# STAT 606: Computing in Data Science and Statistics | UW–Madison [View on UW Courses](https://uwcourses.com/courses/STAT_606) ## 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 bc86576d1dc7c886a084974b8cd95f4b3a54486a1de9add4afc42df0d72c8595 ### course id STAT 606 ### course uid course\_cdc21e46ef21cd0cb632076c ### catalog version id fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6 ### course number 606 ### subjects * STAT ### title COMPUTING IN DATA SCIENCE AND STATISTICS ### description A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework. ### requirements text Declared in Statistics MS or Statistics VISP (undergraduate) ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 26f85920be625eb176853b790121f130466ae1718f5e23a646856ae5a4562cc0 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary STAT 606: COMPUTING IN DATA SCIENCE AND STATISTICS surveys popular data science tools, covering Python, machine learning, data visualization, web scraping, and MapReduce. ### llm topics * Python programming, object-oriented and functional programming * Scientific computing platforms in Python * Basic machine learning models * Data visualization * Web scraping and APIs * Distributed computing and MapReduce ### llm skills * Python programming * Training basic machine learning models * Data visualization * Web data collection via scraping and APIs * Distributed computing with MapReduce ### llm assumed background None recorded. ### llm search phrases * Python programming for data science * machine learning tools Python * data scraping APIs statistics * MapReduce distributed computing ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 98909490be862dca5837e75975e98c589610cae42b0d0d4c278afb4572493862 #### course id STAT 606 #### current instructors None recorded. #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered false #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Spring 2024: 3.38 GPA, 78.9% A/AB (n=38 letter grades); Spring 2025: 3.35 GPA, 70.6% A/AB (n=17 letter grades); Spring 2026: 3.40 GPA, 75.0% A/AB (n=24 letter grades). ```json { "citations": [ { "course_id": "STAT 606", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "ef351c24-0050-36f4-adb1-5384077963b6", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1244", "type": "grade" }, { "course_id": "STAT 606", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "ef351c24-0050-36f4-adb1-5384077963b6", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "STAT 606", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "ef351c24-0050-36f4-adb1-5384077963b6", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [], "condition": "Declared in Statistics MS or Statistics VISP (undergraduate)", "course": null, "evidence": "Declared in Statistics MS or Statistics VISP (undergraduate)", "id": "n0", "kind": "condition" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_6cad7c24de51315540604b40", "source": "madgrades", "source_instructor_id": "6315817", "identity_basis": "source_id", "identity_status": "source_identified", "name": "KEITH LEVIN", "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": 52, "quality": 1.9, "difficulty": 4.04, "quality_count": 52, "difficulty_count": 52, "profile_id": "rmp:2726941", "source_url": "https://www.ratemyprofessors.com/professor/2726941", "match_basis": "exact_name", "observed_at": "2026-09-07 16:26:32.891187+00:00", "courses": { "course_e593b86c370a0c671af794df": { "review_count": 12, "quality": 2.5, "difficulty": 4.33, "quality_count": 12, "difficulty_count": 12 }, "course_d2d0c15e32bd1631a6074548": { "review_count": 39, "quality": 1.74, "difficulty": 3.92, "quality_count": 39, "difficulty_count": 39 } }, "bayesian_quality": 2.388801606540721, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.265, "graded": 719, "counts": [ 219, 204, 162, 69, 42, 15, 8 ], "sections": 25 }, "instructor_url": "/instructors/KEITH_LEVIN--instructor_6cad7c24de51315540604b40" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 606", "course_uid": "course_cdc21e46ef21cd0cb632076c", "term_id": "1234", "term_name": "Spring 2023", "instructors": [ "Keith Levin" ], "a": 21, "ab": 16, "b": 4, "bc": 2, "c": 3, "d": 1, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 47, "source_aliases": [ "STAT 606" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 606", "course_uid": "course_cdc21e46ef21cd0cb632076c", "term_id": "1244", "term_name": "Spring 2024", "instructors": [ "Keith Levin" ], "a": 14, "ab": 16, "b": 2, "bc": 1, "c": 4, "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": 38, "source_aliases": [ "STAT 606" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 606", "course_uid": "course_cdc21e46ef21cd0cb632076c", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Keith Levin" ], "a": 6, "ab": 6, "b": 3, "bc": 0, "c": 1, "d": 1, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 17, "source_aliases": [ "STAT 606" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 606", "course_uid": "course_cdc21e46ef21cd0cb632076c", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Keith Levin" ], "a": 11, "ab": 7, "b": 4, "bc": 0, "c": 0, "d": 1, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 24, "source_aliases": [ "STAT 606" ] } ] ``` ### statistics #### gpa 3.421 #### graded 126 #### counts * 52 * 45 * 13 * 3 * 8 * 3 * 2 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_cdc21e46ef21cd0cb632076c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_cdc21e46ef21cd0cb632076c-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_cdc21e46ef21cd0cb632076c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_cdc21e46ef21cd0cb632076c-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_cdc21e46ef21cd0cb632076c-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_cdc21e46ef21cd0cb632076c-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.4206349206349205 #### count 126 #### university ##### size 2701 ##### gpa Percentile 18 ##### count Percentile 57 ##### median Count 104 ##### 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 | 13 | false | | 2.8–3.2 | 172 | false | | 3.2–3.6 | 693 | true | | 3.6–4.0 | 1823 | false | #### departments ```json [ { "subject": "STAT", "comparison": { "size": 47, "gpaPercentile": 30, "countPercentile": 41, "medianCount": 149, "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": 8, "current": false }, { "range": "3.2–3.6", "count": 15, "current": true }, { "range": "3.6–4.0", "count": 24, "current": false } ] } } ] ``` ### terms #### 1234 ##### term 1234 ##### gpa 3.4893617021276597 ##### count 47 ##### university ###### size 1188 ###### gpa Percentile 35 ###### count Percentile 32 ###### 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 | 3 | false | | 2.4–2.8 | 17 | false | | 2.8–3.2 | 144 | false | | 3.2–3.6 | 374 | true | | 3.6–4.0 | 650 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 25, "gpaPercentile": 63, "countPercentile": 13, "medianCount": 67, "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": 5, "current": false }, { "range": "3.2–3.6", "count": 12, "current": true }, { "range": "3.6–4.0", "count": 7, "current": false } ] } } ] ``` #### 1244 ##### term 1244 ##### gpa 3.3815789473684212 ##### count 38 ##### university ###### size 1241 ###### gpa Percentile 24 ###### count Percentile 17 ###### 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 | true | | 3.6–4.0 | 706 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 29, "gpaPercentile": 54, "countPercentile": 14, "medianCount": 62, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 8, "current": false }, { "range": "3.2–3.6", "count": 13, "current": true }, { "range": "3.6–4.0", "count": 8, "current": false } ] } } ] ``` #### 1254 ##### term 1254 ##### gpa 3.3529411764705883 ##### count 17 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1264 ##### term 1264 ##### gpa 3.3958333333333335 ##### count 24 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | ### benchmarks #### all ##### school ###### size 2701 ###### gpa 3.6818072807746525 ###### top Share 83.341103666219 ###### count 104 ##### STAT ###### size 47 ###### gpa 3.5363196161219537 ###### top Share 74.31357386264372 ###### count 149 #### terms ##### 1234 ###### school ###### size 1188 ###### gpa 3.5799441677552393 ###### top Share 77.18256448537606 ###### count 65 ###### STAT ###### size 25 ###### gpa 3.396227416481204 ###### top Share 66.8429997070879 ###### count 67 ##### 1244 ###### school ###### size 1241 ###### gpa 3.5975573126929192 ###### top Share 78.29036874847135 ###### count 65 ###### STAT ###### size 29 ###### gpa 3.390846810323202 ###### top Share 64.2629736611174 ###### count 62 ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ###### STAT ###### size 28 ###### gpa 3.4752654121860003 ###### top Share 68.35873780152826 ###### count 70 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### STAT ###### size 26 ###### gpa 3.343399816676682 ###### top Share 63.23102425546245 ###### count 66 ## instructor Trends ```json [ { "uid": "instructor_6cad7c24de51315540604b40", "name": "KEITH LEVIN", "count": 126, "terms": [ { "term": "1234", "count": 47, "sections": 1, "gpa": 3.4893617021276597 }, { "term": "1244", "count": 38, "sections": 1, "gpa": 3.3815789473684212 }, { "term": "1254", "count": 17, "sections": 1, "gpa": 3.3529411764705883 }, { "term": "1264", "count": 24, "sections": 1, "gpa": 3.3958333333333335 } ] } ] ``` ## following None recorded.