# STAT 611: Statistical Models for Data Science | UW–Madison [View on UW Courses](https://uwcourses.com/courses/STAT_611) ## 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 dc95f5821a1bb47869d72c2740f749ac789cc12ecf69ecc566fbfb3185797cc0 ### course id STAT 611 ### course uid course\_802dd3b68ec2677c556307b6 ### catalog version id dd9d36fda78b4200b2a584d1f803c61aa2bf82a4ce0887a0e6811a1232fc1a83 ### course number 611 ### subjects * STAT ### title STATISTICAL MODELS FOR DATA SCIENCE ### description Probability, random variables and their distributions, joint and conditional distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts and limit theorems for sequences of random variables. ### requirements text Declared in Data Science MS or Data Engineering MS ### credits min 3 ### credits max 3 ### credit offering ids * 1272:932:026262 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 750d704b407184f1b831f1b4c5b7fa8be70426c604d6fb9aac8319bb77870b28 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary STAT 611 covers probability, distributions, generating functions, and limit theorems for statistical modeling in data science. ### llm topics * Probability and random variables * Joint and conditional distributions * Moments and inequalities * Generating functions * Transformations of random variables * Sampling and distribution theory * Convergence concepts and limit theorems ### llm skills * Understanding probability and random variable distributions * Analyzing joint and conditional distributions and moments * Applying generating functions and transformations * Applying sampling and distribution theory * Understanding convergence concepts and limit theorems ### llm assumed background None recorded. ### llm search phrases * STAT 611 statistical models data science * probability random variables distributions * limit theorems random variables * generating functions transformations ### llm requirements status needs\_review ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash e01ce307eec0bb2caeb824cbdc4fa4fa938bf66775526c14b0126e54f11ce9a8 #### course id STAT 611 #### current instructors ```json [ { "instructor_uid": "instructor_f44b89c80752cccf153d0b17", "message": "No course-specific reviews available", "name": "JIAQI Tang", "review_status": "no_course_reviews", "rmp_instructor_id": null, "summary": [] }, { "instructor_uid": "instructor_c937bd5ff5084d3cd8d28c78", "message": "No course-specific reviews available", "name": "Yongyi Guo", "review_status": "no_course_reviews", "rmp_instructor_id": "rmp:3010926", "summary": [] } ] ``` #### 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 2023: 3.61 GPA, 73.7% A/AB (n=19 letter grades); Fall 2024: 3.70 GPA, 86.7% A/AB (n=30 letter grades); Fall 2025: 3.36 GPA, 62.5% A/AB (n=32 letter grades). ```json { "citations": [ { "course_id": "STAT 611", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "6e2c7cac-1516-3bdf-8bdb-647258f99d55", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1242", "type": "grade" }, { "course_id": "STAT 611", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "6e2c7cac-1516-3bdf-8bdb-647258f99d55", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1252", "type": "grade" }, { "course_id": "STAT 611", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "6e2c7cac-1516-3bdf-8bdb-647258f99d55", "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 None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [], "condition": "Declared in Data Science MS or Data Engineering MS", "course": null, "evidence": "Declared in Data Science MS or Data Engineering MS", "id": "n0", "kind": "condition" } ] ``` #### notes * Unlinked program references 'Data Science MS' and 'Data Engineering MS' require manual verification of canonical program identities. #### root n0 #### status needs\_review ### instructors ```json [ { "instructor_uid": "instructor_f44b89c80752cccf153d0b17", "source": "enrollment", "source_instructor_id": "tang274", "identity_basis": "netid", "identity_status": "source_identified", "name": "JIAQI Tang", "email": "TANG274@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/JIAQI_TANG" }, { "instructor_uid": "instructor_c937bd5ff5084d3cd8d28c78", "source": "enrollment", "source_instructor_id": "guo98", "identity_basis": "netid", "identity_status": "source_identified", "name": "Yongyi Guo", "email": "GUO98@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": 6, "quality": 1.17, "difficulty": 4.67, "quality_count": 6, "difficulty_count": 6, "profile_id": "rmp:3010926", "source_url": "https://www.ratemyprofessors.com/professor/3010926", "match_basis": "exact_name", "observed_at": "2026-09-07 15:57:34.668824+00:00", "courses": { "course_d2d0c15e32bd1631a6074548": { "review_count": 6, "quality": 1.17, "difficulty": 4.67, "quality_count": 6, "difficulty_count": 6 } }, "bayesian_quality": 3.085142910420458, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "instructor_url": "/instructors/YONGYI_GUO" } ] ``` ### offerings ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:932:026262", "course_id": "STAT 611", "course_uid": "course_802dd3b68ec2677c556307b6", "term_id": "1272", "source_course_id": "026262", "source_subject_id": "932", "title": "Statistical Models for Data Science", "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:1272:27203", "term_id": "1272", "source_section_id": "27203", "identity_basis": "class_number", "section_number": "001", "section_type": "LEC", "instruction_mode": "Classroom Instruction", "capacity": 45, "enrolled": 21, "waitlisted": 0, "start_date": "2026-09-02 05:00:00+00:00", "end_date": "2026-12-09 06:00:00+00:00" } ] ``` ### grade instructors ```json [ { "instructor_uid": "instructor_a1a6312441a09a9ee137f7b1", "source": "madgrades", "source_instructor_id": "5214164", "identity_basis": "source_id", "identity_status": "source_identified", "name": "GARVESH RASKUTTI", "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": 3.68, "difficulty": 3.79, "quality_count": 28, "difficulty_count": 28, "profile_id": "rmp:1870984", "source_url": "https://www.ratemyprofessors.com/professor/1870984", "match_basis": "exact_name", "observed_at": "2026-09-07 16:38:40.338784+00:00", "courses": { "course_2a00c702ff402614a950606e": { "review_count": 23, "quality": 3.74, "difficulty": 3.78, "quality_count": 23, "difficulty_count": 23 }, "course_e593b86c370a0c671af794df": { "review_count": 3, "quality": 3, "difficulty": 4.33, "quality_count": 3, "difficulty_count": 3 } }, "bayesian_quality": 3.6715357431444153, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.286, "graded": 915, "counts": [ 398, 180, 146, 81, 52, 40, 18 ], "sections": 57 }, "instructor_url": "/instructors/GARVESH_RASKUTTI--instructor_a1a6312441a09a9ee137f7b1" }, { "instructor_uid": "instructor_f509ff4fde897514c5b44d7e", "source": "madgrades", "source_instructor_id": "4839254", "identity_basis": "source_id", "identity_status": "source_identified", "name": "SUSAN GLENN", "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": 3.718, "graded": 39, "counts": [ 24, 10, 3, 2, 0, 0, 0 ], "sections": 2 }, "instructor_url": "/instructors/SUSAN_GLENN--instructor_f509ff4fde897514c5b44d7e" }, { "instructor_uid": "instructor_1f60e25246f6b2efbbdf667b", "source": "madgrades", "source_instructor_id": "6397900", "identity_basis": "source_id", "identity_status": "source_identified", "name": "XINYAN WANG", "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": 3.726, "graded": 62, "counts": [ 32, 26, 4, 0, 0, 0, 0 ], "sections": 2 }, "instructor_url": "/instructors/XINYAN_WANG--instructor_1f60e25246f6b2efbbdf667b" }, { "instructor_uid": "instructor_0b2119f22babc1389a02a8fa", "source": "madgrades", "source_instructor_id": "6487496", "identity_basis": "source_id", "identity_status": "source_identified", "name": "HEYAN ZHANG", "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": 3.431, "graded": 116, "counts": [ 44, 36, 28, 4, 1, 0, 3 ], "sections": 4 }, "instructor_url": "/instructors/HEYAN_ZHANG--instructor_0b2119f22babc1389a02a8fa" }, { "instructor_uid": "instructor_37a7f7533ff672c5e16425d1", "source": "madgrades", "source_instructor_id": "4728474", "identity_basis": "source_id", "identity_status": "source_identified", "name": "MIAOYAN WANG", "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": 3.627, "graded": 272, "counts": [ 118, 111, 42, 0, 0, 0, 1 ], "sections": 22 }, "instructor_url": "/instructors/MIAOYAN_WANG--instructor_37a7f7533ff672c5e16425d1" }, { "instructor_uid": "instructor_bad0c690e1779d556680846f", "source": "madgrades", "source_instructor_id": "6396145", "identity_basis": "source_id", "identity_status": "source_identified", "name": "JOOWON LEE", "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": 3.062, "graded": 161, "counts": [ 36, 38, 43, 16, 20, 7, 1 ], "sections": 3 }, "instructor_url": "/instructors/JOOWON_LEE--instructor_bad0c690e1779d556680846f" }, { "instructor_uid": "instructor_c937bd5ff5084d3cd8d28c78", "source": "enrollment", "source_instructor_id": "guo98", "identity_basis": "netid", "identity_status": "source_identified", "name": "Yongyi Guo", "email": "GUO98@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": 6, "quality": 1.17, "difficulty": 4.67, "quality_count": 6, "difficulty_count": 6, "profile_id": "rmp:3010926", "source_url": "https://www.ratemyprofessors.com/professor/3010926", "match_basis": "exact_name", "observed_at": "2026-09-07 15:57:34.668824+00:00", "courses": { "course_d2d0c15e32bd1631a6074548": { "review_count": 6, "quality": 1.17, "difficulty": 4.67, "quality_count": 6, "difficulty_count": 6 } }, "bayesian_quality": 3.085142910420458, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "instructor_url": "/instructors/YONGYI_GUO" }, { "instructor_uid": "instructor_f44b89c80752cccf153d0b17", "source": "enrollment", "source_instructor_id": "tang274", "identity_basis": "netid", "identity_status": "source_identified", "name": "JIAQI Tang", "email": "TANG274@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/JIAQI_TANG" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 611", "course_uid": "course_802dd3b68ec2677c556307b6", "term_id": "1242", "term_name": "Fall 2023", "instructors": [ "Garvesh Raskutti", "SUSAN GLENN" ], "a": 11, "ab": 3, "b": 3, "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": 19, "source_aliases": [ "STAT 611" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 611", "course_uid": "course_802dd3b68ec2677c556307b6", "term_id": "1252", "term_name": "Fall 2024", "instructors": [ "Garvesh Raskutti", "Xinyan Wang" ], "a": 16, "ab": 10, "b": 4, "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": 30, "source_aliases": [ "STAT 611" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 611", "course_uid": "course_802dd3b68ec2677c556307b6", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "Heyan Zhang", "JOOWON LEE", "Miaoyan Wang" ], "a": 9, "ab": 11, "b": 11, "bc": 0, "c": 0, "d": 0, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 32, "source_aliases": [ "STAT 611" ] } ] ``` ### statistics #### gpa 3.543 #### graded 81 #### counts * 36 * 24 * 18 * 2 * 0 * 0 * 1 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_802dd3b68ec2677c556307b6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_802dd3b68ec2677c556307b6-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_802dd3b68ec2677c556307b6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_802dd3b68ec2677c556307b6-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_802dd3b68ec2677c556307b6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_802dd3b68ec2677c556307b6-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_802dd3b68ec2677c556307b6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_802dd3b68ec2677c556307b6-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.54320987654321 #### count 81 #### university ##### size 2460 ##### gpa Percentile 27 ##### count Percentile 44 ##### median Count 94 ##### 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 | 6 | false | | 2.8–3.2 | 170 | false | | 3.2–3.6 | 630 | true | | 3.6–4.0 | 1654 | false | #### departments ```json [ { "subject": "STAT", "comparison": { "size": 48, "gpaPercentile": 53, "countPercentile": 26, "medianCount": 127.5, "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": 7, "current": false }, { "range": "3.2–3.6", "count": 20, "current": true }, { "range": "3.6–4.0", "count": 21, "current": false } ] } } ] ``` ### terms #### 1242 ##### term 1242 ##### gpa 3.6052631578947367 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1252 ##### term 1252 ##### gpa 3.7 ##### count 30 ##### university ###### size 1333 ###### gpa Percentile 52 ###### count Percentile 0 ###### 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 | false | | 3.6–4.0 | 773 | true | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 32, "gpaPercentile": 84, "countPercentile": 0, "medianCount": 70.5, "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": 9, "current": false }, { "range": "3.2–3.6", "count": 14, "current": false }, { "range": "3.6–4.0", "count": 9, "current": true } ] } } ] ``` #### 1262 ##### term 1262 ##### gpa 3.359375 ##### count 32 ##### university ###### size 1320 ###### gpa Percentile 19 ###### count Percentile 5 ###### 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 | true | | 3.6–4.0 | 797 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 29, "gpaPercentile": 50, "countPercentile": 0, "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": 0, "current": false }, { "range": "2.8–3.2", "count": 9, "current": false }, { "range": "3.2–3.6", "count": 12, "current": true }, { "range": "3.6–4.0", "count": 8, "current": false } ] } } ] ``` ### benchmarks #### all ##### school ###### size 2460 ###### gpa 3.6795793071535745 ###### top Share 83.15650336722103 ###### count 94 ##### STAT ###### size 48 ###### gpa 3.5183385827909035 ###### top Share 73.53242708685785 ###### count 127.5 #### terms ##### 1242 ###### school ###### size 1295 ###### gpa 3.595302892737449 ###### top Share 78.36291889885307 ###### count 67 ###### STAT ###### size 34 ###### gpa 3.4558758327213903 ###### top Share 69.10154863162224 ###### count 55 ##### 1252 ###### school ###### size 1333 ###### gpa 3.619494049739118 ###### top Share 79.71442190500672 ###### count 69 ###### STAT ###### size 32 ###### gpa 3.423918594183889 ###### top Share 68.17668754351217 ###### count 70.5 ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ###### STAT ###### size 29 ###### gpa 3.415022952129974 ###### top Share 64.95385808199006 ###### count 81 ## instructor Trends ```json [ { "uid": "instructor_a1a6312441a09a9ee137f7b1", "name": "GARVESH RASKUTTI", "count": 49, "terms": [ { "term": "1242", "count": 19, "sections": 1, "gpa": 3.6052631578947367 }, { "term": "1252", "count": 30, "sections": 1, "gpa": 3.7 } ] }, { "uid": "instructor_0b2119f22babc1389a02a8fa", "name": "HEYAN ZHANG", "count": 32, "terms": [ { "term": "1262", "count": 32, "sections": 1, "gpa": 3.359375 } ] }, { "uid": "instructor_37a7f7533ff672c5e16425d1", "name": "MIAOYAN WANG", "count": 32, "terms": [ { "term": "1262", "count": 32, "sections": 1, "gpa": 3.359375 } ] }, { "uid": "instructor_bad0c690e1779d556680846f", "name": "JOOWON LEE", "count": 32, "terms": [ { "term": "1262", "count": 32, "sections": 1, "gpa": 3.359375 } ] }, { "uid": "instructor_1f60e25246f6b2efbbdf667b", "name": "XINYAN WANG", "count": 30, "terms": [ { "term": "1252", "count": 30, "sections": 1, "gpa": 3.7 } ] }, { "uid": "instructor_f509ff4fde897514c5b44d7e", "name": "SUSAN GLENN", "count": 19, "terms": [ { "term": "1242", "count": 19, "sections": 1, "gpa": 3.6052631578947367 } ] } ] ``` ## following | code | title | | -------------------- | -------------------------------------- | | ECON/GENBUS/STAT 775 | BAYESIAN STATISTICS | | STAT 441 | ADVANCED SPORTS ANALYTICS | | STAT 612 | STATISTICAL INFERENCE FOR DATA SCIENCE | | STAT 780 | INTRODUCTION TO QUANTUM DATA SCIENCE |