# COMPSCI/STAT 471: Introduction to Computational Statistics | UW–Madison [View on UW Courses](https://uwcourses.com/courses/COMPSCI_STAT_471) ## 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 a970da535c4759dc46addd2313af7a204ae2c1f51cdbf95b6791b0126854bdda ### course id COMPSCI/STAT 471 ### course uid course\_8f1b73751758944263fbb9e1 ### catalog version id 1e1b5b22e7b1ed8e0943adf7b1452cd75de352256453a85371d3b36d98e72f52 ### course number 471 ### subjects * COMPSCI * STAT ### title INTRODUCTION TO COMPUTATIONAL STATISTICS ### description Classical statistical procedures arise where closed-form mathematical expressions are available for various inference summaries (e.g. linear regression; analysis of variance). A major emphasis of modern statistics is the development of inference principles in cases where both more complex data structures are involved and where more elaborate computations are required. Topics from numerical linear algebra, optimization, Monte Carlo (including Markov chain Monte Carlo), and graph theory are developed, especially as they relate to statistical inference (e.g., bootstrapping, permutation, Bayesian inference, EM algorithm, multivariate analysis). ### requirements text STAT/​MATH  310and (STAT 333or340), graduate/professional standing, or declared in Statistics VISP ### credit offering ids None recorded. ### llm job id enrich-f516c4d3e82cfe326b4f5f54 ### llm output id 5fc79bb1d0418767ab9d5ce3529d7dce3f09e0683ccaad4ff3a47a937e78416e ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Introduction to computational statistics focusing on inference principles for complex data structures using numerical methods like Monte Carlo and optimization. ### llm topics * Numerical linear algebra, optimization, Monte Carlo, and graph theory * Bootstrapping, permutation, Bayesian inference, EM algorithm, and multivariate analysis ### llm skills * Developing inference principles for complex data and computations * Applying numerical linear algebra, optimization, Monte Carlo, and graph theory to statistical inference * Implementing bootstrapping, permutation tests, Bayesian inference, EM algorithm, and multivariate analysis ### llm assumed background * Probability, mathematical statistics, linear regression, and R programming * Prerequisite courses in statistics and mathematics ### llm search phrases * computational statistics * Monte Carlo methods * Markov chain Monte Carlo * EM algorithm * Bayesian inference * numerical linear algebra * optimization statistics ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash b8b4c9d8a803e6f9ca0f9d9522c352188b11a5eb96a1cf499a3678e7b302aaa4 #### course id COMPSCI/STAT 471 #### current instructors None recorded. #### difficulty workload Historical reviews of Fangfang Wang: Homework is described as really long, vague, and huge parts of the grade, requiring significant self-study due to rushed lectures. ```json { "citations": [ { "instructor_name": "Fangfang Wang", "review_date": "2018-04-10 17:13:08 +0000 UTC", "review_id": "61dd7bdd22076587f2494e69", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTI5Nzc3MzAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" }, { "instructor_name": "Fangfang Wang", "review_date": "2018-05-07 18:13:41 +0000 UTC", "review_id": "c0a3d4ff846c9b0a2c5fa86f", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTMwMDE1MDAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" } ] } ``` #### errors None recorded. #### historical context Historical reviews of Fangfang Wang: Fangfang Wang's course features long, vague programming assignments that heavily impact grades, with no traditional exams but including quizzes and a final project. Lectures are criticized for covering excessive content too quickly, leading students to self-teach relevant material. ```json { "citations": [ { "instructor_name": "Fangfang Wang", "review_date": "2018-04-10 17:13:08 +0000 UTC", "review_id": "61dd7bdd22076587f2494e69", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTI5Nzc3MzAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" }, { "instructor_name": "Fangfang Wang", "review_date": "2018-05-07 18:13:41 +0000 UTC", "review_id": "c0a3d4ff846c9b0a2c5fa86f", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTMwMDE1MDAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" } ] } ``` #### offered false #### profile hash e59ddc7389015d0035b68cd195c939d475bf72b959b29cf12eab59b454ccaef1 #### quick take Historical reviews of Fangfang Wang: The course is heavily CS-focused with no exams, relying instead on long programming assignments, quizzes, and a final project. ```json { "citations": [ { "instructor_name": "Fangfang Wang", "review_date": "2018-04-10 17:13:08 +0000 UTC", "review_id": "61dd7bdd22076587f2494e69", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTI5Nzc3MzAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" }, { "instructor_name": "Fangfang Wang", "review_date": "2018-05-07 18:13:41 +0000 UTC", "review_id": "c0a3d4ff846c9b0a2c5fa86f", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTMwMDE1MDAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" } ] } ``` Recent recorded grades — Spring 2020: 3.84 GPA, 89.7% A/AB (n=29 letter grades); Fall 2020: 2.50 GPA, 31.6% A/AB (n=19 letter grades); Fall 2025: 3.74 GPA, 94.7% A/AB (n=19 letter grades). ```json { "citations": [ { "course_id": "COMPSCI/STAT 471", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1204", "type": "grade" }, { "course_id": "COMPSCI/STAT 471", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1212", "type": "grade" }, { "course_id": "COMPSCI/STAT 471", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1262", "type": "grade" } ] } ``` #### student experience Historical reviews of Fangfang Wang: Lectures covered too much content in too little time, leading some students to tune out and teach themselves relevant material. ```json { "citations": [ { "instructor_name": "Fangfang Wang", "review_date": "2018-05-07 18:13:41 +0000 UTC", "review_id": "c0a3d4ff846c9b0a2c5fa86f", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:2346125", "source_review_id": "UmF0aW5nLTMwMDE1MDAx", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "type": "review" } ] } ``` #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history FANGFANG WANG is recorded teaching in Spring 2018, Spring 2019. Recorded history may be incomplete and does not establish a future schedule. ```json { "citations": [ { "course_id": "COMPSCI/STAT 471", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "source_record": { "entity_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1184", "type": "grade" }, { "course_id": "COMPSCI/STAT 471", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "source_record": { "entity_id": "5e4fc331-6b9f-3607-8410-7d1e399c57eb", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1194", "type": "grade" } ] } ``` #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [ "n1", "n6", "n7" ], "condition": null, "course": null, "evidence": "STAT/MATH 310and (STAT 333or340), graduate/professional standing, or declared in Statistics VISP", "id": "n0", "kind": "any" }, { "children": [ "n2", "n3" ], "condition": null, "course": null, "evidence": "STAT/MATH 310and (STAT 333or340)", "id": "n1", "kind": "all" }, { "children": [], "condition": null, "course": { "course_number": 310, "minimum_grade": null, "subjects": [ "MATH", "STAT" ], "timing": "prior" }, "evidence": "STAT/MATH 310", "id": "n2", "kind": "course" }, { "children": [ "n4", "n5" ], "condition": null, "course": null, "evidence": "(STAT 333or340)", "id": "n3", "kind": "any" }, { "children": [], "condition": null, "course": { "course_number": 333, "minimum_grade": null, "subjects": [ "STAT" ], "timing": "prior" }, "evidence": "STAT 333", "id": "n4", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 340, "minimum_grade": null, "subjects": [ "STAT" ], "timing": "prior" }, "evidence": "340", "id": "n5", "kind": "course" }, { "children": [], "condition": "graduate/professional standing", "course": null, "evidence": "graduate/professional standing", "id": "n6", "kind": "condition" }, { "children": [], "condition": "declared in Statistics VISP", "course": null, "evidence": "declared in Statistics VISP", "id": "n7", "kind": "condition" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_3ada7044de07e16f686058c1", "source": "madgrades", "source_instructor_id": "2602050", "identity_basis": "source_id", "identity_status": "source_identified", "name": "DOUGLAS BATES", "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": 16, "quality": 2.75, "difficulty": 3.12, "quality_count": 16, "difficulty_count": 16, "profile_id": "rmp:383979", "source_url": "https://www.ratemyprofessors.com/professor/383979", "match_basis": "exact_name", "observed_at": "2026-09-07 16:43:13.901690+00:00", "courses": {}, "bayesian_quality": 3.2553809908592197, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.145, "graded": 567, "counts": [ 132, 125, 169, 93, 37, 4, 7 ], "sections": 15 }, "instructor_url": "/instructors/DOUGLAS_BATES" }, { "instructor_uid": "instructor_f371fd2a28dcb497f02e3fc5", "source": "madgrades", "source_instructor_id": "2601916", "identity_basis": "source_id", "identity_status": "source_identified", "name": "MICHAEL NEWTON", "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": 3.776, "graded": 441, "counts": [ 285, 120, 31, 4, 1, 0, 0 ], "sections": 67 }, "instructor_url": "/instructors/MICHAEL_NEWTON--instructor_f371fd2a28dcb497f02e3fc5" }, { "instructor_uid": "instructor_1dadda4e80ccda3b00d60248", "source": "madgrades", "source_instructor_id": "3783301", "identity_basis": "source_id", "identity_status": "source_identified", "name": "FANGFANG 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", "ratings": { "review_count": 2, "quality": 3, "difficulty": 4.5, "quality_count": 2, "difficulty_count": 2, "profile_id": "rmp:2346125", "source_url": "https://www.ratemyprofessors.com/professor/2346125", "match_basis": "exact_name", "observed_at": "2026-09-07 16:39:23.508204+00:00", "courses": { "course_8f1b73751758944263fbb9e1": { "review_count": 2, "quality": 3, "difficulty": 4.5, "quality_count": 2, "difficulty_count": 2 } }, "bayesian_quality": 3.5997143486787233, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.565, "graded": 201, "counts": [ 84, 76, 30, 7, 3, 1, 0 ], "sections": 6 }, "instructor_url": "/instructors/FANGFANG_WANG" }, { "instructor_uid": "instructor_42031d9bf0b0b0f242d30ce4", "source": "madgrades", "source_instructor_id": "5170492", "identity_basis": "source_id", "identity_status": "source_identified", "name": "YING 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": 3.828, "graded": 29, "counts": [ 22, 5, 1, 1, 0, 0, 0 ], "sections": 1 }, "instructor_url": "/instructors/YING_ZHANG" }, { "instructor_uid": "instructor_be349776153ff3c511c3de89", "source": "madgrades", "source_instructor_id": "5905014", "identity_basis": "source_id", "identity_status": "source_identified", "name": "SHAN LU", "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": 3.633, "graded": 398, "counts": [ 222, 98, 52, 18, 6, 2, 0 ], "sections": 8 }, "instructor_url": "/instructors/SHAN_LU" }, { "instructor_uid": "instructor_8a33af6351d9ba19cb0b308a", "source": "madgrades", "source_instructor_id": "6253085", "identity_basis": "source_id", "identity_status": "source_identified", "name": "HENRY MENDOZA RIVERA", "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": 3.245, "graded": 355, "counts": [ 107, 72, 108, 42, 19, 5, 2 ], "sections": 5 }, "instructor_url": "/instructors/HENRY_MENDOZA_RIVERA--instructor_8a33af6351d9ba19cb0b308a" }, { "instructor_uid": "instructor_ebccd50a43f3c8007d148ac1", "source": "madgrades", "source_instructor_id": "6090509", "identity_basis": "source_id", "identity_status": "source_identified", "name": "ELINA CHOI", "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": 3.643, "graded": 210, "counts": [ 143, 29, 15, 9, 10, 4, 0 ], "sections": 7 }, "instructor_url": "/instructors/ELINA_CHOI--instructor_ebccd50a43f3c8007d148ac1" }, { "instructor_uid": "instructor_1172e88b5ae6ba42f3343950", "source": "madgrades", "source_instructor_id": "5694563", "identity_basis": "source_id", "identity_status": "source_identified", "name": "BEHZAD AALIPUR HAFSHEJANI", "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": 3.617, "graded": 551, "counts": [ 350, 88, 57, 28, 19, 6, 3 ], "sections": 12 }, "instructor_url": "/instructors/BEHZAD_AALIPUR_HAFSHEJANI" }, { "instructor_uid": "instructor_8bbaf0e94ced306bd3b3c10a", "source": "madgrades", "source_instructor_id": "6176657", "identity_basis": "source_id", "identity_status": "source_identified", "name": "NICOLAS GARCIA TRILLOS", "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": 3.599, "graded": 478, "counts": [ 297, 75, 56, 26, 16, 5, 3 ], "sections": 36 }, "instructor_url": "/instructors/NICOLAS_GARCIA_TRILLOS--instructor_8bbaf0e94ced306bd3b3c10a" }, { "instructor_uid": "instructor_d9e618b3ccbe558e29172b04", "source": "madgrades", "source_instructor_id": "6188409", "identity_basis": "source_id", "identity_status": "source_identified", "name": "CAMERON JONES", "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": 15, "quality": 4.67, "difficulty": 2.67, "quality_count": 15, "difficulty_count": 15, "profile_id": "rmp:2974757", "source_url": "https://www.ratemyprofessors.com/professor/2974757", "match_basis": "exact_name", "observed_at": "2026-09-07 16:50:06.132806+00:00", "courses": { "course_03f8aaae24bac7658b319ca8": { "review_count": 14, "quality": 4.64, "difficulty": 2.71, "quality_count": 14, "difficulty_count": 14 }, "course_f57701752c565e5b0c704a1d": { "review_count": 1, "quality": 5, "difficulty": 2, "quality_count": 1, "difficulty_count": 1 } }, "bayesian_quality": 4.092677590598055, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.521, "graded": 354, "counts": [ 166, 84, 73, 19, 11, 0, 1 ], "sections": 7 }, "instructor_url": "/instructors/CAMERON_JONES--instructor_d9e618b3ccbe558e29172b04" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1112", "term_name": "Fall 2010", "instructors": [ "DOUGLAS BATES" ], "a": 9, "ab": 2, "b": 8, "bc": 1, "c": 2, "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": 22, "source_aliases": [ "COMPSCI/STAT 471" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1144", "term_name": "Spring 2014", "instructors": [ "Michael Newton" ], "a": 10, "ab": 11, "b": 4, "bc": 1, "c": 0, "d": 0, "f": 0, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 27, "source_aliases": [ "COMPSCI/STAT 471" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1184", "term_name": "Spring 2018", "instructors": [ "FANGFANG WANG", "Jing Zhang" ], "a": 22, "ab": 5, "b": 1, "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": 29, "source_aliases": [ "COMPSCI/STAT 471" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1194", "term_name": "Spring 2019", "instructors": [ "FANGFANG WANG", "SHAN LU" ], "a": 12, "ab": 20, "b": 16, "bc": 6, "c": 2, "d": 1, "f": 0, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 58, "source_aliases": [ "COMPSCI/STAT 471" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1204", "term_name": "Spring 2020", "instructors": [ "ELINA CHOI", "HENRY MENDOZA RIVERA" ], "a": 24, "ab": 2, "b": 2, "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": 29, "source_aliases": [ "COMPSCI/STAT 471" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1212", "term_name": "Fall 2020", "instructors": [ "BEHZAD AALIPUR HAFSHEJANI", "Nicolas Garcia Trillos" ], "a": 5, "ab": 1, "b": 2, "bc": 4, "c": 3, "d": 2, "f": 2, "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": [ "COMPSCI/STAT 471" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI/STAT 471", "course_uid": "course_8f1b73751758944263fbb9e1", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "CAMERON JONES", "Michael Newton" ], "a": 12, "ab": 6, "b": 0, "bc": 0, "c": 1, "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": [ "COMPSCI/STAT 471" ] } ] ``` ### statistics #### gpa 3.45 #### graded 201 #### counts * 94 * 47 * 33 * 14 * 8 * 3 * 2 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_8f1b73751758944263fbb9e1-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_8f1b73751758944263fbb9e1-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_8f1b73751758944263fbb9e1-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_8f1b73751758944263fbb9e1-0.json) #### reviews * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course\_8f1b73751758944263fbb9e1-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course_8f1b73751758944263fbb9e1-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_8f1b73751758944263fbb9e1-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_8f1b73751758944263fbb9e1-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.4502487562189055 #### count 201 #### university ##### size 3558 ##### gpa Percentile 24 ##### count Percentile 73 ##### median Count 97.5 ##### 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 | 296 | false | | 3.2–3.6 | 1039 | true | | 3.6–4.0 | 2217 | false | #### departments ```json [ { "subject": "COMPSCI", "comparison": { "size": 89, "gpaPercentile": 33, "countPercentile": 58, "medianCount": 176, "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": 31, "current": true }, { "range": "3.6–4.0", "count": 46, "current": false } ] } }, { "subject": "STAT", "comparison": { "size": 60, "gpaPercentile": 29, "countPercentile": 63, "medianCount": 159.5, "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": 26, "current": true }, { "range": "3.6–4.0", "count": 27, "current": false } ] } } ] ``` ### terms #### 1112 ##### term 1112 ##### gpa 3.340909090909091 ##### count 22 ##### departments | subject | comparison | | ------- | ---------- | | COMPSCI | | | STAT | | #### 1144 ##### term 1144 ##### gpa 3.576923076923077 ##### count 26 ##### departments | subject | comparison | | ------- | ---------- | | COMPSCI | | | STAT | | #### 1184 ##### term 1184 ##### gpa 3.8275862068965516 ##### count 29 ##### departments | subject | comparison | | ------- | ---------- | | COMPSCI | | | STAT | | #### 1194 ##### term 1194 ##### gpa 3.263157894736842 ##### count 57 ##### university ###### size 1040 ###### gpa Percentile 28 ###### count Percentile 41 ###### median Count 67 ###### 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 | 23 | false | | 2.8–3.2 | 220 | false | | 3.2–3.6 | 342 | true | | 3.6–4.0 | 454 | false | ##### departments ```json [ { "subject": "COMPSCI", "comparison": { "size": 38, "gpaPercentile": 46, "countPercentile": 22, "medianCount": 92, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 15, "current": false }, { "range": "3.2–3.6", "count": 11, "current": true }, { "range": "3.6–4.0", "count": 11, "current": false } ] } }, { "subject": "STAT", "comparison": { "size": 23, "gpaPercentile": 32, "countPercentile": 18, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 6, "current": false }, { "range": "3.2–3.6", "count": 9, "current": true }, { "range": "3.6–4.0", "count": 7, "current": false } ] } } ] ``` #### 1204 ##### term 1204 ##### gpa 3.8448275862068964 ##### count 29 ##### departments | subject | comparison | | ------- | ---------- | | COMPSCI | | | STAT | | #### 1212 ##### term 1212 ##### gpa 2.5 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | COMPSCI | | | STAT | | #### 1262 ##### term 1262 ##### gpa 3.736842105263158 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | COMPSCI | | | STAT | | ### benchmarks #### all ##### school ###### size 3558 ###### gpa 3.652581003644356 ###### top Share 81.36702404783058 ###### count 97.5 ##### COMPSCI ###### size 89 ###### gpa 3.5752705370251325 ###### top Share 77.95621898606137 ###### count 176 ##### STAT ###### size 60 ###### gpa 3.566930608311878 ###### top Share 76.69818284873139 ###### count 159.5 #### terms ##### 1112 ###### school ###### size 805 ###### gpa 3.3737773184830386 ###### top Share 63.91039244872789 ###### count 66 ###### COMPSCI ###### size 27 ###### gpa 3.2907039795974518 ###### top Share 61.485726181424475 ###### count 41 ###### STAT ###### size 14 ###### gpa 3.242406375705586 ###### top Share 57.34276482904032 ###### count 68.5 ##### 1144 ###### school ###### size 878 ###### gpa 3.4092443327803017 ###### top Share 65.95978235963416 ###### count 64 ###### COMPSCI ###### size 26 ###### gpa 3.263233863996996 ###### top Share 58.711877593449 ###### count 70.5 ###### STAT ###### size 14 ###### gpa 3.0970281238579784 ###### top Share 51.741770775379614 ###### count 74.5 ##### 1184 ###### school ###### size 1010 ###### gpa 3.47925290354517 ###### top Share 70.86270937115944 ###### count 68 ###### COMPSCI ###### size 31 ###### gpa 3.329310414227759 ###### top Share 61.243618316797935 ###### count 126 ###### STAT ###### size 25 ###### gpa 3.460210558661105 ###### top Share 70.42953406269322 ###### count 70 ##### 1194 ###### school ###### size 1040 ###### gpa 3.4906527436719808 ###### top Share 71.74728845515045 ###### count 67 ###### COMPSCI ###### size 38 ###### gpa 3.3433846242383978 ###### top Share 63.409438479169644 ###### count 92 ###### STAT ###### size 23 ###### gpa 3.3818127863152334 ###### top Share 66.9295898310719 ###### count 79 ##### 1204 ###### school ###### size 1012 ###### gpa 3.703747431523761 ###### top Share 84.59811226304916 ###### count 65.5 ###### COMPSCI ###### size 42 ###### gpa 3.6240893265324647 ###### top Share 79.98991493017058 ###### count 90.5 ###### STAT ###### size 27 ###### gpa 3.6537351147420534 ###### top Share 80.87794592686507 ###### count 56 ##### 1212 ###### school ###### size 1111 ###### gpa 3.5856416028074487 ###### top Share 77.58926748660845 ###### count 68 ###### COMPSCI ###### size 42 ###### gpa 3.450396020252364 ###### top Share 69.90950224603134 ###### count 96 ###### STAT ###### size 25 ###### gpa 3.4555542540486095 ###### top Share 69.54152339778892 ###### count 100 ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ###### COMPSCI ###### size 53 ###### gpa 3.533845597774486 ###### top Share 75.31824598445624 ###### count 82 ###### STAT ###### size 29 ###### gpa 3.415022952129974 ###### top Share 64.95385808199006 ###### count 81 ## instructor Trends ```json [ { "uid": "instructor_1dadda4e80ccda3b00d60248", "name": "FANGFANG WANG", "count": 86, "terms": [ { "term": "1184", "count": 29, "sections": 1, "gpa": 3.8275862068965516 }, { "term": "1194", "count": 57, "sections": 1, "gpa": 3.263157894736842 } ] }, { "uid": "instructor_be349776153ff3c511c3de89", "name": "SHAN LU", "count": 57, "terms": [ { "term": "1194", "count": 57, "sections": 1, "gpa": 3.263157894736842 } ] }, { "uid": "instructor_f371fd2a28dcb497f02e3fc5", "name": "MICHAEL NEWTON", "count": 45, "terms": [ { "term": "1144", "count": 26, "sections": 1, "gpa": 3.576923076923077 }, { "term": "1262", "count": 19, "sections": 2, "gpa": 3.736842105263158 } ] }, { "uid": "instructor_42031d9bf0b0b0f242d30ce4", "name": "YING ZHANG", "count": 29, "terms": [ { "term": "1184", "count": 29, "sections": 1, "gpa": 3.8275862068965516 } ] }, { "uid": "instructor_8a33af6351d9ba19cb0b308a", "name": "HENRY MENDOZA RIVERA", "count": 29, "terms": [ { "term": "1204", "count": 29, "sections": 2, "gpa": 3.8448275862068964 } ] }, { "uid": "instructor_ebccd50a43f3c8007d148ac1", "name": "ELINA CHOI", "count": 29, "terms": [ { "term": "1204", "count": 29, "sections": 2, "gpa": 3.8448275862068964 } ] }, { "uid": "instructor_3ada7044de07e16f686058c1", "name": "DOUGLAS BATES", "count": 22, "terms": [ { "term": "1112", "count": 22, "sections": 1, "gpa": 3.340909090909091 } ] }, { "uid": "instructor_1172e88b5ae6ba42f3343950", "name": "BEHZAD AALIPUR HAFSHEJANI", "count": 19, "terms": [ { "term": "1212", "count": 19, "sections": 1, "gpa": 2.5 } ] }, { "uid": "instructor_8bbaf0e94ced306bd3b3c10a", "name": "NICOLAS GARCIA TRILLOS", "count": 19, "terms": [ { "term": "1212", "count": 19, "sections": 1, "gpa": 2.5 } ] }, { "uid": "instructor_d9e618b3ccbe558e29172b04", "name": "CAMERON JONES", "count": 19, "terms": [ { "term": "1262", "count": 19, "sections": 1, "gpa": 3.736842105263158 } ] } ] ``` ## following None recorded.