[{"run_id":"20260906T231458-5fdd2fff","semester":"1272","observed_at":"2026-09-06 23:14:58.172943+00:00","record_version_id":"9640fddc71a6c47ba0068fc2f36c8181a7376a544f2db589364f477e1abb1b7b","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"1061ee60bc95f0ea74b48b5921cc2d62e078adb90e2bccbf5ef4fa57ae15b9a3","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341,345, or375) and (STAT/​MATH  309,431,STAT 311, orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)","official_learning_outcomes_json":"[]"},{"run_id":"20260907T155543-ce3781c4","semester":"1272","observed_at":"2026-09-07 15:55:43.033547+00:00","record_version_id":"9640fddc71a6c47ba0068fc2f36c8181a7376a544f2db589364f477e1abb1b7b","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"1061ee60bc95f0ea74b48b5921cc2d62e078adb90e2bccbf5ef4fa57ae15b9a3","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341,345, or375) and (STAT/​MATH  309,431,STAT 311, orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)","official_learning_outcomes_json":"[]"},{"run_id":"20261014T203208-0851c9a1","semester":"1274","observed_at":"2026-10-14 17:50:09.728220+00:00","record_version_id":"b4b4e1bb2e341355161f89e429be6018ff5cbb57ad7d2eb694744c41614f1d6f","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"5d684a4cae77ff8fe1c933f88d57be864b89e9825ab60caa574bf96af9cb8852","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341,345, or375) and (STAT/​MATH  309,431,STAT 311, orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP (undergraduate or graduate)","official_learning_outcomes_json":"[{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Conduct numerical simulations of stochastic dynamical systems and implement data assimilation methods.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Analyze data from a stochastic dynamical system and characterize the system's properties in terms of its statistics, such as mean, variance, and autocorrelation function.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Calculate analytical formulas for the solutions of discrete-time stochastic processes and stochastic differential equations.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Recall and state the basic properties of Brownian motion and the Wiener process.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Illustrate the basic properties of Brownian motion and the Wiener process using numerical simulations.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Estimate the parameters of a dynamical system using simulated data.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Predict the future evolution of a dynamical system using analytical formulas and numerical simulations.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Assess the accuracy of a prediction in terms of different definitions of error and uncertainty.\"},{\"catalog_year\":\"2026-2027\",\"observed_at\":\"2026-10-14T17:51:41.022582+00:00\",\"source\":\"catalog\",\"source_url\":\"https://guide.wisc.edu/courses/math/\",\"term\":null,\"text\":\"Identify applications of course content in areas of modern research.\"}]"},{"run_id":"legacy-03b0bbb9723907cf3d1b41cf0bb34837dc398e42","semester":"1262","observed_at":"2025-04-16 05:20:33.104844+00:00","record_version_id":"b8e1fcd10aac2123141298b14c96ca69fe5f70bf4aa3e24f769ef7065e814499","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-0837245627eb714ae6bb0375f7abe8f41042011f","semester":"1262","observed_at":"2025-04-16 05:20:33.104844+00:00","record_version_id":"b8e1fcd10aac2123141298b14c96ca69fe5f70bf4aa3e24f769ef7065e814499","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-08c92e359bbbc110b0778b7e0b15d6f40000e6b1","semester":"1262","observed_at":"2025-08-21 07:05:14.730696+00:00","record_version_id":"b9008ac86086e7743425af09bbc1778844d5ca5c74ec1fac2aa29b669e5a5e15","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-0944d76440dc778fbb89626058c10451009ce9c6","semester":"1262","observed_at":"2025-05-21 03:36:18.194118+00:00","record_version_id":"f6b71d0261b9f813ae94459beae5f9f6c59d4a99a44e6c1c69047ad023a9f901","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-0a7f4bce29c816ef4d3d71b4eb8a2a3e15e33cc1","semester":"1262","observed_at":"2025-06-18 20:04:02.695444+00:00","record_version_id":"7f27c2885a1ab5ca188d491a19d12fb15a5f2259ba2b41e7312f2a0292124f47","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-0ab917f827a7de22ebdc1087114d377a31c4687b","semester":"1262","observed_at":"2025-05-22 02:11:02.531191+00:00","record_version_id":"07797f98277425b2ebd4a757becc28c32a664796db5122f89bda72956cfb81e9","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-0c40ecab4b6db9333d1f18aef816e3ee659e300c","semester":"1262","observed_at":"2025-05-25 18:02:34.797617+00:00","record_version_id":"69636f35933ab61d732ef9d114e4ac7bc1d6d30cf28bb17bd8b92b9ba8d75d07","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-0cb4d29a588ec7f11c7e00b7cf5dc3a0d23d0349","semester":"1266","observed_at":"2026-01-22 01:00:26.555427+00:00","record_version_id":"894302258edc910aed81f9d722c22fbf10b9523c6ca137768bf9a8a9884152f2","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-11c30ec39e91498cbfcf3a923c53a4b03e387280","semester":"1262","observed_at":"2025-07-20 04:58:26.847117+00:00","record_version_id":"b7626514bce83ced407f0807429827d131dd840a778626273d0fa1ee26e5b5e0","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-13cd500edf2587e9a3428f326253738fbea965ea","semester":"1264","observed_at":"2025-12-14 04:50:04.653658+00:00","record_version_id":"66c42abea9df041e09e3db55adce6894b63a2a2d0df9469919afcfdddad5f929","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-1da08b2de39f05e6bc877c189fc2fd6eeda563f5","semester":"1262","observed_at":"2025-07-01 00:03:56.707251+00:00","record_version_id":"fb193f40a17cf8c6e8b2808af91a3c40b8e615bc16527b6978d1a9d32bbde238","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-2031af47efe133b0e7877f433286ff07385afc41","semester":"1262","observed_at":"2025-07-02 20:16:45.749584+00:00","record_version_id":"4a45fa056a9b1c7503ebdca643f722ea1735820f26b568a89fc60f1b510dbc00","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-238eba6f32fbc30407f616dd0628ff02a2a824dd","semester":"1262","observed_at":"2025-08-17 05:15:07.788352+00:00","record_version_id":"31739da733ef9f850e14836fb45b5137c857aab2d88193106424d525519f6e2e","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-25f0ff56bc4d80f8a8afa1a0bcce8dae6fddb5e5","semester":"1262","observed_at":"2025-05-25 17:20:25.942090+00:00","record_version_id":"5db358a39788817d66980aaf6a60f13245ec8c6d05417674cb591538ee1d628f","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-2923f91826ce253b7aeefa9f75b4a39fe4c80ff0","semester":"1262","observed_at":"2025-08-10 05:10:25.938416+00:00","record_version_id":"cb1e1725b6278974ec1775067dfb5c3e30d8a6ef5c33ee7ad98d5c5c8e05d543","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-2a05214a4023e0157fa6f7d188c85308f714176c","semester":"1262","observed_at":"2025-09-07 04:56:38.255315+00:00","record_version_id":"157260891248bebbcd613da85b37f5104a8c011218e1c1cc3fd08276c20205b4","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-2b1b8e39131de92b94620b25c5bbb25690efed4c","semester":"1262","observed_at":"2025-06-01 07:54:53.913454+00:00","record_version_id":"f314872f899aa6954552cc98816be55b32e636d4c3308cc4052bfe4d376dbfea","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-33a1225644e5a5855d60f66e65413946e72fedf4","semester":"1262","observed_at":"2025-06-22 01:52:05.152650+00:00","record_version_id":"849fb76880df476dcaee343f3013832c24f8212d2734bc539c904a3dd30e8cb6","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-3b89c83dbf5208e13756184de1f2b2d0c09af35f","semester":"1262","observed_at":"2025-06-30 06:40:10.235522+00:00","record_version_id":"8cc002e7bd50f7a7ade91227963ab42646342bf042f0cc2f3565eb811b25c806","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-3c3eebf7f35c39364b445a2fa5a112ce28dbb4a1","semester":"1262","observed_at":"2025-06-01 17:03:59.062985+00:00","record_version_id":"29bac5b34578bf07b79882ae75d3ee5d951087aa304761c7d9ad7513a37bc9e4","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-3fa4dd71391f1c9cf4ab2216680bbc428689f92d","semester":"1262","observed_at":"2025-05-25 01:15:58.180530+00:00","record_version_id":"72f3aa53342be367a248270c5d6cebcba76eaf9a87a2ed1a3606e17acc0a00bc","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-4040aa216da722a34483f68fc1e129ea571ae08d","semester":"1262","observed_at":"2025-06-01 07:27:39.695506+00:00","record_version_id":"f6fb85e8bc41f898561a3eb774fd8abbb1fc1c0f3e019c986d87d16689b31ef6","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-454d53cd690294459f8ad0793cc3ed562060e56c","semester":"1262","observed_at":"2025-05-25 01:34:00.536004+00:00","record_version_id":"d0081697f4dde460a426bc5ff8b182146afb3143a3c29991781bee326789ec84","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-486f091e051fdb4ccdb80ee7b4fb0a5814510324","semester":"1264","observed_at":"2025-11-16 05:32:31.273726+00:00","record_version_id":"48b331e74f1967ca58ab82dc3985343037b930558b7fa86e0e8a564832f70b61","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-50393cfc5fd5598d4f4731ef1919f2ff53af07d9","semester":"1262","observed_at":"2025-06-30 06:05:57.438846+00:00","record_version_id":"a93c2aeaa379125fae55f0b9b34a97e3f0c17a7ada17a586cb08c66d936a5e44","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-50f6947786b208c800db8bbd0cec84a8f0f187d2","semester":"1262","observed_at":"2025-06-02 03:49:42.915428+00:00","record_version_id":"0f49593b8fa61fd80ed40e4326a0644ea9fd2cd8fc560e6a8824f75b3a2d3125","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-51d86ed4082dced3803c295419b0ec8440942016","semester":"1264","observed_at":"2025-11-12 04:50:41.141307+00:00","record_version_id":"88ca396f2cb3b3875d83a052a6166f644d6ed441144a4746771d0af58bd84028","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-53101bd1c10db0d00d094e4c9154d4c244ff23d9","semester":"1262","observed_at":"2025-10-05 04:58:05.366968+00:00","record_version_id":"accc9bd990a2032beea1468bc635e3ffedc10f4d2dcd2dbb2ed3bd957c4650ba","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-58751151c564e11c5cb8aedd3a2d3527c22f73eb","semester":"1262","observed_at":"2025-06-01 06:59:01.536576+00:00","record_version_id":"3c20a2e359a5c59d7ea341e4d9126bad9cf156fe93892baeb467a325963442a8","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-5c4c8d3701a333ba4ddadef7d0b5dde8440fcdf0","semester":"1262","observed_at":"2025-06-01 01:57:51.256704+00:00","record_version_id":"f78973caa34d6ef0f5ada7b788b6c7a609197e2f094688285efd99ae33d75dcd","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-5d20d9f81651d3c83fcaa2fd10870ce6e5d5a685","semester":"1262","observed_at":"2025-05-21 16:48:48.484741+00:00","record_version_id":"14e88c0fb63373390ff76d795107cd8cd9a37062270a13edf6d043aee2a48576","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-5feaebf2ab37051c86ad360cc0fa0d7e0e2ea295","semester":"1262","observed_at":"2025-05-24 15:58:23.378859+00:00","record_version_id":"877e85d4dbf6a2efb475a2740ec3a13e5dcdd5b0850d13f7403f6ed4636388b4","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-60949965544fce098cc006354e201149005fb77b","semester":"1264","observed_at":"2025-10-26 05:07:14.077864+00:00","record_version_id":"e04a0f82b56bb3fa9c4525669e0413332588ad423467a96beb28add1a4ea9d0c","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-639225ac3addf8522f1d30cb4fe26054ea7f8c49","semester":"1262","observed_at":"2025-06-01 08:14:42.497608+00:00","record_version_id":"c3d156b9a93bcf97b2463e1b9dd450018814933732e5c9b4c396bff6a62a9d4c","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-64093b17b3a9c67a41aa33eb37adb53ec38df714","semester":"1262","observed_at":"2025-05-22 02:11:02.531191+00:00","record_version_id":"b5862a61601795e7ce4b30ff32647e5ebe00edb8f5241288e1cffd73218327b6","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-6885b56099f55e08aa0205546369fb0381d50e13","semester":"1262","observed_at":"2025-07-13 04:59:22.367394+00:00","record_version_id":"052fa4b1847b6462e161eb730e3d79f1b4d592449c8594ff76b419c2e61e3717","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-69d4b2b16e8268f75b6390b3a0e2ce8bf73999da","semester":"1262","observed_at":"2025-06-30 03:21:29.925498+00:00","record_version_id":"31ad14621f512e2832b29d7d45cf9fde721ad004aad07dc55addefc6f1a2e3af","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-6b29c3e0673b2f9a736d2363db5409e03d5b3ed4","semester":"1264","observed_at":"2025-12-03 12:33:41.418072+00:00","record_version_id":"6eac3588dadee16c4fe59c1ebc0d8f02c9af40c85130c764125e5abc5e845c44","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-73259984748de484976ef2ad56ad7ab3d4cb4518","semester":"1262","observed_at":"2025-06-26 08:27:56.461216+00:00","record_version_id":"d2f2f6aa5887910cae842f3fbdf811c5dc077db85f424defbf79191c72662072","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-741602fbc6252c232a0b9e6bc1a9ae3101625f5a","semester":"1262","observed_at":"2025-06-30 22:49:32.522034+00:00","record_version_id":"a1528cc944f39c6deb826111db5b06473b2ee8b171333a71c96e7d8db925e9a3","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-7774f45558276ec29bf626e58fffb4a27bdbb2a9","semester":"1264","observed_at":"2025-11-01 11:49:36.359151+00:00","record_version_id":"f575f522d540cf623002183fc94e005ec246ee04f48902cbec367add8d46a955","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-7ab7c5e783fc8ebef8b8ee8cea019ee37a72ce74","semester":"1262","observed_at":"2025-09-28 04:58:07.340658+00:00","record_version_id":"733a5bd9643d37390ffc811468b8978a50304dbc83766f18c1d6b596e43bb906","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-7b2f22d83ea05055ab90dcb77dc802382cccc6f7","semester":"1262","observed_at":"2025-05-21 17:56:46.096741+00:00","record_version_id":"35df48dfc1ac14c81be738bc59afddfce5942a8a5c9308b23bd8689853eee0e8","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-7ca1175502be18eb51f7b016818a884a43fd4ebb","semester":"1262","observed_at":"2025-07-29 06:28:23.644795+00:00","record_version_id":"96ef8205af5eada8a37b1e0f15a2b38a4a2fa1949f3e188e546e57c5462bff9a","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-7d8cee5e42602ad9ba5dc0322085af2fa0dffaac","semester":"1262","observed_at":"2025-05-22 01:15:19.291664+00:00","record_version_id":"0aa0ae8469ede13e9d0787c46fe45f480eab4b9e3038c3d1247b170b93d24f97","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-81a0d6a7f668c98c59bd05fa988aa079c5ad1f68","semester":"1262","observed_at":"2025-05-31 07:49:50.419452+00:00","record_version_id":"3d05554b0836b89b28d833df739be2d6bae9e52c09674ce3787717c519d351af","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-82f31c08c746a214872494cde6ecaeb9330441b4","semester":"1262","observed_at":"2025-06-17 21:26:08.547156+00:00","record_version_id":"3eb36fd48be762da6829030ba70178757ac3e39ccdc930aea1c6beead0c3d70b","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-84c248343c5a99ab42c5616c594812a5e3e31cbb","semester":"1262","observed_at":"2025-07-04 06:00:07.487827+00:00","record_version_id":"5f4ee50c42bf1d9d07f23afdd6c967289a136a92e08be562a7a42b87c48a79e1","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-86334e7275e315853287e8580c35456c68df800b","semester":"1262","observed_at":"2025-05-22 06:53:03.336354+00:00","record_version_id":"e4abfa05e93eb7dd7dcd42cdf29a0678f01e39fab5a2d4bbe8a2ac408fbe46b7","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-8686dc1664f93a6c05f50321410961b39b12f91b","semester":"1262","observed_at":"2025-05-28 10:17:41.595894+00:00","record_version_id":"dea4efaa1e8815c490d1f4395af1e39c9b3e6fc10b2e7e5810f2d0f2b0376229","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-86d941366886c9a14a2581c04e20d0bdd25e1b02","semester":"1262","observed_at":"2025-08-31 04:54:14.656039+00:00","record_version_id":"0ed54f5945bcf4cb2d64d4f2e03d978802e4d667a6652977fcdd3ca86da6e566","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-8ba1d2e6cfbb1525b4f9f769ec164fb6ad0f237a","semester":"1262","observed_at":"2025-06-02 05:22:45.010398+00:00","record_version_id":"d8e5a65745928bd616222acd77e8d8bd51aa1fbf65ed8114c06c9a65a58a3f4d","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-90e5aa456689a3b222016c05c319bbcd0bddd453","semester":"1262","observed_at":"2025-05-25 02:00:10.037905+00:00","record_version_id":"fde2d034d938d6e6ca5ceaf47d0313879349a1646b178af980faf048a2a80575","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-92d960aa7e4cd6e4c9ac8b2e232092904038e9e4","semester":"1262","observed_at":"2025-06-08 01:50:46.451351+00:00","record_version_id":"de7ef71597c4636fff2aaabd1a5a5bf6233168a79ea7db7af5705d4fe72bad64","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-94002ae588a8f71eca6d64105f2e603e8587293b","semester":"1262","observed_at":"2025-05-21 09:01:16.021508+00:00","record_version_id":"30933a2f481843782fb52db2907b665c7e5b935c68103681004b4fad639e8502","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-9dd575e013c5c6e000ab3ad2d1288cc1dd9c96dc","semester":"1264","observed_at":"2025-12-21 04:47:46.589081+00:00","record_version_id":"783c890b74676d8d47b4e8e47a1f63910402c696b622f115cf9559ef62ee6c37","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-a1ea26ed78cbd6e94bc3221e50a6ed9b6bf43871","semester":"1262","observed_at":"2025-05-25 09:16:55.180581+00:00","record_version_id":"58805a757df039d896f0bbedc0f00182981aae49863bb4301d4d7cc9b39d356f","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-a76a3505853d5ea9c13bb98472b87361d469e2ee","semester":"1262","observed_at":"2025-05-28 18:03:41.793846+00:00","record_version_id":"536f52e3473b2011e7336e8059c8269e6dec255695060a10a50859ccf3318e79","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-b285d591f6acf3aa1910e02edc297664db86eb8c","semester":"1262","observed_at":"2025-06-10 22:48:22.460295+00:00","record_version_id":"1d7c8bb0ba27317465900b80d1660ad8c96288b5b2d53829d83b37584b032105","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-b79af756e4f00ba98231feb859ed8e98239eb811","semester":"1262","observed_at":"2025-05-22 02:47:56.931581+00:00","record_version_id":"13e3711ca47c5ffe07e017b2373a45a1d152ddca3625453332f2504e57b170ec","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-b89e05b94730c6471439082d9279c0a834a4aeec","semester":"1262","observed_at":"2025-08-26 20:25:42.325239+00:00","record_version_id":"30a27c341269ca06f90099119966b8f309d2949125bb882e32f7c8d50a12cc7d","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-b9279a12d1f2688df9e556183c94d41a89e431dc","semester":"1262","observed_at":"2025-07-06 01:49:47.203835+00:00","record_version_id":"353fee12431c3021c8630271ec6347c0bee44021564392b4bc4a9074b1cc182b","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-bbdc905e96cc681a5e9a8b02c795b682206e36d1","semester":"1262","observed_at":"2025-06-25 08:02:45.445730+00:00","record_version_id":"aa299b1eadc74886ee5827612445c94c0708862efd4c1a61add9337a8bd82e17","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-bde225b5243a13d81827aa173e39c78a652b54a0","semester":"1262","observed_at":"2025-05-23 07:22:33.219345+00:00","record_version_id":"af138db8a137d9177b32e1d25212dea948dde774ea6c5dd09bbb02deeb05e894","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-c1fac4cbfde737f4f4ac02cdf15e6f5a9b5b676d","semester":"1262","observed_at":"2025-05-22 01:15:19.291664+00:00","record_version_id":"0aa0ae8469ede13e9d0787c46fe45f480eab4b9e3038c3d1247b170b93d24f97","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-c6b62be45f8a13cba109ef2a7741b9cd9a6b8272","semester":"1262","observed_at":"2025-08-24 08:48:09.035112+00:00","record_version_id":"59e2b51a9cb6dc4585dce7446e5c55f6d11005efb75f1742a531a7952cce2acb","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-cc83f62cb6db61f39320916d819ad48ce55cd96d","semester":"1262","observed_at":"2025-05-22 00:45:35.735230+00:00","record_version_id":"635c9d22a901e6afbd4d74b527eadf2aa827330c3549b3ddc12fa019760cf7bc","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-d33818a01b6b4c5e2902e704da6e5b713742d4dc","semester":"1262","observed_at":"2025-10-13 10:46:24.251475+00:00","record_version_id":"380b36de82ecc51fe4f176a5987b3b8f3cc05c432f6407dc8760d2b064a23a95","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-d3c17db9832d3064916046caf891dd23f10b1bb9","semester":"1262","observed_at":"2025-08-26 11:30:42.288639+00:00","record_version_id":"592840defa84e87407120e3ef6919de1b31e49f1efc538149ae0945f4699e5c0","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-d4ee226dc44268e9b6767fb9f5f20343322f494b","semester":"1262","observed_at":"2025-06-15 01:52:23.911697+00:00","record_version_id":"f6cc154ff1f437eaacc377c8083ac241b57f42ca534d43b611a780e6b4b43742","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-d8f9fc7f136e2033556d9d65bbdbdf33425dbdc9","semester":"1262","observed_at":"2025-05-28 16:48:12.400438+00:00","record_version_id":"5c8390048e16fb005628beacb428eb95d6fe5b45d51279b641a97c3635cac8ae","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-dbbc9739c616c3846b2e796e00a36fa41225f7d8","semester":"1262","observed_at":"2025-06-01 17:47:19.325144+00:00","record_version_id":"42d750a8af9a68a156e348a0a87ed5b9a6ee900320d6bd5fe86c22e8bea43b5d","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-dbe5f2d24b48c3177297a3d69c553cc9d1194bac","semester":"1262","observed_at":"2025-09-14 05:00:27.366070+00:00","record_version_id":"fc5a2bc961530f956d1642dc71f822c916d808af60abf28ae96ad8e9da85337a","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-dd357dc2758e9e282c30988a527ce37de92593b9","semester":"1264","observed_at":"2025-10-19 01:39:41.951404+00:00","record_version_id":"522ed885c8fe8e99e96959740d4fc5c229367accbb7186b367cf4cf96a1db227","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-de74de559e409a3fe668685e28043b888d2e6841","semester":"1262","observed_at":"2025-06-27 07:38:50.882897+00:00","record_version_id":"216982c211ffd67010f156eaff2057b8430adbbb7f534ea9faae31ca1b0ab44b","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-deca7188cf536ade3203cd88b52a45e92a059617","semester":"1262","observed_at":"2025-06-27 00:37:46.087071+00:00","record_version_id":"d6c77e23722bb3e63b9bbc398432f18578168c3d69d1faea6af806e7631820b4","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-df60fa9ac3931c5612b0fa641b7a6f5fbec86fab","semester":"1262","observed_at":"2025-05-24 06:36:37.554678+00:00","record_version_id":"90e6e9d3731982c1b35be93f4f15fb52bbab09defc56e045799417623c1f2c2d","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-e339da2d849aaf8bb4dceca07a84728af23cf755","semester":"1262","observed_at":"2025-07-23 08:49:22.748925+00:00","record_version_id":"b78af217912b46cdb31c45d3a83b53ad8ad27b7e15528b0d247dde040c309f19","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-e934b1784835f5d36ae9b7ef0992b11e72c39142","semester":"1262","observed_at":"2025-06-01 06:21:25.219533+00:00","record_version_id":"00c56298195cc4b2e2b7361bd507cc7aa593f31d269a00d2b9955cfb002e105a","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-ef471690c082448deebd2687eab84b0b78813c36","semester":"1262","observed_at":"2025-06-29 01:47:56.317645+00:00","record_version_id":"fb64ea36bbb937dc05204b45c6461e418e7487765c6f255abfaca96312b085f1","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-efc4e8e2cd7c3f68e059a308990c00c77c7611eb","semester":"1264","observed_at":"2025-10-16 05:48:13.935141+00:00","record_version_id":"446f98c4f2ea2a28f8e50d1fcb46e51f8b2a925d72849adae47722ba88880a5b","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"},{"run_id":"legacy-f18b7749725097d41f69c60779ff38cf073b93b3","semester":"1262","observed_at":"2025-05-21 19:52:03.259353+00:00","record_version_id":"9f0f3c7ec78be01c7cfec99cc90330adbce85cc63fc843966855ce82aca70a9f","course_id":"MATH 616","course_uid":"course_2d244e0d230263518e578140","catalog_version_id":"99d3cf1a2c3854cac92a3c4124ee7428aef7fbc2ce95605609819ad773aeba47","course_number":616,"subjects":["MATH"],"title":"DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION","description":"An introduction to data-driven dynamical systems, including mathematical theory, methodology, numerical algorithms, applications and the use of a programming language to solve related coding problems. Topics include stochastic toolkits for dynamical systems and data science, linear Gaussian processes, nonlinear stochastic systems, elementary stochastic differential equations, data assimilation, parameter estimation, forecasting and prediction.","requirements_text":"(MATH 320,340,341or375) and (STAT/​MATH  309,431,STAT 311orMATH 531) and (MATH 322,341,375,421, or467), graduate/professional standing, or declared in Mathematics VISP","official_learning_outcomes_json":"[]"}]