[{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"3aea9febefb1dfb8193056d627fc252d446abf146c55f023f9193732458d082b","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\",\"n8\"],\"condition\":null,\"course\":null,\"evidence\":\"GEN BUS 307,317704, 705, 881,ECON 400,410,STAT/MATH 310,STAT 333,340, or declared in the Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":307,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 307\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":704,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"704\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":705,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"705\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":881,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"881\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":400,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 400\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"410\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n8\",\"kind\":\"course\"}],\"notes\":[\"The string '317704' is ambiguous; parsed as separate courses 317 and 704. Course 704 is not in linked_courses.\",\"Course 705 and 881 are not in linked_courses.\",\"Course 410 is not in linked_courses.\",\"Course 333 and 340 are missing from the parsed nodes despite being in the requirements text.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","error":"Course requirement is absent from the source links","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"3aea9febefb1dfb8193056d627fc252d446abf146c55f023f9193732458d082b","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"GENBUS 307\",\"field\":\"description\",\"quote\":\"Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Foundations in business analytics, statistical inference, regression, and hypothesis testing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"},{\"course_id\":\"ECON 400\",\"field\":\"description\",\"quote\":\"An introduction to applied econometrics - the statistical methods economists use to evaluate empirical relationships\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified\"}],\"text\":\"Programming with R, calculus-based statistics, and applied econometric methods.\"}],\"search_phrases\":[\"predictive modeling business\",\"linear regression classification\",\"bias-variance tradeoff\",\"statistical learning business analytics\",\"GENBUS 656 prerequisites\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Leads to development of linear regression and classification models, and discussion of building models for prediction.\"}],\"text\":\"Developing linear regression and classification models for prediction.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Applying feature selection, regularization, and managing the bias-variance tradeoff.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"title\",\"quote\":\"FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS\"},{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models\"}],\"text\":\"An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Feature selection and regularization methods.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"discussion of building models for prediction.\"}],\"text\":\"Building predictive models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"development of linear regression and classification models\"}],\"text\":\"Linear regression and classification models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"the bias-variance tradeoff\"}],\"text\":\"The bias-variance tradeoff.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-5291a20b802b9bbbe22b24cb","output_id":"3aea9febefb1dfb8193056d627fc252d446abf146c55f023f9193732458d082b","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"97551845141843c5efb0459fa870d6acc1cf44baf695782a68e0e825f72a0833","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\",\"n8\",\"n9\",\"n10\"],\"condition\":null,\"course\":null,\"evidence\":\"GEN BUS 307,317704, 705, 881,ECON 400,410,STAT/MATH 310,STAT 333,340, or declared in the Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":307,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 307\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"704\",\"course\":null,\"evidence\":\"704\",\"id\":\"n3\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"705\",\"course\":null,\"evidence\":\"705\",\"id\":\"n4\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"881\",\"course\":null,\"evidence\":\"881\",\"id\":\"n5\",\"kind\":\"condition\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":400,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 400\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"410\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n8\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n9\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n10\",\"kind\":\"course\"}],\"notes\":[\"Courses 704, 705, 881 are absent from linked_courses. They are preserved as condition nodes with status needs_review.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"97551845141843c5efb0459fa870d6acc1cf44baf695782a68e0e825f72a0833","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"GENBUS 307\",\"field\":\"description\",\"quote\":\"Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Foundations in business analytics, statistical inference, regression, and hypothesis testing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"},{\"course_id\":\"ECON 400\",\"field\":\"description\",\"quote\":\"An introduction to applied econometrics - the statistical methods economists use to evaluate empirical relationships\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified\"}],\"text\":\"Programming with R, calculus-based statistics, and applied econometric methods.\"}],\"search_phrases\":[\"predictive modeling business\",\"linear regression classification\",\"bias-variance tradeoff\",\"statistical learning business analytics\",\"GENBUS 656 prerequisites\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Leads to development of linear regression and classification models, and discussion of building models for prediction.\"}],\"text\":\"Developing linear regression and classification models for prediction.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Applying feature selection, regularization, and managing the bias-variance tradeoff.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"title\",\"quote\":\"FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS\"},{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models\"}],\"text\":\"An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Feature selection and regularization methods.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"discussion of building models for prediction.\"}],\"text\":\"Building predictive models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"development of linear regression and classification models\"}],\"text\":\"Linear regression and classification models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"the bias-variance tradeoff\"}],\"text\":\"The bias-variance tradeoff.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-5590a4969e0a630fe46a86e8","output_id":"97551845141843c5efb0459fa870d6acc1cf44baf695782a68e0e825f72a0833","section":"student_experience","status":"valid","value_json":"{\"status\":\"supported\",\"themes\":[{\"aspect\":\"teaching_clarity\",\"evidence\":[{\"comment\":\"Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.\",\"course_id\":\"GENBUS 656\",\"date\":\"2023-05-18 03:36:47 +0000 UTC\",\"difficulty_rating\":2,\"id\":\"2cecd9c0a6874638ddb19c30\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":4,\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"},{\"comment\":\"He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend. \",\"course_id\":\"GENBUS 656\",\"date\":\"2025-04-25 18:50:18 +0000 UTC\",\"difficulty_rating\":3,\"id\":\"a6e5aa340d08ae62bd2da635\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":5,\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":2,\"review_ids\":[\"2cecd9c0a6874638ddb19c30\",\"a6e5aa340d08ae62bd2da635\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2025\",\"review_year_start\":\"2023\"},\"sentiment\":\"positive\",\"summary\":\"Professor Shi is described as a great lecturer who explains difficult material easily and is very helpful and approachable.\"},{\"aspect\":\"assessment\",\"evidence\":[{\"comment\":\"He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend. \",\"course_id\":\"GENBUS 656\",\"date\":\"2025-04-25 18:50:18 +0000 UTC\",\"difficulty_rating\":3,\"id\":\"a6e5aa340d08ae62bd2da635\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":5,\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":1,\"review_ids\":[\"a6e5aa340d08ae62bd2da635\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2025\",\"review_year_start\":\"2025\"},\"sentiment\":\"positive\",\"summary\":\"Exams are covered by lecture content, and asking questions during lectures helps prepare for them.\"},{\"aspect\":\"workload\",\"evidence\":[{\"comment\":\"Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.\",\"course_id\":\"GENBUS 656\",\"date\":\"2023-05-18 03:36:47 +0000 UTC\",\"difficulty_rating\":2,\"id\":\"2cecd9c0a6874638ddb19c30\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":4,\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":1,\"review_ids\":[\"2cecd9c0a6874638ddb19c30\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2023\",\"review_year_start\":\"2023\"},\"sentiment\":\"mixed\",\"summary\":\"Lectures can be a bit boring, but the inclusion of R exercises is useful.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-789789da373eecc1ff75f626","output_id":"2482522b199b85b22028675df513d6c7a2e6575b788d949e9b4c4342cc244c99","section":"requirements","status":"invalid","value_json":null,"candidate_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\"],\"condition\":null,\"course\":null,\"evidence\":\"GEN BUS 307,317704, 705, 881,ECON 400,410,STAT/MATH 310,STAT 333,340, or declared in the Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":307,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 307\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":400,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 400\",\"id\":\"n3\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"410\",\"id\":\"n4\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n5\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n7\",\"kind\":\"course\"}],\"notes\":[\"Parsed requirements text. Excluded courses 704, 705, 881 as they are absent from source links. Evidence strings exactly match source substrings.\"],\"root\":\"n0\",\"status\":\"parsed\"}","error":"Source numeric references missing from leaf conditions: 705, 881. Preserve every alternative. References absent from linked_courses must remain verbatim condition nodes with needs_review, not be dropped.","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-789789da373eecc1ff75f626","output_id":"2482522b199b85b22028675df513d6c7a2e6575b788d949e9b4c4342cc244c99","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"GENBUS 307\",\"field\":\"description\",\"quote\":\"Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Foundations in business analytics, statistical inference, regression, and hypothesis testing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"},{\"course_id\":\"ECON 400\",\"field\":\"description\",\"quote\":\"An introduction to applied econometrics - the statistical methods economists use to evaluate empirical relationships\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified\"}],\"text\":\"Programming with R, calculus-based statistics, and applied econometric methods.\"}],\"search_phrases\":[\"predictive modeling business\",\"linear regression classification\",\"bias-variance tradeoff\",\"statistical learning business analytics\",\"GENBUS 656 prerequisites\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Leads to development of linear regression and classification models, and discussion of building models for prediction.\"}],\"text\":\"Developing linear regression and classification models for prediction.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Applying feature selection, regularization, and managing the bias-variance tradeoff.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"title\",\"quote\":\"FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS\"},{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models\"}],\"text\":\"An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Feature selection and regularization methods.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"discussion of building models for prediction.\"}],\"text\":\"Building predictive models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"development of linear regression and classification models\"}],\"text\":\"Linear regression and classification models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"the bias-variance tradeoff\"}],\"text\":\"The bias-variance tradeoff.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-789789da373eecc1ff75f626","output_id":"2482522b199b85b22028675df513d6c7a2e6575b788d949e9b4c4342cc244c99","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"adbd6fee81bcd0d89f4e4f7169d2425e48d4b7bea2ea111926071900a593b465","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\",\"n8\",\"n9\",\"n10\"],\"condition\":null,\"course\":null,\"evidence\":\"GEN BUS 307,317704, 705, 881,ECON 400,410,STAT/MATH 310,STAT 333,340, or declared in the Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":307,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 307\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"704\",\"course\":null,\"evidence\":\"704\",\"id\":\"n3\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"705\",\"course\":null,\"evidence\":\"705\",\"id\":\"n4\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"881\",\"course\":null,\"evidence\":\"881\",\"id\":\"n5\",\"kind\":\"condition\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":400,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 400\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"410\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n8\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n9\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n10\",\"kind\":\"course\"}],\"notes\":[\"Courses 704, 705, 881 are absent from linked_courses. They are preserved as condition nodes with status needs_review.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"adbd6fee81bcd0d89f4e4f7169d2425e48d4b7bea2ea111926071900a593b465","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"GENBUS 307\",\"field\":\"description\",\"quote\":\"Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Foundations in business analytics, statistical inference, regression, and hypothesis testing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"},{\"course_id\":\"ECON 400\",\"field\":\"description\",\"quote\":\"An introduction to applied econometrics - the statistical methods economists use to evaluate empirical relationships\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified\"}],\"text\":\"Programming with R, calculus-based statistics, and applied econometric methods.\"}],\"search_phrases\":[\"predictive modeling business\",\"linear regression classification\",\"bias-variance tradeoff\",\"statistical learning business analytics\",\"GENBUS 656 prerequisites\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Leads to development of linear regression and classification models, and discussion of building models for prediction.\"}],\"text\":\"Developing linear regression and classification models for prediction.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Applying feature selection, regularization, and managing the bias-variance tradeoff.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"title\",\"quote\":\"FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS\"},{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models\"}],\"text\":\"An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Feature selection and regularization methods.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"discussion of building models for prediction.\"}],\"text\":\"Building predictive models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"development of linear regression and classification models\"}],\"text\":\"Linear regression and classification models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"the bias-variance tradeoff\"}],\"text\":\"The bias-variance tradeoff.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"adbd6fee81bcd0d89f4e4f7169d2425e48d4b7bea2ea111926071900a593b465","section":"student_experience","status":"valid","value_json":"{\"status\":\"supported\",\"themes\":[{\"aspect\":\"teaching_clarity\",\"evidence\":[{\"comment\":\"Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.\",\"course_id\":\"GENBUS 656\",\"date\":\"2023-05-18 03:36:47 +0000 UTC\",\"difficulty_rating\":2,\"id\":\"2cecd9c0a6874638ddb19c30\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":4,\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"},{\"comment\":\"He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend. \",\"course_id\":\"GENBUS 656\",\"date\":\"2025-04-25 18:50:18 +0000 UTC\",\"difficulty_rating\":3,\"id\":\"a6e5aa340d08ae62bd2da635\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":5,\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":2,\"review_ids\":[\"2cecd9c0a6874638ddb19c30\",\"a6e5aa340d08ae62bd2da635\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2025\",\"review_year_start\":\"2023\"},\"sentiment\":\"positive\",\"summary\":\"Professor Shi is described as a great lecturer who explains difficult material easily and is very helpful and approachable.\"},{\"aspect\":\"assessment\",\"evidence\":[{\"comment\":\"He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend. \",\"course_id\":\"GENBUS 656\",\"date\":\"2025-04-25 18:50:18 +0000 UTC\",\"difficulty_rating\":3,\"id\":\"a6e5aa340d08ae62bd2da635\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":5,\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":1,\"review_ids\":[\"a6e5aa340d08ae62bd2da635\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2025\",\"review_year_start\":\"2025\"},\"sentiment\":\"positive\",\"summary\":\"Exams are covered by lecture content, and asking questions during lectures helps prepare for them.\"},{\"aspect\":\"workload\",\"evidence\":[{\"comment\":\"Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.\",\"course_id\":\"GENBUS 656\",\"date\":\"2023-05-18 03:36:47 +0000 UTC\",\"difficulty_rating\":2,\"id\":\"2cecd9c0a6874638ddb19c30\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":4,\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":1,\"review_ids\":[\"2cecd9c0a6874638ddb19c30\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2023\",\"review_year_start\":\"2023\"},\"sentiment\":\"mixed\",\"summary\":\"Lectures can be a bit boring, but the inclusion of R exercises is useful.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-8b774950c2b6adfdc46d1b82","output_id":"adbd6fee81bcd0d89f4e4f7169d2425e48d4b7bea2ea111926071900a593b465","section":"student_summary","status":"invalid","value_json":"{\"context_hash\":\"01afedf2ebfa142b4c18588cc68a1fc3e6fa888d0280944d8e13bc3bf9f6cac6\",\"course_id\":\"GENBUS 656\",\"current_instructors\":[{\"instructor_uid\":\"instructor_7ae8046db6b009671007abdc\",\"message\":\"No course-specific reviews available\",\"name\":\"Kyohei Okumura\",\"review_status\":\"no_course_reviews\",\"rmp_instructor_id\":null,\"summary\":[{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":10,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":11,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Fall 2025: 3.40 GPA, 58.2% A/AB (n=67 letter grades). Includes jointly taught sections.\"}]}],\"difficulty_workload\":[],\"errors\":[{\"error\":\"ModelAPIError: Connection error.\",\"instructor_uid\":null,\"mode\":\"history\"},{\"error\":\"ModelAPIError: Connection error.\",\"instructor_uid\":null,\"mode\":\"overview\"}],\"historical_context\":[],\"message\":null,\"offered\":true,\"profile_hash\":\"5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02\",\"quick_take\":[{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2025: 3.53 GPA, 73.7% A/AB (n=38 letter grades); Fall 2025: 3.72 GPA, 80.5% A/AB (n=149 letter grades); Spring 2026: 3.60 GPA, 90.0% A/AB (n=30 letter grades).\"}],\"student_experience\":[],\"task_hash\":\"74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68\",\"teaching_history\":[{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":10,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":11,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"}],\"text\":\"KYOHEI OKUMURA is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.\"},{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1212\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1224\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1234\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1244\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"PENG SHI is recorded teaching in Fall 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.\"}],\"term_id\":\"1272\",\"term_name\":\"2026 Fall\",\"version\":2}","candidate_json":null,"error":"[{\"mode\": \"history\", \"instructor_uid\": null, \"error\": \"ModelAPIError: Connection error.\"}, {\"mode\": \"overview\", \"instructor_uid\": null, \"error\": \"ModelAPIError: Connection error.\"}]","model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-dab8f6acaa72f26086773521","output_id":"72ca5b126ca01dda73a099d63661962a2bb82d576c059d97ed932afcfd76d028","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\",\"n8\",\"n9\",\"n10\"],\"condition\":null,\"course\":null,\"evidence\":\"GEN BUS 307,317704, 705, 881,ECON 400,410,STAT/MATH 310,STAT 333,340, or declared in the Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":307,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 307\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"704\",\"course\":null,\"evidence\":\"704\",\"id\":\"n3\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"705\",\"course\":null,\"evidence\":\"705\",\"id\":\"n4\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"881\",\"course\":null,\"evidence\":\"881\",\"id\":\"n5\",\"kind\":\"condition\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":400,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 400\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"410\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n8\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n9\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n10\",\"kind\":\"course\"}],\"notes\":[\"Courses 704, 705, 881 are absent from linked_courses. They are preserved as condition nodes with status needs_review.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-dab8f6acaa72f26086773521","output_id":"72ca5b126ca01dda73a099d63661962a2bb82d576c059d97ed932afcfd76d028","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"GENBUS 307\",\"field\":\"description\",\"quote\":\"Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Foundations in business analytics, statistical inference, regression, and hypothesis testing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"},{\"course_id\":\"ECON 400\",\"field\":\"description\",\"quote\":\"An introduction to applied econometrics - the statistical methods economists use to evaluate empirical relationships\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified\"}],\"text\":\"Programming with R, calculus-based statistics, and applied econometric methods.\"}],\"search_phrases\":[\"predictive modeling business\",\"linear regression classification\",\"bias-variance tradeoff\",\"statistical learning business analytics\",\"GENBUS 656 prerequisites\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Leads to development of linear regression and classification models, and discussion of building models for prediction.\"}],\"text\":\"Developing linear regression and classification models for prediction.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Applying feature selection, regularization, and managing the bias-variance tradeoff.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"title\",\"quote\":\"FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS\"},{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models\"}],\"text\":\"An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Feature selection and regularization methods.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"discussion of building models for prediction.\"}],\"text\":\"Building predictive models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"development of linear regression and classification models\"}],\"text\":\"Linear regression and classification models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"the bias-variance tradeoff\"}],\"text\":\"The bias-variance tradeoff.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260906T231458-5fdd2fff","observed_at":"2026-09-06 23:14:58.172943+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-dab8f6acaa72f26086773521","output_id":"72ca5b126ca01dda73a099d63661962a2bb82d576c059d97ed932afcfd76d028","section":"student_experience","status":"insufficient_evidence","value_json":"{\"status\":\"insufficient_evidence\",\"themes\":[]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":false},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-f516c4d3e82cfe326b4f5f54","output_id":"8f42c555a1b916d4226a5c5de489773aee150f0e186563733775902acd0eeac6","section":"requirements","status":"needs_review","value_json":"{\"nodes\":[{\"children\":[\"n1\",\"n2\",\"n3\",\"n4\",\"n5\",\"n6\",\"n7\",\"n8\",\"n9\",\"n10\"],\"condition\":null,\"course\":null,\"evidence\":\"GEN BUS 307,317704, 705, 881,ECON 400,410,STAT/MATH 310,STAT 333,340, or declared in the Business Exchange program\",\"id\":\"n0\",\"kind\":\"any\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":307,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"GEN BUS 307\",\"id\":\"n1\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":317,\"minimum_grade\":null,\"subjects\":[\"GENBUS\"],\"timing\":\"prior\"},\"evidence\":\"317\",\"id\":\"n2\",\"kind\":\"course\"},{\"children\":[],\"condition\":\"704\",\"course\":null,\"evidence\":\"704\",\"id\":\"n3\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"705\",\"course\":null,\"evidence\":\"705\",\"id\":\"n4\",\"kind\":\"condition\"},{\"children\":[],\"condition\":\"881\",\"course\":null,\"evidence\":\"881\",\"id\":\"n5\",\"kind\":\"condition\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":400,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"ECON 400\",\"id\":\"n6\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":410,\"minimum_grade\":null,\"subjects\":[\"ECON\"],\"timing\":\"prior\"},\"evidence\":\"410\",\"id\":\"n7\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":310,\"minimum_grade\":null,\"subjects\":[\"MATH\",\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT/MATH 310\",\"id\":\"n8\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":333,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"STAT 333\",\"id\":\"n9\",\"kind\":\"course\"},{\"children\":[],\"condition\":null,\"course\":{\"course_number\":340,\"minimum_grade\":null,\"subjects\":[\"STAT\"],\"timing\":\"prior\"},\"evidence\":\"340\",\"id\":\"n10\",\"kind\":\"course\"}],\"notes\":[\"Courses 704, 705, 881 are absent from linked_courses. They are preserved as condition nodes with status needs_review.\"],\"root\":\"n0\",\"status\":\"needs_review\"}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-f516c4d3e82cfe326b4f5f54","output_id":"8f42c555a1b916d4226a5c5de489773aee150f0e186563733775902acd0eeac6","section":"search_profile","status":"valid","value_json":"{\"assumed_background\":[{\"evidence\":[{\"course_id\":\"GENBUS 307\",\"field\":\"description\",\"quote\":\"Emphasis on hands-on experience with many commonly used analytic methodologies using the modeling and optimization tools available on almost every professional desktop. The focus is predictive and prescriptive analytics. Predictive approaches use historical data to infer causal relationships and forecast future outcomes\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"Statistical inference and analyses based on models will be introduced and applied in a business context using a calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified so decision makers have information about the quality of the estimates. Regression and time series models are commonly used in business analytics applications\"},{\"course_id\":\"MATH/STAT 310\",\"field\":\"description\",\"quote\":\"Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing\"},{\"course_id\":\"STAT 333\",\"field\":\"description\",\"quote\":\"A foundational understanding of linear regression and how it is used for prediction, feature selection and interpretation.\"}],\"text\":\"Foundations in business analytics, statistical inference, regression, and hypothesis testing.\"},{\"evidence\":[{\"course_id\":\"STAT 340\",\"field\":\"description\",\"quote\":\"Teaches how to explore, model, and analyze data using R. Topics include basic probability models; the central limit theorem; Monte Carlo simulation; one- and two-sample hypothesis testing; Bayesian inference; linear and logistic regression; ANOVA; the bootstrap; random forests and cross-validation.\"},{\"course_id\":\"ECON 400\",\"field\":\"description\",\"quote\":\"An introduction to applied econometrics - the statistical methods economists use to evaluate empirical relationships\"},{\"course_id\":\"GENBUS 317\",\"field\":\"description\",\"quote\":\"calculus-based focus. Topics covered include point estimation, confidence intervals, hypothesis testing, regression models, and time series models. Various methods for fitting a model to data will be explored, and uncertainty about parameter estimates will be quantified\"}],\"text\":\"Programming with R, calculus-based statistics, and applied econometric methods.\"}],\"search_phrases\":[\"predictive modeling business\",\"linear regression classification\",\"bias-variance tradeoff\",\"statistical learning business analytics\",\"GENBUS 656 prerequisites\"],\"skills_taught\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Leads to development of linear regression and classification models, and discussion of building models for prediction.\"}],\"text\":\"Developing linear regression and classification models for prediction.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Applying feature selection, regularization, and managing the bias-variance tradeoff.\"}],\"summary\":{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"title\",\"quote\":\"FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS\"},{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models\"}],\"text\":\"An introduction to predictive modeling for business applications, focusing on linear regression, classification, and model selection techniques.\"},\"topics\":[{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"Topics include selection, regularization, and the bias-variance tradeoff.\"}],\"text\":\"Feature selection and regularization methods.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"discussion of building models for prediction.\"}],\"text\":\"Building predictive models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"development of linear regression and classification models\"}],\"text\":\"Linear regression and classification models.\"},{\"evidence\":[{\"course_id\":\"GENBUS 656\",\"field\":\"description\",\"quote\":\"the bias-variance tradeoff\"}],\"text\":\"The bias-variance tradeoff.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-f516c4d3e82cfe326b4f5f54","output_id":"8f42c555a1b916d4226a5c5de489773aee150f0e186563733775902acd0eeac6","section":"student_experience","status":"valid","value_json":"{\"status\":\"supported\",\"themes\":[{\"aspect\":\"teaching_clarity\",\"evidence\":[{\"comment\":\"Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.\",\"course_id\":\"GENBUS 656\",\"date\":\"2023-05-18 03:36:47 +0000 UTC\",\"difficulty_rating\":2,\"id\":\"2cecd9c0a6874638ddb19c30\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":4,\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"},{\"comment\":\"He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend. \",\"course_id\":\"GENBUS 656\",\"date\":\"2025-04-25 18:50:18 +0000 UTC\",\"difficulty_rating\":3,\"id\":\"a6e5aa340d08ae62bd2da635\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":5,\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":2,\"review_ids\":[\"2cecd9c0a6874638ddb19c30\",\"a6e5aa340d08ae62bd2da635\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2025\",\"review_year_start\":\"2023\"},\"sentiment\":\"positive\",\"summary\":\"Professor Shi is described as a great lecturer who explains difficult material easily and is very helpful and approachable.\"},{\"aspect\":\"assessment\",\"evidence\":[{\"comment\":\"He is a great lecturer, one of the best I have ever had for a coding/stats class. He is able to explain difficult material very easily. If you go to lecture and pay attention you cover all the questions that will be on the exams. He asks them throughout the lectures. Very friendly, approachable, and down to earth. Highly recommend. \",\"course_id\":\"GENBUS 656\",\"date\":\"2025-04-25 18:50:18 +0000 UTC\",\"difficulty_rating\":3,\"id\":\"a6e5aa340d08ae62bd2da635\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":5,\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":1,\"review_ids\":[\"a6e5aa340d08ae62bd2da635\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2025\",\"review_year_start\":\"2025\"},\"sentiment\":\"positive\",\"summary\":\"Exams are covered by lecture content, and asking questions during lectures helps prepare for them.\"},{\"aspect\":\"workload\",\"evidence\":[{\"comment\":\"Professor Shi is very helpful and easy to talk to if you don't understand something. Sometimes the lectures can be a bit boring, but he has R exercises mixed in that are pretty useful to understand the material. Overall, a great guy and a good professor.\",\"course_id\":\"GENBUS 656\",\"date\":\"2023-05-18 03:36:47 +0000 UTC\",\"difficulty_rating\":2,\"id\":\"2cecd9c0a6874638ddb19c30\",\"instructor_id\":\"rmp:1877077\",\"instructor_name\":\"Peng Shi\",\"quality_rating\":4,\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\"}],\"evidence_count\":1,\"review_ids\":[\"2cecd9c0a6874638ddb19c30\"],\"scope\":{\"historical\":true,\"instructors\":[{\"id\":\"rmp:1877077\",\"name\":\"Peng Shi\"}],\"review_year_end\":\"2023\",\"review_year_start\":\"2023\"},\"sentiment\":\"mixed\",\"summary\":\"Lectures can be a bit boring, but the inclusion of R exercises is useful.\"}]}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true},{"run_id":"20260907T155543-ce3781c4","observed_at":"2026-09-07 15:55:43.033547+00:00","course_uid":"course_874e30b1d4fa32884aa74250","course_id":"GENBUS 656","catalog_version_id":"15218206943dc85103e8014d09717fc5ee1818c7293ae302ec88792196e9e5cf","record_version_id":"189ca66368234f9396b717ad4caa7a34412ee65472a4f43b87ac7bd3ab55550a","job_id":"enrich-f516c4d3e82cfe326b4f5f54","output_id":"8f42c555a1b916d4226a5c5de489773aee150f0e186563733775902acd0eeac6","section":"student_summary","status":"valid","value_json":"{\"context_hash\":\"01afedf2ebfa142b4c18588cc68a1fc3e6fa888d0280944d8e13bc3bf9f6cac6\",\"course_id\":\"GENBUS 656\",\"current_instructors\":[{\"instructor_uid\":\"instructor_7ae8046db6b009671007abdc\",\"message\":\"No course-specific reviews available\",\"name\":\"Kyohei Okumura\",\"review_status\":\"no_course_reviews\",\"rmp_instructor_id\":null,\"summary\":[{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":10,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":11,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Fall 2025: 3.40 GPA, 58.2% A/AB (n=67 letter grades). Includes jointly taught sections.\"}]}],\"difficulty_workload\":[{\"citations\":[{\"instructor_name\":\"Peng Shi\",\"review_date\":\"2025-04-25 18:50:18 +0000 UTC\",\"review_id\":\"a6e5aa340d08ae62bd2da635\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_instructor_id\":\"rmp:1877077\",\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\",\"type\":\"review\"}],\"text\":\"Historical reviews of Peng Shi: Attending lectures and paying attention covers all exam questions, as the professor asks them throughout the sessions.\"}],\"errors\":[],\"historical_context\":[{\"citations\":[{\"instructor_name\":\"Peng Shi\",\"review_date\":\"2023-05-18 03:36:47 +0000 UTC\",\"review_id\":\"2cecd9c0a6874638ddb19c30\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_instructor_id\":\"rmp:1877077\",\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\",\"type\":\"review\"},{\"instructor_name\":\"Peng Shi\",\"review_date\":\"2025-04-25 18:50:18 +0000 UTC\",\"review_id\":\"a6e5aa340d08ae62bd2da635\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_instructor_id\":\"rmp:1877077\",\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\",\"type\":\"review\"}],\"text\":\"Kyohei Okumura is the current instructor, but available reviews only cover historical instructor Peng Shi. Shi was praised as helpful, approachable, and an excellent lecturer who explains difficult material clearly. However, some students found his lectures occasionally boring.\"}],\"message\":null,\"offered\":true,\"profile_hash\":\"e59ddc7389015d0035b68cd195c939d475bf72b959b29cf12eab59b454ccaef1\",\"quick_take\":[{\"citations\":[{\"instructor_name\":\"Peng Shi\",\"review_date\":\"2023-05-18 03:36:47 +0000 UTC\",\"review_id\":\"2cecd9c0a6874638ddb19c30\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_instructor_id\":\"rmp:1877077\",\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\",\"type\":\"review\"},{\"instructor_name\":\"Peng Shi\",\"review_date\":\"2025-04-25 18:50:18 +0000 UTC\",\"review_id\":\"a6e5aa340d08ae62bd2da635\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_instructor_id\":\"rmp:1877077\",\"source_review_id\":\"UmF0aW5nLTQxMDQ3Mzg3\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\",\"type\":\"review\"}],\"text\":\"Historical reviews for Peng Shi describe him as a helpful, approachable lecturer who explains difficult material easily, though some find his lectures boring.\"},{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"Recent recorded grades — Spring 2025: 3.53 GPA, 73.7% A/AB (n=38 letter grades); Fall 2025: 3.72 GPA, 80.5% A/AB (n=149 letter grades); Spring 2026: 3.60 GPA, 90.0% A/AB (n=30 letter grades).\"}],\"student_experience\":[{\"citations\":[{\"instructor_name\":\"Peng Shi\",\"review_date\":\"2023-05-18 03:36:47 +0000 UTC\",\"review_id\":\"2cecd9c0a6874638ddb19c30\",\"run_id\":\"20260907T155543-ce3781c4\",\"source_instructor_id\":\"rmp:1877077\",\"source_review_id\":\"UmF0aW5nLTM3OTUwOTY1\",\"source_url\":\"https://www.ratemyprofessors.com/professor/1877077\",\"type\":\"review\"}],\"text\":\"Historical reviews of Peng Shi: R exercises mixed into lectures are useful for understanding the material, though the lecture style can be boring.\"}],\"task_hash\":\"74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68\",\"teaching_history\":[{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":10,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":11,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1262\",\"type\":\"grade\"}],\"text\":\"KYOHEI OKUMURA is recorded teaching in Fall 2025. Recorded history may be incomplete and does not establish a future schedule.\"},{\"citations\":[{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1212\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1224\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1234\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1244\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1254\",\"type\":\"grade\"},{\"course_id\":\"GENBUS 656\",\"run_id\":\"20260907T155543-ce3781c4\",\"section_number\":1,\"source_course_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"source_record\":{\"entity_id\":\"d5a5cce9-e57a-38f2-816c-30d6878db375\",\"file\":\"tables/observations.parquet\",\"kind\":\"grades\",\"source\":\"madgrades\"},\"table\":\"section_grades_latest\",\"term_id\":\"1264\",\"type\":\"grade\"}],\"text\":\"PENG SHI is recorded teaching in Fall 2020, Spring 2022, Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.\"}],\"term_id\":\"1272\",\"term_name\":\"2026 Fall\",\"version\":2}","candidate_json":null,"error":null,"model":"nvidia/Qwen3.6-35B-A3B-NVFP4","model_revision":"1355db6a052410cfd62085d94b58866fd0f2c3c5","selected_for_release":true}]