# GENBUS 657: Machine Learning and Artificial Intelligence Models for Business Analytics | UW–Madison [View on UW Courses](https://uwcourses.com/courses/GENBUS_657) ## Dataset ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ### repository twangodev/uwcourses ### schema version 6 ### importer version 7 ### projection id 52a78527ff09011d88cb04c7d43867d9fbfec2930bf1dd2ec359ebd2844e0b07 ### observed at 2026-09-07T15:55:43.033547+00:00 ### built at 2026-09-10T19:50:42.743001+00:00 ### courses 8951 ### current instructors 5754 ### limited false ### terms * 1272 * 1264 * 1262 * 1254 * 1252 * 1244 * 1242 * 1234 * 1232 * 1224 * 1222 * 1212 * 1204 * 1202 * 1194 * 1192 * 1184 * 1182 * 1174 * 1172 * 1164 * 1162 * 1154 * 1152 * 1144 * 1142 * 1134 * 1132 * 1124 * 1122 * 1114 * 1112 * 1104 * 1102 * 1094 * 1092 * 1084 * 1082 * 1074 * 1072 ### term 1272 ### departments | subject | count | | --------- | ----- | | AAE | 90 | | ABT | 20 | | ACCTIS | 36 | | ACTSCI | 14 | | AFAERO | 10 | | AFRICAN | 87 | | AFROAMER | 71 | | AGROECOL | 21 | | AMERIND | 53 | | ANAT\&PHY | 7 | | ANATOMY | 2 | | ANESTHES | 8 | | ANSCI | 57 | | ANTHRO | 95 | | ART | 125 | | ARTED | 10 | | ARTHIST | 121 | | ASIALANG | 129 | | ASIAN | 109 | | ASIANAM | 28 | | ASTRON | 38 | | ATMOCN | 69 | | BIOCHEM | 51 | | BIOCORE | 10 | | BIOLOGY | 14 | | BIOMDSCI | 18 | | BME | 66 | | BMI | 41 | | BMOLCHEM | 10 | | BOTANY | 69 | | BSE | 44 | | C\&ESOC | 73 | | CBE | 48 | | CHEM | 101 | | CHICLA | 52 | | CIVENGR | 133 | | CLASSICS | 47 | | CNP | 10 | | CNSRSCI | 50 | | COMARTS | 137 | | COMPBIO | 15 | | COMPLIT | 11 | | COMPSCI | 138 | | COUNPSY | 78 | | CRB | 19 | | CS\&D | 71 | | CSCS | 40 | | CURRIC | 193 | | DANCE | 93 | | DERM | 8 | | DS | 88 | | DYSCI | 35 | | ECE | 155 | | ECON | 145 | | EDPOL | 114 | | EDPSYCH | 97 | | ELPA | 70 | | EMA | 53 | | EMERMED | 17 | | ENGL | 206 | | ENTOM | 40 | | ENVIRST | 148 | | EP | 15 | | EPD | 67 | | ESL | 16 | | F\&WECOL | 60 | | FAMMED | 22 | | FINANCE | 50 | | FOLKLORE | 40 | | FOODSCI | 43 | | FRENCH | 63 | | GEN\&WS | 145 | | GENBUS | 72 | | GENECSLR | 17 | | GENETICS | 60 | | GEOG | 113 | | GEOSCI | 83 | | GERMAN | 82 | | GLE | 48 | | GNS | 38 | | GREEK | 27 | | HDFS | 41 | | HEBR-BIB | 13 | | HEBR-MOD | 10 | | HISTORY | 233 | | HISTSCI | 56 | | HONCOL | 13 | | ILS | 43 | | INFOSYS | 9 | | INTEGART | 7 | | INTEGSCI | 22 | | INTER-AG | 18 | | INTER-HE | 11 | | INTER-LS | 22 | | INTEREGR | 16 | | INTLBUS | 21 | | INTLST | 50 | | ISYE | 83 | | ITALIAN | 53 | | JEWISH | 56 | | JOURN | 88 | | KINES | 128 | | LACIS | 26 | | LANDARC | 61 | | LATIN | 24 | | LAW | 120 | | LEGALST | 48 | | LINGUIS | 35 | | LIS | 89 | | LITTRANS | 78 | | LSC | 52 | | M\&ENVTOX | 7 | | MARKETNG | 62 | | MATH | 153 | | MDGENET | 8 | | ME | 130 | | MEDHIST | 43 | | MEDICINE | 60 | | MEDIEVAL | 32 | | MEDPHYS | 39 | | MEDSC-M | 29 | | MEDSC-V | 41 | | MHR | 68 | | MICROBIO | 45 | | MILSCI | 16 | | MM\&I | 22 | | MOLBIOL | 6 | | MS\&E | 59 | | MUSIC | 165 | | MUSPERF | 126 | | NAVSCI | 20 | | NE | 44 | | NEURODPT | 11 | | NEUROL | 7 | | NEURSURG | 4 | | NTP | 11 | | NURSING | 111 | | NUTRSCI | 65 | | OBS\&GYN | 18 | | OCCTHER | 39 | | ONCOLOGY | 14 | | OPHTHALM | 5 | | OTM | 43 | | PATH | 34 | | PATH-BIO | 29 | | PEDIAT | 26 | | PHARMACY | 31 | | PHILOS | 77 | | PHMCOL-M | 11 | | PHMPRAC | 45 | | PHMSCI | 60 | | PHYASST | 37 | | PHYSICS | 88 | | PHYSIOL | 3 | | PHYTHER | 37 | | PLANTSCI | 52 | | PLPATH | 35 | | POLISCI | 192 | | POPHLTH | 58 | | PORTUG | 33 | | PSYCH | 101 | | PSYCHIAT | 22 | | PUBAFFR | 54 | | PUBLHLTH | 23 | | RADIOL | 11 | | REALEST | 41 | | RELIGST | 90 | | RHABMED | 9 | | RMI | 24 | | RP\&SE | 102 | | S\&APHM | 17 | | SCANDST | 73 | | SLAVIC | 89 | | SOC | 149 | | SOCWORK | 87 | | SOILSCI | 46 | | SPANISH | 83 | | SRMED | 21 | | STAT | 94 | | STDYABRD | 52 | | STS | 8 | | SURGERY | 25 | | SURGSCI | 33 | | THEATRE | 91 | | URBRPL | 60 | | UROLOGY | 5 | | ZOOLOGY | 89 | ## course ### run id 20260907T155543-ce3781c4 ### semester 1272 ### observed at 2026-09-07 15:55:43.033547+00:00 ### record version id 15080f3259226ee69160969fe9ace53d1a02ff09ae0b68d41fa4beb1d756d71e ### course id GENBUS 657 ### course uid course\_d867856b67bb1895c4ab83ea ### catalog version id b5be40df840deba24175ecce7c4f351b398bf1b78f2157317639b52ce90db792 ### course number 657 ### subjects * GENBUS ### title MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE MODELS FOR BUSINESS ANALYTICS ### description An introduction to machine learning models for business applications. Builds on the predictive modeling basics by developing general algorithmic prediction models for supervised machine learning. Covers additive models, CARTs, bagging/boosting, deep learning approaches, and AI models. Discussion of unsupervised learning techniques, including clustering and anomaly detection. ### requirements text ACT SCI 640or (GEN BUS 656or concurrent enrollment) ### credits min 2 ### credits max 2 ### credit offering ids * 1272:231:026426 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 91063df3832c2054a8fd791544ef29cf9952a8746522f2ddd2e140dcd7c21030 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Introduces machine learning and AI models for business analytics, covering supervised and unsupervised techniques. ### llm topics * Additive models, CARTs, bagging/boosting, deep learning, and AI models * Unsupervised learning, clustering, and anomaly detection ### llm skills * Developing algorithmic prediction models for supervised learning * Applying additive models, CARTs, bagging/boosting, and deep learning * Applying unsupervised learning techniques like clustering and anomaly detection ### llm assumed background * Foundational predictive modeling knowledge * Linear regression and classification models * Statistical learning theory ### llm search phrases * machine learning business applications * supervised machine learning models * additive models CARTs bagging boosting * deep learning AI models business * unsupervised learning clustering anomaly detection * predictive modeling advanced techniques ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 914eca74033723845e7615848aba8f2367249f3ddae74d1ea030279e913c662b #### course id GENBUS 657 #### current instructors ```json [ { "instructor_uid": "instructor_6c4f90dbb233c69ba31bd916", "message": "No course-specific reviews available", "name": "Zhongtian Chen", "review_status": "no_course_reviews", "rmp_instructor_id": null, "summary": [ { "citations": [ { "course_id": "GENBUS 657", "run_id": "20260907T155543-ce3781c4", "section_number": 10, "source_course_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "source_record": { "entity_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" }, { "course_id": "GENBUS 657", "run_id": "20260907T155543-ce3781c4", "section_number": 11, "source_course_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "source_record": { "entity_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" } ], "text": "Recent recorded grades — Fall 2025: 3.71 GPA, 87.3% A/AB (n=63 letter grades). Includes jointly taught sections." } ] } ] ``` #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered true #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Fall 2025: 3.71 GPA, 87.3% A/AB (n=63 letter grades); Spring 2026: 3.96 GPA, 97.5% A/AB (n=80 letter grades). ```json { "citations": [ { "course_id": "GENBUS 657", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1262", "type": "grade" }, { "course_id": "GENBUS 657", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history ZHONGTIAN CHEN is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule. ```json { "citations": [ { "course_id": "GENBUS 657", "run_id": "20260907T155543-ce3781c4", "section_number": 10, "source_course_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "source_record": { "entity_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" }, { "course_id": "GENBUS 657", "run_id": "20260907T155543-ce3781c4", "section_number": 11, "source_course_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "source_record": { "entity_id": "f7bf9649-5b74-367e-aeb7-dbb9f8a0874f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" } ] } ``` #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [ "n1", "n2" ], "condition": null, "course": null, "evidence": "ACT SCI 640or (GEN BUS 656or concurrent enrollment)", "id": "n0", "kind": "any" }, { "children": [], "condition": null, "course": { "course_number": 640, "minimum_grade": null, "subjects": [ "ACTSCI" ], "timing": "prior" }, "evidence": "ACT SCI 640", "id": "n1", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 656, "minimum_grade": null, "subjects": [ "GENBUS" ], "timing": "prior_or_concurrent" }, "evidence": "GEN BUS 656", "id": "n2", "kind": "course" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors | instructor\_uid | source | source\_instructor\_id | identity\_basis | identity\_status | name | email | first\_observed\_at | last\_observed\_at | instructor\_url | | ------------------------------------ | ---------- | ---------------------- | --------------- | ------------------ | -------------- | ------------------------ | -------------------------------- | -------------------------------- | ---------------------------- | | instructor\_6c4f90dbb233c69ba31bd916 | enrollment | zchen2556 | netid | source\_identified | Zhongtian Chen | ZHONGTIAN.CHEN\@WISC.EDU | 2026-09-07 15:55:43.033547+00:00 | 2026-09-07 15:55:43.033547+00:00 | /instructors/ZHONGTIAN\_CHEN | ### offerings ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:231:026426", "course_id": "GENBUS 657", "course_uid": "course_d867856b67bb1895c4ab83ea", "term_id": "1272", "source_course_id": "026426", "source_subject_id": "231", "title": "Machine Learning and Artificial Intelligence Models for Business Analytics", "credits_min": 2, "credits_max": 2, "typically_offered": "Not Applicable" } ] ``` ### sections ```json [ { "run_id": "20260907T155543-ce3781c4", "observed_at": "2026-09-07 15:55:43.033547+00:00", "section_uid": "uw-section:1272:32732", "term_id": "1272", "source_section_id": "32732", "identity_basis": "class_number", "section_number": "010", "section_type": "LEC", "instruction_mode": "Classroom Instruction", "capacity": 35, "enrolled": 28, "waitlisted": 0, "start_date": "2026-10-26 05:00:00+00:00", "end_date": "2026-12-09 06:00:00+00:00" }, { "run_id": "20260907T155543-ce3781c4", "observed_at": "2026-09-07 15:55:43.033547+00:00", "section_uid": "uw-section:1272:32735", "term_id": "1272", "source_section_id": "32735", "identity_basis": "class_number", "section_number": "011", "section_type": "LEC", "instruction_mode": "Classroom Instruction", "capacity": 35, "enrolled": 20, "waitlisted": 0, "start_date": "2026-10-26 05:00:00+00:00", "end_date": "2026-12-09 06:00:00+00:00" } ] ``` ### grade instructors ```json [ { "instructor_uid": "instructor_3015ada0cf27981ba2786718", "source": "madgrades", "source_instructor_id": "6657413", "identity_basis": "source_id", "identity_status": "source_identified", "name": "YIJING XU", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.667, "graded": 255, "counts": [ 111, 121, 21, 1, 1, 0, 0 ], "sections": 7 }, "instructor_url": "/instructors/YIJING_XU--instructor_3015ada0cf27981ba2786718" }, { "instructor_uid": "instructor_49466c521ec60d9cb2a0ebb6", "source": "madgrades", "source_instructor_id": "6802658", "identity_basis": "source_id", "identity_status": "source_identified", "name": "ZHONGTIAN CHEN", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.706, "graded": 63, "counts": [ 34, 21, 8, 0, 0, 0, 0 ], "sections": 3 }, "instructor_url": "/instructors/ZHONGTIAN_CHEN--instructor_49466c521ec60d9cb2a0ebb6" }, { "instructor_uid": "instructor_b7aa031ee746dea34fec7430", "source": "madgrades", "source_instructor_id": "6447992", "identity_basis": "source_id", "identity_status": "source_identified", "name": "CARRIE DENG", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.969, "graded": 162, "counts": [ 156, 3, 2, 1, 0, 0, 0 ], "sections": 2 }, "instructor_url": "/instructors/CARRIE_DENG--instructor_b7aa031ee746dea34fec7430" }, { "instructor_uid": "instructor_6c4f90dbb233c69ba31bd916", "source": "enrollment", "source_instructor_id": "zchen2556", "identity_basis": "netid", "identity_status": "source_identified", "name": "Zhongtian Chen", "email": "ZHONGTIAN.CHEN@WISC.EDU", "first_observed_at": "2026-09-07 15:55:43.033547+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "instructor_url": "/instructors/ZHONGTIAN_CHEN" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "GENBUS 657", "course_uid": "course_d867856b67bb1895c4ab83ea", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "YIJING XU", "Zhongtian Chen" ], "a": 34, "ab": 21, "b": 8, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 63, "source_aliases": [ "GENBUS 657" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "GENBUS 657", "course_uid": "course_d867856b67bb1895c4ab83ea", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "CARRIE DENG" ], "a": 76, "ab": 2, "b": 2, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 80, "source_aliases": [ "GENBUS 657" ] } ] ``` ### statistics #### gpa 3.85 #### graded 143 #### counts * 110 * 23 * 10 * 0 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_d867856b67bb1895c4ab83ea-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_d867856b67bb1895c4ab83ea-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_d867856b67bb1895c4ab83ea-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_d867856b67bb1895c4ab83ea-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_d867856b67bb1895c4ab83ea-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_d867856b67bb1895c4ab83ea-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_d867856b67bb1895c4ab83ea-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_d867856b67bb1895c4ab83ea-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.8496503496503496 #### count 143 #### university ##### size 2123 ##### gpa Percentile 68 ##### count Percentile 75 ##### median Count 71 ##### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 14 | false | | 2.8–3.2 | 170 | false | | 3.2–3.6 | 553 | false | | 3.6–4.0 | 1385 | true | #### departments ```json [ { "subject": "GENBUS", "comparison": { "size": 42, "gpaPercentile": 73, "countPercentile": 66, "medianCount": 89, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 1, "current": false }, { "range": "3.2–3.6", "count": 10, "current": false }, { "range": "3.6–4.0", "count": 31, "current": true } ] } } ] ``` ### terms #### 1262 ##### term 1262 ##### gpa 3.7063492063492065 ##### count 63 ##### university ###### size 1320 ###### gpa Percentile 51 ###### count Percentile 44 ###### median Count 70 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 8 | false | | 2.8–3.2 | 133 | false | | 3.2–3.6 | 381 | false | | 3.6–4.0 | 797 | true | ##### departments ```json [ { "subject": "GENBUS", "comparison": { "size": 27, "gpaPercentile": 42, "countPercentile": 27, "medianCount": 98, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 1, "current": false }, { "range": "3.2–3.6", "count": 9, "current": false }, { "range": "3.6–4.0", "count": 17, "current": true } ] } } ] ``` #### 1264 ##### term 1264 ##### gpa 3.9625 ##### count 80 ##### university ###### size 1283 ###### gpa Percentile 89 ###### count Percentile 58 ###### median Count 66 ###### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 1 | false | | 2.4–2.8 | 20 | false | | 2.8–3.2 | 130 | false | | 3.2–3.6 | 343 | false | | 3.6–4.0 | 789 | true | ##### departments ```json [ { "subject": "GENBUS", "comparison": { "size": 30, "gpaPercentile": 97, "countPercentile": 55, "medianCount": 79, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 1, "current": false }, { "range": "3.2–3.6", "count": 7, "current": false }, { "range": "3.6–4.0", "count": 22, "current": true } ] } } ] ``` ### benchmarks #### all ##### school ###### size 2123 ###### gpa 3.6592277338479904 ###### top Share 82.12897781052423 ###### count 71 ##### GENBUS ###### size 42 ###### gpa 3.705221752068083 ###### top Share 85.20339895982832 ###### count 89 #### terms ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ###### GENBUS ###### size 27 ###### gpa 3.655019994289601 ###### top Share 82.47740840714685 ###### count 98 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### GENBUS ###### size 30 ###### gpa 3.6817668874579623 ###### top Share 83.39332566820664 ###### count 79 ## instructor Trends ```json [ { "uid": "instructor_b7aa031ee746dea34fec7430", "name": "CARRIE DENG", "count": 80, "terms": [ { "term": "1264", "count": 80, "sections": 1, "gpa": 3.9625 } ] }, { "uid": "instructor_3015ada0cf27981ba2786718", "name": "YIJING XU", "count": 63, "terms": [ { "term": "1262", "count": 63, "sections": 2, "gpa": 3.7063492063492065 } ] }, { "uid": "instructor_49466c521ec60d9cb2a0ebb6", "name": "ZHONGTIAN CHEN", "count": 63, "terms": [ { "term": "1262", "count": 63, "sections": 2, "gpa": 3.7063492063492065 } ] } ] ``` ## following | code | title | | ---------- | --------------------------------------------------------------------- | | GENBUS 891 | TEXT MINING AND GENERATION FOR BUSINESS ANALYTICS | | GENBUS 894 | PITFALLS, ETHICS, COMMUNICATION, AND LEADERSHIP IN BUSINESS ANALYTICS |