- Thursday, 4 \u2013 5:15 p.m. (20-50% online)<\/td><\/td>Evening<\/td>Amol Mali<\/td>3<\/td>Basic course in Probability or Statistics.<\/span><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
ECON 310G<\/td>Introduction to Econometrics and Data Science<\/td>11842<\/td>Section 201- Monday\/Wednesday, 2:30 -- 3:45 pm (ONLINE)<\/td>Online<\/td><\/td>Staff<\/td>3<\/td>Junior standing; ECON 210(P) or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
ECON 703<\/td>Econometrics ***must sign up for lecture and discussion section <\/td>11279<\/td>Section 401- Tuesday\/Thursday, 10:00 -- 11:15 a.m.<\/td><\/td><\/td>Narayan Kishor<\/td>3<\/td>Graduate standing <\/td><\/td><\/td><\/td>\n<\/tr>\n
11491<\/td>Discussion Section 601- Friday, 8:30 -- 9:20 am<\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
ED PSY 724<\/td>Statistical Methods for Professionals and Practitioners II<\/td>13471<\/td>Section 001\t- Tuesday, 5 \u2013 8:30 p.m.<\/td><\/td>Evening<\/td>Razia Azen<\/td>4<\/td>Graduate standing and Ed Psy 624(P)<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
MTHSTAT 361G<\/td>Introduction to Mathematical Statistics I<\/td>16607<\/td>Section 001\t- Monday\/Wednesday, 2:30-3:45 pm<\/td><\/td><\/td>Chao Zhu<\/td>3<\/td>Junior standing and MATH 212(P) or MATH 233(P).<\/span><\/td><\/td><\/td><\/td>\n<\/tr>\n
***Not recommended for graduate students in math, or students not planning to take MTHSTAT 362.<\/u><\/td><\/u><\/span><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
MTHSTAT 362G<\/td>Introduction to Mathematical Statistics II<\/td>12248<\/td>Section 001 - Monday\/Wednesday 2:30\u20143:45 p.m.<\/td><\/td><\/td>Daniel Gervini<\/td>3<\/td>Junior standing; MthStat 361(P)<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
MTHSTAT 763<\/td>Regression Analysis<\/td>12272<\/td>Section 001 - Tuesday\/Thursday, 11:30 \u201312:45 p.m.<\/td><\/td><\/td>David Spade<\/td>3<\/td>Graduate standing; MTHSTAT 362(P) or MTHSTAT 467(P); or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
MTHSTAT 871<\/td>Mathematical Statistics I<\/td>17923<\/td>Section 001\t- Tuesday\/Thursday, 2:30-3:45 p.m.<\/td><\/td><\/td>David Spade<\/td>3<\/td>Graduate standing and MATH 524
\n(522)(C), MATH 624(622)(C), or MATH 724(C).<\/td>		<\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
PH 702<\/td>Introduction to Biostatistics<\/td>16328<\/td>Section 202- Must sign up for Lab 901<\/td>Online<\/td><\/td>Zhiyang Zhou<\/td>3<\/td>Grad st; Math 116 with B or better, or equivalent, or cons instr<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td>16330<\/td> Lab 901<\/td>Online<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td>Introduction to Biostatistics<\/td>14033<\/td>Section 401 (Monday 5:30-7:20 pm- must sign up for one of the labs below)<\/td><\/td><\/td>Zhiyang Zhou<\/td>3<\/td>Grad st; Math 116 with B or better, or equivalent, or cons instr<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td>14916<\/td> Lab 801- Thursday, 7:30 -- 9:20 pm<\/td><\/td>Evening<\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td>16329<\/td> Lab 802- Thursday, 2:30 -- 4:20 pm<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
SOCIOL 461G<\/td>Social Data Analysis Using Regression ***must sign up for lecture and one lab section <\/td>11858<\/td>(LEC) - Section 401- Tuesday 5:30-8:10 pm<\/td><\/td><\/td>Aki Roberts<\/td>3<\/td>Junior standing and SOCIOL 261(P) or other approved statistics course; or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
11864<\/td>(LAB)\t- Section 901 - ONLINE<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
SOCIOL 982<\/td>Advanced Quantitative Analysis<\/td>18155<\/td>Section 002- Wednesday, 5:30 -- 8:10 p.m.<\/td><\/td>Evening<\/td>M. Britton<\/td>3<\/td>Graduate standing and a minimum score of 85 on department diagnostic exam.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
Advanced Programming for Data Collection\/Data Science (1 course\/3 credits required)<\/u><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Name<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites:<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPST 702<\/td>Software Development in Python <\/td>12513<\/td>Section 001 - Monday\/Wednesday 1:00-2:15 pm<\/td><\/td><\/td>Jayson Rock<\/td>3<\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPST 751<\/td>Data Structure and Algorithms<\/td>12510<\/td>Section 001 - Tuesday\/Thursday, 9:30-10:20 am, F 4:30-5:20<\/td><\/td><\/td>John Boyland<\/td>3<\/td>COMPST 750(P); COMPSCI 251(R).<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
INFOST 350G<\/td>Introduction to Application Development<\/td>13714<\/td>Section 001- Tuesday\/Thursday, 11:30-12:45 pm<\/td><\/em><\/td><\/em><\/td>Matthew Friedl<\/td>3<\/td>Students who have completed COMPST 702 may not receive cr in INFOST 350G. Prereq: INFOST 110(C) or graduate standing or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
13715<\/td>Section 002- Monday\/Wednesday, 2:30-3:45 pm<\/td><\/em><\/td><\/em><\/td>Michel Zamfiroiu<\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
13716<\/td>Section 201 - ONLINE<\/td><\/em><\/td><\/em><\/td>Matthew Friedl<\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
13717<\/td>Section 220- ONLINE<\/td><\/em><\/td><\/em><\/td>Michel Zamfiroiu<\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
MTHSTAT 566G\/766<\/td>Computational Statistics<\/td>12273<\/td>Section 001 - Monday\/Wednesday, 4:00\u20145:15 p.m.<\/td><\/td>Evening<\/td>Daniel Gervini<\/td>3<\/td>Graduate standing; MTHSTAT 362(P) or MTHSTAT 467(P), or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
URBPLAN 794<\/td>Internet Geographic Information Systems (GIS)<\/td>15748<\/td>Section 105 - Monday, 2:30 -- 5:10 p.m.<\/td><\/td><\/td>Paul Vepraskas<\/td>3<\/td>Grad st; UrbPlan 792(P) or Geog 625(P); or cons instr.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
Ethics (1 course\/3 credits required)<\/u><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Name<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites:<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 790<\/td>Advanced Topics in Computer Science: AI Ethics<\/td>15358<\/td>Section 002- Thursday, 1:00 -- 2:15 pm (20-50% online)<\/td><\/td><\/td>Amol Mali<\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
Electives (4 courses\/12 credits required)<\/u><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
Course No.<\/strong><\/td>Course Name<\/strong><\/td>Class Code<\/strong><\/td>Class Time<\/strong><\/td>Online<\/strong><\/td>Evening<\/strong><\/td>Faculty<\/strong><\/td>Credits<\/strong><\/td>Prerequisites:<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
ART 316G<\/td>Creative Interfaces: Crafting AI<\/td>18217<\/td>Section 801 (LAB)- Monday\/Wednesday, 11:30 a.m. -- 2:10 p.m.<\/td><\/td><\/td>Nathaniel Stern<\/td>3<\/td>Junior standing and ART 218(P); or junior standing and SOIS, CEAS or SARUP major; or graduate standing; or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
BUS ADM 741<\/td>Web Mining and Analytics<\/td>12981<\/td>Lecture 203- ONLINE<\/td>Online<\/td><\/td>Cheng Chen<\/td>3<\/td>Grad st.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/td><\/strong><\/td><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
BUS ADM 742<\/td>Big Data for Business<\/td>17565<\/td>Lecture 001- Tuesday, 5:30-8:10 pm<\/td><\/td>Evening<\/td>Dijo Alexander<\/td>3<\/td>Grad st.<\/td><\/td><\/td><\/td>\n<\/tr>\n
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BUS ADM 745<\/td>AI for Business<\/td>17566<\/td>Lecture 203- ONLINE<\/td>Online<\/td><\/td>Dijo Alexander<\/td>3<\/td>Grad st.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/strong><\/td><\/td><\/td><\/td>\n<\/tr>\n
BUS ADM 763<\/td>Marketing Analytics<\/td>15904<\/td>Section 001 - Monday 5:30\u20148:10 p.m.<\/td><\/td>Evening<\/td>Pallav Routh<\/td>3<\/td>Grad st; BusMgmt 709(C) or cons instr.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
BUS ADM 812<\/td>Machine Learning for Business<\/td>12835<\/td>Section 001 - Thursday, 5:30 -- 8:10 pm<\/td><\/td>Evening<\/td>Cheng Chen<\/td>3<\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
BUS ADM 816<\/td>Business Intelligence Technologies & Solutions<\/td>11412<\/td>Section 203 - ONLINE<\/td>Online<\/td><\/td>Atish Sinha<\/td>3<\/td>Grad st.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 469G<\/td>Introduction to Computer Security<\/td>15357<\/td>Section 001- Tuesday\/Thursday, 8:30-9:45 am<\/td><\/td><\/td>Zhen Zeng<\/td>3<\/td>Junior standing; a grade of C or better in both COMPSCI 317(P) and COMPSCI 251(P).<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 535G<\/td>Algorithm Design and Analysis<\/td>12555<\/td>Section 001 - Monday\/Wednesday, 1:00\u20142:15 p.m.<\/td><\/td><\/td>Zeyun Yu<\/td>3<\/td>Junior standing; MATH 211(P), MATH 213(P), MATH 221(P) or MATH 231(P); a grade of C or better in COMPSCI 351(P) and COMPSCI 317(P); or MATH 341(P) and MTHSTAT 361(P).<\/td><\/td><\/td><\/td>\n<\/tr>\n
16168<\/td>Section 002- Tuesday\/Thursday, 1:00 - 2:15 p.m.<\/td><\/td><\/td>Christine Cheng<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 723<\/td>Natural Language Processing<\/td>17566<\/td>Section 001- Monday\/Wednesday, 11:30-12:45 pm<\/td><\/td><\/td>Rohit Kate<\/td>3<\/td>Graduate standing and COMPSCI 351 or COMPST 751.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 790<\/td>Advanced Topics in Computer Science: AI System Development and Security Challenges<\/td>12790<\/td>Section 001- Tuesday\/Thursday, 11:30 -- 12:45 pm<\/td><\/td><\/td>Zhen Zeng<\/td>3<\/td>Graduate standing.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
COMPSCI 790<\/td>Advanced Topics in Computer Science: Foundations of Artificial Intelligence<\/td>16944<\/td>Section 003- Tuesday\/Thursday, 4:00 -- 5:15 pm<\/td><\/td>Evening<\/td>Nikolai Kouklin<\/td>3<\/td>Graduate standing.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
CRM JST 713<\/td>Measuring Crime & Analyzing Crime Data<\/td>13053<\/td>Section 201 -ONLINE<\/td>Online<\/td><\/td>Danielle Romaine Dagenhardt<\/td>3<\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
CRM JST 716<\/td>Advanced Analytic Techniques for Crime Analysts<\/td>17668<\/td>Section 201- ONLINE<\/td>Online<\/td><\/td>Rebecca Konkel<\/td>3<\/td>Grad st; Crm Jst 520(P), Crm Jst 713(P), Crm Jst 716(P)<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
ECON 735<\/td>Causal Inference and Panel Data Econometrics<\/td>17687<\/td>Lecture 001- Tuesday, 5:30-8:10 pm<\/td><\/td>Evening<\/td>Staff<\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
ED PSY 821<\/td>Psychometric Theory and Scale Development<\/td>17921<\/td>Thursday, 3:30 - 6:10 pm<\/td><\/td><\/td>Bo Zhang<\/td>3<\/td>Graduate standing and ED PSY 720(P) or equivalent; or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
GEOG 726<\/td>Geographic Information Science<\/td>12516<\/td>(LEC) - Section 202 - ONLINE<\/td>Online<\/td><\/td>Zengwang Xu<\/td>4<\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
17803<\/td>(LAB) - Section 901 - ONLINE<\/td>Online<\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
17804<\/td>(LAB) - Section 902 - ONLINE<\/td>Online<\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
17805<\/td>(LAB) - Section 903 - ONLINE<\/td>Online<\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
GEOG 826<\/td>Intermediate Geographic Information Science ***must sign up for lecture and one lab section <\/td>11782<\/td>(LEC) - Section 401 - Tuesday\/Thursday, 1:00\u20142:15 p.m.<\/td><\/td><\/td>Changshan Wu<\/td>4<\/td>Grad st; Geog 525(P) or Geog 726(P) or UrbPlan 591(P) or UrbPlan 791(P).<\/td><\/td><\/td><\/td>\n<\/tr>\n
11783<\/span><\/td>(LAB) - Section 801 - Friday 11:30-1:20 p.m.<\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/span><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
Specialized Skills in Data Science for Healthcare<\/strong><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
HI 537G<\/td>Health Information Technology and Management<\/td>13611<\/td>Section 001 - Monday\/Wednesday, 10:00 -- 11:15 am<\/td><\/td><\/td>Min Wu<\/td>3<\/td>Junior or greater in a Health Sciences program, or HCA minor, or HCI Cert, or consent of instructor; and HCA 224(P) or INFOST 110(P) or BUS ADM 230(P).<\/td><\/td><\/td><\/td>\n<\/tr>\n
13612<\/td>Section 202- ONLINE<\/td>Online<\/td><\/td>Min Wu<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
HI 700<\/td>Introduction to Health Care Informatics<\/td>13613<\/td>Section 001- Wednesday, 2:30 -- 5:10 pm<\/td><\/td><\/td>Jake Luo<\/td>3<\/td>Graduate standing or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
13614<\/td>Section 202- ONLINE<\/td>Online<\/td><\/td>Jake Luo<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
HI 742<\/td>Computational Intelligence in Health Informatics<\/td>13619<\/td>Section 201- ONLINE<\/td>Online<\/td><\/td>Spencer Schultz<\/td>3<\/td>Graduate standing or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
HI 744 (*can be counted as either an elective or in Organizing and Maintaining Large Data Sets category but not both)<\/td>Text Retrieval and Its Applications in Biomedicine<\/td>15376<\/td>Section 001- Thursday, 2:30 -- 5:10 pm<\/td><\/td><\/td>Lu He<\/td>3<\/td>Graduate standing or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
15377<\/td>Section 202- ONLINE<\/td>Online<\/td><\/td>Lu He<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
INFOST 671G<\/td>Applied Web 3.0: Artificial Intelligence and Blockchain<\/td>16061<\/td>Section 001- Tuesday\/Thursday, 10:00 -- 11:15 am<\/td><\/td><\/td>Matthew Friedel<\/td>3<\/td>Junior standing and one of the following: INFOST 350(P) or COMPSCI 351(P); or graduate standing and COMPSCI 702(P) or COMPSCI 751(P); or consent of instructor.<\/td><\/td><\/td><\/td>\n<\/tr>\n
13750<\/td>Section 201- ONLINE<\/td>Online<\/td><\/td>Matthew Friedel<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
INFOST 695G<\/td>Ethical Hacking I<\/td>13725<\/td>Section 001- Monday\/Wednesday, 1:00-2:15 pm<\/td><\/td><\/td>Khaled Sabha<\/td>3<\/td>INFOST 325(P) or graduate standing.<\/td><\/td><\/td><\/td>\n<\/tr>\n
15331<\/td>Section 201- ONLINE<\/td>Online<\/td><\/td>Khaled Sabha<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
PH 717<\/td>Applied Longitudinal Data Analysis<\/td>14043<\/td>Section 001- Tuesday\/Thursday, 11:30 -- 12:45 pm<\/td><\/td><\/td>Laleh Jamshidi<\/td>3<\/td> PH 711 and PH 712; cons instr.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
POL SCI 390G<\/td>Political Data Analysis<\/td>12461<\/td>Section 201- ONLINE<\/td>Online<\/td><\/td>Thomas Holbrook<\/td>3<\/td>Jr. st; satisfaction of GER Quantitative Literacy Part A req.<\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/td><\/td><\/td><\/td>\n<\/tr>\n
SOCIOL 750<\/td>Research Methods in Sociology<\/td>11989<\/td>Section 001 - Thursday, 2:30-5:10 p.m.<\/td><\/td><\/td>Noelle Chesley<\/td>3<\/td>Graduate standing <\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n
URBPLAN 791<\/td>Introduction to Urban Geographic Information Systems for Planning ***must sign up for lecture and one lab section <\/td>14421<\/td>(LEC) - Section 201 - Monday, 5:30\u20146:20 p.m. ONLINE<\/td>Online<\/td><\/td>Michael Benedict<\/td>3<\/td>Graduate standing<\/td><\/td><\/td><\/td>\n<\/tr>\n
14422<\/td>(LAB) - Section 203 - Monday 6:30\u20148:00 p.m. ONLINE<\/td>Online<\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/u><\/strong><\/em><\/span><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/strong><\/em><\/td><\/strong><\/em><\/td><\/strong><\/em><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td><\/td>\n<\/tr>\n
<\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/em><\/td><\/td><\/td><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n

Overview of the MS Data Science degree<\/a><\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Below is a list of current classes available for the MS Data Science and Applied Artificial Intelligence degree. For a complete listing of all classes related to the degree please see the Requirements tab of the Data Science and Applied Artificial Intelligence …<\/p>\n","protected":false},"author":9839,"featured_media":0,"parent":0,"menu_order":24,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":"","uwm_wg_additional_authors":[]},"class_list":["post-2517","page","type-page","status-publish","hentry"],"yoast_head":"\nGraduate School<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/uwm.edu\/graduateschool\/msds-current-classes\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"MS Data Science Current Classes\" \/>\n<meta property=\"og:description\" content=\"Below is a list of current classes available for the MS Data Science and Applied Artificial Intelligence degree. 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