# MATH 717: Stochastic Computational Methods | UW–Madison [View on UW Courses](https://uwcourses.com/courses/MATH_717) ## 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 503dc82c1164a09710fd48a2c8aa5105172f926024e139a9a42a4a536ce7f34d ### course id MATH 717 ### course uid course\_6b1402df5d4cb88b5d36366c ### catalog version id 3519d08d788bd92d513e75899a9dd75ff9aabd6042005b3cee90c3868ec0d590 ### course number 717 ### subjects * MATH ### title STOCHASTIC COMPUTATIONAL METHODS ### description Introduction to computational methods that use stochastic algorithms and/or methods that are applied to random or stochastic mathematical problems. The main emphasis will be placed on learning practical tools, while some aspects of theoretical foundations will also be covered (e.g., basic error analysis for numerical solution of stochastic differential equations (SDEs), and basic convergence of Monte Carlo methods). Topics include Monte Carlo methods, Bayesian inference and Bayesian sampling, simulation of Markov chains, numerical analysis for SDEs, data assimilation / state estimation, stochastic optimization methods and random sketching. Applications to science, engineering, finance, data science, and other practical problems also included. ### requirements text Graduate/professional standing or declared in Mathematics Visiting International Student Program (graduate or dissertator) ### credits min 3 ### credits max 3 ### credit offering ids * 1272:600:026275 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id ac37e4476ea682efe9fd7eff4014a8bded9a997735d39c4e4202c2972e72e7a7 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary MATH 717 introduces computational methods using stochastic algorithms for random mathematical problems, covering Monte Carlo, Bayesian inference, and SDEs. ### llm topics * Monte Carlo methods * Bayesian inference and sampling * Markov chain simulation * Numerical analysis for Stochastic Differential Equations * Data assimilation and state estimation * Stochastic optimization and random sketching ### llm skills * Application of practical computational tools * Error analysis for numerical solutions of SDEs * Analysis of Monte Carlo method convergence * Simulation of Markov chains * Data assimilation and state estimation * Stochastic optimization and random sketching ### llm assumed background None recorded. ### llm search phrases * stochastic computational methods * Monte Carlo methods * Bayesian inference * Markov chain simulation * stochastic differential equations * data assimilation * stochastic optimization ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 487990c261e9e05a000c18364d7ee9dd7d38f0f425570bde2384825ea9196ae4 #### course id MATH 717 #### current instructors ```json [ { "instructor_uid": "instructor_eed27452237c073abcf30787", "message": "No course-specific reviews available", "name": "Nan Chen", "review_status": "no_course_reviews", "rmp_instructor_id": "rmp:2402431", "summary": [ { "citations": [ { "course_id": "MATH 717", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "source_record": { "entity_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1232", "type": "grade" }, { "course_id": "MATH 717", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "source_record": { "entity_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1244", "type": "grade" }, { "course_id": "MATH 717", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "source_record": { "entity_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" } ], "text": "Recent recorded grades — Fall 2022: 3.97 GPA, 100.0% A/AB (n=19 letter grades); Spring 2024: 3.94 GPA, 95.0% A/AB (n=40 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=34 letter grades)." } ] } ] ``` #### 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 — Spring 2024: 3.94 GPA, 95.0% A/AB (n=40 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=25 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=34 letter grades). ```json { "citations": [ { "course_id": "MATH 717", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1244", "type": "grade" }, { "course_id": "MATH 717", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "MATH 717", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5ebfd0ab-a497-3859-9248-0c633b433d48", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1262", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history NAN CHEN is recorded teaching in Fall 2022, Spring 2024, Fall 2025. 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] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors ```json [ { "instructor_uid": "instructor_eed27452237c073abcf30787", "source": "enrollment", "source_instructor_id": "nchen29", "identity_basis": "netid", "identity_status": "source_identified", "name": "Nan Chen", "email": "CHENNAN@MATH.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", "ratings": { "review_count": 29, "quality": 4.59, "difficulty": 3.1, "quality_count": 29, "difficulty_count": 29, "profile_id": "rmp:2402431", "source_url": "https://www.ratemyprofessors.com/professor/2402431", "match_basis": "exact_name", "observed_at": "2026-09-07 16:14:47.792861+00:00", "courses": { "course_6bc496e4640fd08f71e1b041": { "review_count": 3, "quality": 3.67, "difficulty": 3.67, "quality_count": 3, "difficulty_count": 3 }, "course_346ab4e00def5d21d922d818": { "review_count": 1, "quality": 5, "difficulty": 4, "quality_count": 1, "difficulty_count": 1 }, "course_8634dde81cc95f6c58014087": { "review_count": 21, "quality": 4.86, "difficulty": 2.95, "quality_count": 21, "difficulty_count": 21 } }, "bayesian_quality": 4.210279911651671, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "instructor_url": "/instructors/NAN_CHEN" } ] ``` ### offerings ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:600:026275", "course_id": "MATH 717", "course_uid": "course_6b1402df5d4cb88b5d36366c", "term_id": "1272", "source_course_id": "026275", "source_subject_id": "600", "title": "Stochastic Computational Methods", "credits_min": 3, "credits_max": 3, "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:1272:31326", "term_id": "1272", "source_section_id": "31326", "identity_basis": "class_number", 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"quality_count": 3, "difficulty_count": 3 }, "course_346ab4e00def5d21d922d818": { "review_count": 1, "quality": 5, "difficulty": 4, "quality_count": 1, "difficulty_count": 1 }, "course_8634dde81cc95f6c58014087": { "review_count": 21, "quality": 4.86, "difficulty": 2.95, "quality_count": 21, "difficulty_count": 21 } }, "bayesian_quality": 4.210279911651671, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.633, "graded": 654, "counts": [ 452, 76, 58, 32, 19, 10, 7 ], "sections": 32 }, "instructor_url": "/instructors/NAN_CHEN--instructor_1227fa1f8db520a460645e3f" }, { "instructor_uid": "instructor_6cee1ae6c2c6ae042c8870e4", "source": "madgrades", "source_instructor_id": "4433347", "identity_basis": "source_id", "identity_status": "source_identified", "name": "QIN LI", "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", "ratings": { "review_count": 28, "quality": 3.79, "difficulty": 3.11, "quality_count": 28, "difficulty_count": 28, "profile_id": "rmp:2059558", "source_url": "https://www.ratemyprofessors.com/professor/2059558", "match_basis": "exact_name", "observed_at": "2026-09-07 16:11:25.404917+00:00", "courses": { "course_cc1de60e9953cdd9c469e9a5": { "review_count": 6, "quality": 3.17, "difficulty": 2.83, "quality_count": 6, "difficulty_count": 6 }, "course_f237678d9fac59631b4aa4ff": { "review_count": 6, "quality": 5, "difficulty": 3.5, "quality_count": 6, "difficulty_count": 6 }, "course_0002a781bc9543f3a2ac1a94": { "review_count": 16, "quality": 3.56, "difficulty": 3.06, "quality_count": 16, "difficulty_count": 16 } }, "bayesian_quality": 3.7357024098110814, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.072, "graded": 1421, "counts": [ 489, 237, 270, 133, 178, 82, 32 ], "sections": 44 }, "instructor_url": "/instructors/QIN_LI--instructor_6cee1ae6c2c6ae042c8870e4" }, { "instructor_uid": "instructor_eed27452237c073abcf30787", "source": "enrollment", "source_instructor_id": "nchen29", "identity_basis": "netid", "identity_status": "source_identified", "name": "Nan Chen", "email": "CHENNAN@MATH.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", "ratings": { "review_count": 29, "quality": 4.59, "difficulty": 3.1, "quality_count": 29, "difficulty_count": 29, "profile_id": "rmp:2402431", "source_url": "https://www.ratemyprofessors.com/professor/2402431", "match_basis": "exact_name", "observed_at": "2026-09-07 16:14:47.792861+00:00", "courses": { "course_6bc496e4640fd08f71e1b041": { "review_count": 3, "quality": 3.67, "difficulty": 3.67, "quality_count": 3, "difficulty_count": 3 }, "course_346ab4e00def5d21d922d818": { "review_count": 1, "quality": 5, "difficulty": 4, "quality_count": 1, "difficulty_count": 1 }, "course_8634dde81cc95f6c58014087": { "review_count": 21, "quality": 4.86, "difficulty": 2.95, "quality_count": 21, "difficulty_count": 21 } }, "bayesian_quality": 4.210279911651671, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "instructor_url": "/instructors/NAN_CHEN" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 717", "course_uid": "course_6b1402df5d4cb88b5d36366c", "term_id": "1232", "term_name": "Fall 2022", "instructors": [ "Nan Chen" ], "a": 18, "ab": 1, "b": 0, "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": 19, "source_aliases": [ "MATH 717" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 717", "course_uid": "course_6b1402df5d4cb88b5d36366c", "term_id": "1244", "term_name": "Spring 2024", "instructors": [ "Nan Chen" ], "a": 37, "ab": 1, "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": 40, "source_aliases": [ "MATH 717" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 717", "course_uid": "course_6b1402df5d4cb88b5d36366c", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Qin Li" ], "a": 25, "ab": 0, "b": 0, "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": 25, "source_aliases": [ "MATH 717" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 717", "course_uid": "course_6b1402df5d4cb88b5d36366c", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "Nan Chen" ], "a": 34, "ab": 0, "b": 0, "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": 34, "source_aliases": [ "MATH 717" ] } ] ``` ### statistics #### gpa 3.975 #### graded 118 #### counts * 114 * 2 * 2 * 0 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_6b1402df5d4cb88b5d36366c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_6b1402df5d4cb88b5d36366c-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_6b1402df5d4cb88b5d36366c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_6b1402df5d4cb88b5d36366c-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_6b1402df5d4cb88b5d36366c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_6b1402df5d4cb88b5d36366c-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_6b1402df5d4cb88b5d36366c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_6b1402df5d4cb88b5d36366c-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.9745762711864407 #### count 118 #### university ##### size 3107 ##### gpa Percentile 89 ##### count Percentile 61 ##### median Count 83 ##### 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 | 198 | false | | 3.2–3.6 | 805 | false | | 3.6–4.0 | 2087 | true | #### departments ```json [ { "subject": "MATH", "comparison": { "size": 86, "gpaPercentile": 89, "countPercentile": 62, "medianCount": 75.5, "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": 4, "current": false }, { "range": "2.8–3.2", "count": 25, "current": false }, { "range": "3.2–3.6", "count": 24, "current": false }, { "range": "3.6–4.0", "count": 33, "current": true } ] } } ] ``` ### terms #### 1232 ##### term 1232 ##### gpa 3.973684210526316 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | MATH | | #### 1244 ##### term 1244 ##### gpa 3.9375 ##### count 40 ##### university ###### size 1241 ###### gpa Percentile 87 ###### count Percentile 22 ###### median Count 65 ###### 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 | 9 | false | | 2.8–3.2 | 151 | false | | 3.2–3.6 | 373 | false | | 3.6–4.0 | 706 | true | ##### departments ```json [ { "subject": "MATH", "comparison": { "size": 43, "gpaPercentile": 95, "countPercentile": 24, "medianCount": 68, "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": 2, "current": false }, { "range": "2.4–2.8", "count": 2, "current": false }, { "range": "2.8–3.2", "count": 20, "current": false }, { "range": "3.2–3.6", "count": 11, "current": false }, { "range": "3.6–4.0", "count": 8, "current": true } ] } } ] ``` #### 1254 ##### term 1254 ##### gpa 4 ##### count 25 ##### departments | subject | comparison | | ------- | ---------- | | MATH | | #### 1262 ##### term 1262 ##### gpa 4 ##### count 34 ##### university ###### size 1320 ###### gpa Percentile 95 ###### count Percentile 8 ###### 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": "MATH", "comparison": { "size": 43, "gpaPercentile": 98, "countPercentile": 12, "medianCount": 81, "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": 3, "current": false }, { "range": "2.8–3.2", "count": 18, "current": false }, { "range": "3.2–3.6", "count": 16, "current": false }, { "range": "3.6–4.0", "count": 6, "current": true } ] } } ] ``` ### benchmarks #### all ##### school ###### size 3107 ###### gpa 3.6753731957792684 ###### top Share 82.95049894654645 ###### count 83 ##### MATH ###### size 86 ###### gpa 3.450161104669193 ###### top Share 68.58299792804891 ###### count 75.5 #### terms ##### 1232 ###### school ###### size 1216 ###### gpa 3.575457431972425 ###### top Share 77.33712216234007 ###### count 66 ###### MATH ###### size 43 ###### gpa 3.2486774162409366 ###### top Share 56.56203211471963 ###### count 69 ##### 1244 ###### school ###### size 1241 ###### gpa 3.5975573126929192 ###### top Share 78.29036874847135 ###### count 65 ###### MATH ###### size 43 ###### gpa 3.228240949537157 ###### top Share 55.86451059486035 ###### count 68 ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ###### MATH ###### size 39 ###### gpa 3.180435541912479 ###### top Share 53.878371816747475 ###### count 89 ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ###### MATH ###### size 43 ###### gpa 3.2481769276584003 ###### top Share 56.325967864851854 ###### count 81 ## instructor Trends ```json [ { "uid": "instructor_1227fa1f8db520a460645e3f", "name": "NAN CHEN", "count": 93, "terms": [ { "term": "1232", "count": 19, "sections": 1, "gpa": 3.973684210526316 }, { "term": "1244", "count": 40, "sections": 1, "gpa": 3.9375 }, { "term": "1262", "count": 34, "sections": 1, "gpa": 4 } ] }, { "uid": "instructor_6cee1ae6c2c6ae042c8870e4", "name": "QIN LI", "count": 25, "terms": [ { "term": "1254", "count": 25, "sections": 1, "gpa": 4 } ] } ] ``` ## following None recorded.