# ECON 725: Machine Learning for Economists | UW–Madison [View on UW Courses](https://uwcourses.com/courses/ECON_725) ## 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 0a2f6473c96e400ca703ccd70741dd6026aba8f60f08d54f7a5fb339f4ca8604 ### course id ECON 725 ### course uid course\_b36213a543e8a0c4048b3d7a ### catalog version id ac1bbe523d863bd78a254b98c52f5c7c23f6d99380fedf570a28baa60ebecf63 ### course number 725 ### subjects * ECON ### title MACHINE LEARNING FOR ECONOMISTS ### description Introduction to the use of Machine Learning (ML) in economic analysis. Covers basic techniques of ML, much attention will be devoted to evaluating the use of these tools in economics. Learn how economists are integrating the tools of ML with econometric techniques in current empirical research. Gain hands on experience in using these techniques to answer traditional questions of interest to economists. Topics include (i) an in-depth discussion of the differences and similarities in goals, empirical settings and tools between ML and econometrics, (ii) supervised learning methods for regression and classification, unsupervised learning methods, large data analysis and data mining, (iii) recent methods at the intersection of ML and econometrics, designed for causal inference, optimal policy estimation, estimation of counterfactual effects. The methods are taught with an emphasis on practical application. ### requirements text Declared in an Economics graduate program ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 367820e8e507e1a15b37f943967ac31e9b1d113b47355a457577c45e697bab1c ### 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 techniques for economic analysis, focusing on integrating ML with econometrics for causal inference and policy estimation. ### llm topics * Comparison of ML and econometrics goals and tools * Supervised learning methods * Unsupervised learning and data mining * Causal inference and counterfactual estimation ### llm skills * Evaluating machine learning tools for economic applications * Applying supervised learning for regression and classification * Performing unsupervised learning and data mining * Estimating causal effects and counterfactuals using ML ### llm assumed background * Background in econometric techniques * Foundational knowledge of Machine Learning concepts ### llm search phrases * machine learning economics * econometrics ML integration * causal inference machine learning * supervised learning regression classification * unsupervised learning economics ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 16de1d9cb1d2863399766cf1e7339a885798f9e2ab9b3e20da998e505ab5ad36 #### course id ECON 725 #### current instructors None recorded. #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered false #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Fall 2022: 3.74 GPA, 95.2% A/AB (n=42 letter grades); Spring 2025: 3.30 GPA, 54.4% A/AB (n=57 letter grades); Spring 2026: 3.55 GPA, 74.4% A/AB (n=39 letter grades). ```json { "citations": [ { "course_id": "ECON 725", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "c3d23c5b-6924-3207-885d-b63cdbe809e8", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1232", "type": "grade" }, { "course_id": "ECON 725", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "c3d23c5b-6924-3207-885d-b63cdbe809e8", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "ECON 725", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "c3d23c5b-6924-3207-885d-b63cdbe809e8", "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 None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [], "condition": "Declared in an Economics graduate program", "course": null, "evidence": "Declared in an Economics graduate program", "id": "n0", "kind": "condition" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_2ce2902b90177906f65669b1", "source": "madgrades", "source_instructor_id": "3903967", "identity_basis": "source_id", "identity_status": "source_identified", "name": "CHRISTOPHER SULLIVAN", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": 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20 }, "grade_statistics": { "gpa": 3.256, "graded": 1254, "counts": [ 328, 368, 298, 171, 75, 11, 3 ], "sections": 6 }, "instructor_url": "/instructors/AUSTIN_MILLER--instructor_05006ec4ccc696bbeb51a8f9" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON 725", "course_uid": "course_b36213a543e8a0c4048b3d7a", "term_id": "1212", "term_name": "Fall 2020", "instructors": [ "Christopher Sullivan", "Lorenzo Magnolfi" ], "a": 24, "ab": 21, "b": 13, "bc": 1, "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": 59, "source_aliases": [ "ECON 725" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON 725", "course_uid": "course_b36213a543e8a0c4048b3d7a", "term_id": "1222", "term_name": "Fall 2021", "instructors": [ "Christopher Sullivan", "Lorenzo Magnolfi" ], "a": 16, "ab": 18, "b": 5, "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": 39, "source_aliases": [ "ECON 725" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON 725", "course_uid": "course_b36213a543e8a0c4048b3d7a", "term_id": "1232", "term_name": "Fall 2022", "instructors": [ "Christopher Sullivan", "Lorenzo Magnolfi" ], "a": 22, "ab": 18, "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": 42, "source_aliases": [ "ECON 725" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON 725", "course_uid": "course_b36213a543e8a0c4048b3d7a", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Lydia Cox", "Miguel Acosta" ], "a": 11, "ab": 20, "b": 18, "bc": 8, "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": 57, "source_aliases": [ "ECON 725" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON 725", "course_uid": "course_b36213a543e8a0c4048b3d7a", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Austin Miller" ], "a": 16, "ab": 13, "b": 8, "bc": 2, "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": 39, "source_aliases": [ "ECON 725" ] } ] ``` ### statistics #### gpa 3.544 #### graded 236 #### counts * 89 * 90 * 46 * 11 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_b36213a543e8a0c4048b3d7a-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_b36213a543e8a0c4048b3d7a-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_b36213a543e8a0c4048b3d7a-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_b36213a543e8a0c4048b3d7a-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_b36213a543e8a0c4048b3d7a-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_b36213a543e8a0c4048b3d7a-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.544491525423729 #### count 236 #### university ##### size 3358 ##### gpa Percentile 28 ##### count Percentile 77 ##### median Count 89 ##### 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 | 0 | false | | 2.4–2.8 | 15 | false | | 2.8–3.2 | 229 | false | | 3.2–3.6 | 856 | true | | 3.6–4.0 | 2258 | false | #### departments ```json [ { "subject": "ECON", "comparison": { "size": 89, "gpaPercentile": 53, "countPercentile": 76, "medianCount": 112, "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": 16, "current": false }, { "range": "3.2–3.6", "count": 38, "current": true }, { "range": "3.6–4.0", "count": 35, "current": false } ] } } ] ``` ### terms #### 1212 ##### term 1212 ##### gpa 3.5762711864406778 ##### count 59 ##### university ###### size 1111 ###### gpa Percentile 43 ###### count Percentile 42 ###### median Count 68 ###### 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 | 0 | false | | 2.4–2.8 | 4 | false | | 2.8–3.2 | 159 | false | | 3.2–3.6 | 337 | true | | 3.6–4.0 | 611 | false | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 43, "gpaPercentile": 69, "countPercentile": 45, "medianCount": 62, "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": 10, "current": false }, { "range": "3.2–3.6", "count": 21, "current": true }, { "range": "3.6–4.0", "count": 12, "current": false } ] } } ] ``` #### 1222 ##### term 1222 ##### gpa 3.641025641025641 ##### count 39 ##### university ###### size 1157 ###### gpa Percentile 51 ###### count Percentile 20 ###### median Count 64 ###### 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 | 17 | false | | 2.8–3.2 | 174 | false | | 3.2–3.6 | 351 | false | | 3.6–4.0 | 614 | true | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 37, "gpaPercentile": 81, "countPercentile": 14, "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": 10, "current": false }, { "range": "3.2–3.6", "count": 17, "current": false }, { "range": "3.6–4.0", "count": 10, "current": true } ] } } ] ``` #### 1232 ##### term 1232 ##### gpa 3.738095238095238 ##### count 42 ##### university ###### size 1216 ###### gpa Percentile 62 ###### count Percentile 25 ###### 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 | 2 | false | | 2.4–2.8 | 14 | false | | 2.8–3.2 | 174 | false | | 3.2–3.6 | 356 | false | | 3.6–4.0 | 670 | true | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 38, "gpaPercentile": 86, "countPercentile": 22, "medianCount": 68.5, "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": 15, "current": false }, { "range": "3.2–3.6", "count": 14, "current": false }, { "range": "3.6–4.0", "count": 9, "current": true } ] } } ] ``` #### 1254 ##### term 1254 ##### gpa 3.2982456140350878 ##### count 57 ##### university ###### size 1289 ###### gpa Percentile 17 ###### count Percentile 43 ###### 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 | 0 | false | | 2.4–2.8 | 17 | false | | 2.8–3.2 | 128 | false | | 3.2–3.6 | 379 | true | | 3.6–4.0 | 765 | false | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 39, "gpaPercentile": 39, "countPercentile": 42, "medianCount": 73, "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": 12, "current": false }, { "range": "3.2–3.6", "count": 17, "current": true }, { "range": "3.6–4.0", "count": 10, "current": false } ] } } ] ``` #### 1264 ##### term 1264 ##### gpa 3.551282051282051 ##### count 39 ##### university ###### size 1283 ###### gpa Percentile 34 ###### count Percentile 18 ###### 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 | true | | 3.6–4.0 | 789 | false | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 37, "gpaPercentile": 67, "countPercentile": 17, "medianCount": 53, "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": 10, "current": false }, { "range": "3.2–3.6", "count": 16, "current": true }, { "range": "3.6–4.0", "count": 11, "current": false } ] } } ] ``` ### benchmarks #### all ##### school ###### size 3358 ###### gpa 3.676658734418077 ###### top Share 83.11767813859053 ###### count 89 ##### ECON ###### size 89 ###### gpa 3.527327440265122 ###### top Share 73.2170712306799 ###### count 112 #### terms ##### 1212 ###### school ###### size 1111 ###### gpa 3.5856416028074487 ###### top Share 77.58926748660845 ###### count 68 ###### ECON ###### size 43 ###### gpa 3.4217856837040865 ###### top Share 67.0762430121588 ###### count 62 ##### 1222 ###### school ###### size 1157 ###### gpa 3.563052311875811 ###### top Share 76.64869058732418 ###### count 64 ###### ECON ###### size 37 ###### gpa 3.4063875168428908 ###### top Share 64.51912969847739 ###### count 79 ##### 1232 ###### school ###### size 1216 ###### gpa 3.575457431972425 ###### top Share 77.33712216234007 ###### count 66 ###### ECON ###### size 38 ###### gpa 3.3360474441634977 ###### top Share 61.683172425833845 ###### count 68.5 ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ###### ECON ###### size 39 ###### gpa 3.4082741335226703 ###### top Share 66.64551412266185 ###### count 73 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### ECON ###### size 37 ###### gpa 3.41582518818968 ###### top Share 66.18686699083136 ###### count 53 ## instructor Trends ```json [ { "uid": "instructor_2ce2902b90177906f65669b1", "name": "CHRISTOPHER SULLIVAN", "count": 140, "terms": [ { "term": "1212", "count": 59, "sections": 1, "gpa": 3.5762711864406778 }, { "term": "1222", "count": 39, "sections": 1, "gpa": 3.641025641025641 }, { "term": "1232", "count": 42, "sections": 1, "gpa": 3.738095238095238 } ] }, { "uid": "instructor_cecf981891a26f6edd514d73", "name": "LORENZO MAGNOLFI", "count": 140, "terms": [ { "term": "1212", "count": 59, "sections": 1, "gpa": 3.5762711864406778 }, { "term": "1222", "count": 39, "sections": 1, "gpa": 3.641025641025641 }, { "term": "1232", "count": 42, "sections": 1, "gpa": 3.738095238095238 } ] }, { "uid": "instructor_38f8efdb9b4b1a3385733490", "name": "MIGUEL ACOSTA", "count": 57, "terms": [ { "term": "1254", "count": 57, "sections": 1, "gpa": 3.2982456140350878 } ] }, { "uid": "instructor_e5e1e12b900409cd15b7c722", "name": "LYDIA COX", "count": 57, "terms": [ { "term": "1254", "count": 57, "sections": 1, "gpa": 3.2982456140350878 } ] }, { "uid": "instructor_05006ec4ccc696bbeb51a8f9", "name": "AUSTIN MILLER", "count": 39, "terms": [ { "term": "1264", "count": 39, "sections": 1, "gpa": 3.551282051282051 } ] } ] ``` ## following | code | title | | -------- | -------------------------------------------------- | | ECON 726 | APPLICATIONS OF MACHINE LEARNING IN ECONOMICS | | ECON 771 | ADVANCES IN ARTIFICIAL INTELLIGENCE FOR ECONOMISTS |