# MATH 616: Data-driven Dynamical Systems, Stochastic Modeling and Prediction | UW–Madison [View on UW Courses](https://uwcourses.com/courses/MATH_616) ## 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 9640fddc71a6c47ba0068fc2f36c8181a7376a544f2db589364f477e1abb1b7b ### course id MATH 616 ### course uid course\_2d244e0d230263518e578140 ### catalog version id 1061ee60bc95f0ea74b48b5921cc2d62e078adb90e2bccbf5ef4fa57ae15b9a3 ### course number 616 ### subjects * MATH ### title DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION ### description An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction. ### requirements text (MATH 320,340,341,345, or375) and (STAT/​MATH  309,431,STAT 311, orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate) ### credit offering ids None recorded. ### llm job id enrich-f516c4d3e82cfe326b4f5f54 ### llm output id 35f66f3e44746496adbb4c6054a7d1c44c8968a4c90c79865480acb5e528db84 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Introduction to data-driven dynamical systems, stochastic modeling, and prediction using numerical algorithms and programming. ### llm topics * Stochastic toolkits for dynamical systems and data science * Linear Gaussian processes * Nonlinear stochastic systems * Elementary stochastic differential equations * Data assimilation * Parameter estimation * Forecasting and prediction ### llm skills * Programming for data-driven systems * Numerical algorithms for dynamical systems * Stochastic toolkits for data science * Data assimilation and prediction ### llm assumed background * Linear algebra and differential equations * Probability and mathematical statistics * Programming in Python ### llm search phrases * data-driven dynamical systems * stochastic modeling * data assimilation * parameter estimation * linear Gaussian processes * nonlinear stochastic systems * stochastic differential equations * forecasting and prediction * numerical algorithms * data science applications ### llm requirements status valid ### llm student summary status valid ### llm experience status valid ### catalog variants None recorded. ### student summary #### context hash cd5be15efd60b9e7d8d2b3670dfd1ae2e5f50af26173e52d2e7a3c0631a7cf67 #### course id MATH 616 #### current instructors None recorded. #### difficulty workload Historical reviews of Samuel Stechmann: The course is rated as having low difficulty, with a difficulty rating of 2. ```json { "citations": [ { "instructor_name": "Samuel Stechmann", "review_date": "2024-09-24 17:47:59 +0000 UTC", "review_id": "f542799895be5d87dd9d77db", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:1671306", "source_review_id": "UmF0aW5nLTM5NzYyMTk5", "source_url": "https://www.ratemyprofessors.com/professor/1671306", "type": "review" } ] } ``` #### errors None recorded. #### historical context Historical reviews of Samuel Stechmann: Samuel Stechmann is described as an awesome professor who delivers funny, clear, and engaging lectures. Reviewers strongly recommend taking a class with him if the opportunity arises. ```json { "citations": [ { "instructor_name": "Samuel Stechmann", "review_date": "2024-09-24 17:47:59 +0000 UTC", "review_id": "f542799895be5d87dd9d77db", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:1671306", "source_review_id": "UmF0aW5nLTM5NzYyMTk5", "source_url": "https://www.ratemyprofessors.com/professor/1671306", "type": "review" } ] } ``` #### offered false #### profile hash e59ddc7389015d0035b68cd195c939d475bf72b959b29cf12eab59b454ccaef1 #### quick take Historical reviews of Samuel Stechmann: Samuel Stechmann delivers funny, clear, and engaging lectures, making his course highly recommended for those who have the opportunity to take it with him. ```json { "citations": [ { "instructor_name": "Samuel Stechmann", "review_date": "2024-09-24 17:47:59 +0000 UTC", "review_id": "f542799895be5d87dd9d77db", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:1671306", "source_review_id": "UmF0aW5nLTM5NzYyMTk5", "source_url": "https://www.ratemyprofessors.com/professor/1671306", "type": "review" } ] } ``` Recent recorded grades — Fall 2024: 3.57 GPA, 75.7% A/AB (n=37 letter grades); Spring 2026: 4.00 GPA, 100.0% A/AB (n=40 letter grades). ```json { "citations": [ { "course_id": "MATH 616", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "d591f0d6-e77d-3c9c-b084-f62a18412e1f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1252", "type": "grade" }, { "course_id": "MATH 616", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "d591f0d6-e77d-3c9c-b084-f62a18412e1f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience Historical reviews of Samuel Stechmann: Students find the lectures engaging and clear, describing the professor as funny and highly effective. ```json { "citations": [ { "instructor_name": "Samuel Stechmann", "review_date": "2024-09-24 17:47:59 +0000 UTC", "review_id": "f542799895be5d87dd9d77db", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:1671306", "source_review_id": "UmF0aW5nLTM5NzYyMTk5", "source_url": "https://www.ratemyprofessors.com/professor/1671306", "type": "review" } ] } ``` #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history SAMUEL STECHMANN is recorded teaching in Fall 2024. Recorded history may be incomplete and does not establish a future schedule. ```json { "citations": [ { "course_id": "MATH 616", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "d591f0d6-e77d-3c9c-b084-f62a18412e1f", "source_record": { "entity_id": "d591f0d6-e77d-3c9c-b084-f62a18412e1f", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1252", "type": "grade" } ] } ``` #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [ "n1", "n2", "n3" ], "condition": null, "course": null, "evidence": "(MATH 320,340,341,345, or375) and (STAT/MATH 309,431,STAT 311, orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)", "id": "n0", "kind": "any" }, { "children": [ "n4", "n5", "n6" ], "condition": null, "course": null, "evidence": "(MATH 320,340,341,345, or375) and (STAT/MATH 309,431,STAT 311, orMATH 531) and (MATH 322,341,375,421, or467)", "id": "n1", "kind": "all" }, { "children": [], "condition": "graduate/professional standing", "course": null, "evidence": "graduate/professional standing", "id": "n2", "kind": "condition" }, { "children": [], "condition": "declared in Mathematics VISP (undergraduate or graduate)", "course": null, "evidence": "declared in Mathematics VISP (undergraduate or graduate)", "id": "n3", "kind": "condition" }, { "children": [ "n7", "n8", "n9", "n10", "n11" ], "condition": null, "course": null, "evidence": "(MATH 320,340,341,345, or375)", "id": "n4", "kind": "any" }, { "children": [ "n12", "n13", "n14", "n15" ], "condition": null, "course": null, "evidence": "(STAT/MATH 309,431,STAT 311, orMATH 531)", "id": "n5", "kind": "any" }, { "children": [ "n16", "n17", "n18", "n19", "n20" ], "condition": null, "course": null, "evidence": "(MATH 322,341,375,421, or467)", "id": "n6", "kind": "any" }, { "children": [], 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"course_number": 309, "minimum_grade": null, "subjects": [ "MATH", "STAT" ], "timing": "prior" }, "evidence": "STAT/MATH 309", "id": "n12", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 431, "minimum_grade": null, "subjects": [ "MATH", "STAT" ], "timing": "prior" }, "evidence": "431", "id": "n13", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 311, "minimum_grade": null, "subjects": [ "STAT" ], "timing": "prior" }, "evidence": "STAT 311", "id": "n14", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 531, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "MATH 531", "id": "n15", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 322, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "MATH 322", "id": "n16", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 341, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "341", "id": "n17", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 375, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "375", "id": "n18", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 421, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "421", "id": "n19", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 467, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "467", "id": "n20", "kind": "course" } ] ``` #### notes * Fixed missing node references and updated course evidence to exact source substrings. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": 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"quality_count": 4, "difficulty_count": 4 }, "course_0002a781bc9543f3a2ac1a94": { "review_count": 2, "quality": 4.5, "difficulty": 3.5, "quality_count": 2, "difficulty_count": 2 }, "course_dcdb624e3fb8cbaf9a557ab9": { "review_count": 3, "quality": 4, "difficulty": 3.33, "quality_count": 3, "difficulty_count": 3 }, "course_346ab4e00def5d21d922d818": { "review_count": 1, "quality": 5, "difficulty": 1, "quality_count": 1, "difficulty_count": 1 } }, "bayesian_quality": 3.8483081504104293, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 2.978, "graded": 1527, "counts": [ 490, 177, 383, 117, 213, 100, 47 ], "sections": 53 }, "instructor_url": "/instructors/SAMUEL_STECHMANN--instructor_e8033b131444ac21313c075f" }, { "instructor_uid": "instructor_1227fa1f8db520a460645e3f", "source": "madgrades", "source_instructor_id": "4891217", "identity_basis": "source_id", "identity_status": "source_identified", "name": "NAN 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", "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 }, "grade_statistics": { "gpa": 3.633, "graded": 654, "counts": [ 452, 76, 58, 32, 19, 10, 7 ], "sections": 32 }, "instructor_url": "/instructors/NAN_CHEN--instructor_1227fa1f8db520a460645e3f" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 616", "course_uid": "course_2d244e0d230263518e578140", "term_id": "1252", "term_name": "Fall 2024", "instructors": [ "SAMUEL STECHMANN" ], "a": 15, "ab": 13, "b": 8, "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": 37, "source_aliases": [ "MATH 616" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 616", "course_uid": "course_2d244e0d230263518e578140", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Nan Chen" ], "a": 40, "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": 40, "source_aliases": [ "MATH 616" ] } ] ``` ### statistics #### gpa 3.792 #### graded 77 #### counts * 55 * 13 * 8 * 1 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_2d244e0d230263518e578140-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_2d244e0d230263518e578140-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_2d244e0d230263518e578140-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_2d244e0d230263518e578140-0.json) #### reviews * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course\_2d244e0d230263518e578140-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course_2d244e0d230263518e578140-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_2d244e0d230263518e578140-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_2d244e0d230263518e578140-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.792207792207792 #### count 77 #### university ##### size 2124 ##### gpa Percentile 60 ##### count Percentile 52 ##### median Count 72 ##### 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 | 16 | false | | 2.8–3.2 | 156 | false | | 3.2–3.6 | 599 | false | | 3.6–4.0 | 1353 | true | #### departments ```json [ { "subject": "MATH", "comparison": { "size": 55, "gpaPercentile": 93, "countPercentile": 41, "medianCount": 97, "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": 6, "current": false }, { "range": "2.8–3.2", "count": 20, "current": false }, { "range": "3.2–3.6", "count": 19, "current": false }, { "range": "3.6–4.0", "count": 10, "current": true } ] } } ] ``` ### terms #### 1252 ##### term 1252 ##### gpa 3.5675675675675675 ##### count 37 ##### university ###### size 1333 ###### gpa Percentile 39 ###### count Percentile 15 ###### median Count 69 ###### 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 | 14 | false | | 2.8–3.2 | 115 | false | | 3.2–3.6 | 431 | true | | 3.6–4.0 | 773 | false | ##### departments ```json [ { "subject": "MATH", "comparison": { "size": 43, "gpaPercentile": 95, "countPercentile": 12, "medianCount": 74, "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": 5, "current": false }, { "range": "2.8–3.2", "count": 20, "current": false }, { "range": "3.2–3.6", "count": 16, "current": true }, { "range": "3.6–4.0", "count": 2, "current": false } ] } } ] ``` #### 1264 ##### term 1264 ##### gpa 4 ##### count 40 ##### university ###### size 1283 ###### gpa Percentile 95 ###### count Percentile 21 ###### 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": "MATH", "comparison": { "size": 42, "gpaPercentile": 100, "countPercentile": 17, "medianCount": 80, "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": 1, "current": false }, { "range": "2.4–2.8", "count": 7, "current": false }, { "range": "2.8–3.2", "count": 17, "current": false }, { "range": "3.2–3.6", "count": 11, "current": false }, { "range": "3.6–4.0", "count": 6, "current": true } ] } } ] ``` ### benchmarks #### all ##### school ###### size 2124 ###### gpa 3.654433279098965 ###### top Share 81.90118684490442 ###### count 72 ##### MATH ###### size 55 ###### gpa 3.247873874239582 ###### top Share 57.639013141414146 ###### count 97 #### terms ##### 1252 ###### school ###### size 1333 ###### gpa 3.619494049739118 ###### top Share 79.71442190500672 ###### count 69 ###### MATH ###### size 43 ###### gpa 3.185692541204979 ###### top Share 54.14175342403773 ###### count 74 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### MATH ###### size 42 ###### gpa 3.13598176627681 ###### top Share 52.25969905887213 ###### count 80 ## instructor Trends ```json [ { "uid": "instructor_1227fa1f8db520a460645e3f", "name": "NAN CHEN", "count": 40, "terms": [ { "term": "1264", "count": 40, "sections": 1, "gpa": 4 } ] }, { "uid": "instructor_e8033b131444ac21313c075f", "name": "SAMUEL STECHMANN", "count": 37, "terms": [ { "term": "1252", "count": 37, "sections": 1, "gpa": 3.5675675675675675 } ] } ] ``` ## following None recorded.