[{"run_id":"20260906T231458-5fdd2fff","semester":"1272","observed_at":"2026-09-06 23:14:58.172943+00:00","record_version_id":"bc86576d1dc7c886a084974b8cd95f4b3a54486a1de9add4afc42df0d72c8595","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"20260907T155543-ce3781c4","semester":"1272","observed_at":"2026-09-07 15:55:43.033547+00:00","record_version_id":"bc86576d1dc7c886a084974b8cd95f4b3a54486a1de9add4afc42df0d72c8595","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-03b0bbb9723907cf3d1b41cf0bb34837dc398e42","semester":"1262","observed_at":"2025-04-16 05:20:33.104844+00:00","record_version_id":"18af08682b8e13c9b7b1e2574a50147fdfc4bb652936c425814bac090e98fbeb","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-0837245627eb714ae6bb0375f7abe8f41042011f","semester":"1262","observed_at":"2025-04-16 05:20:33.104844+00:00","record_version_id":"18af08682b8e13c9b7b1e2574a50147fdfc4bb652936c425814bac090e98fbeb","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-08c92e359bbbc110b0778b7e0b15d6f40000e6b1","semester":"1262","observed_at":"2025-08-21 07:05:14.730696+00:00","record_version_id":"d72ba48bca00c491f9035dab13ebf6d8ba8599d2ecf9c770c0e6121e041a0914","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-0944d76440dc778fbb89626058c10451009ce9c6","semester":"1262","observed_at":"2025-05-21 03:36:18.194118+00:00","record_version_id":"e0225c98761216fbad4c2195180f01593a415dbcf8ce365573042ccceafb0e58","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-0a7f4bce29c816ef4d3d71b4eb8a2a3e15e33cc1","semester":"1262","observed_at":"2025-06-18 20:04:02.695444+00:00","record_version_id":"b7049b0b12a96e863c51e901e826cb32b12f8ef5c1e0704a87dbe49af55d2901","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-0ab917f827a7de22ebdc1087114d377a31c4687b","semester":"1262","observed_at":"2025-05-22 02:11:02.531191+00:00","record_version_id":"cd763d1837a0663d732ff5654eabce41894bd95e7b9c6ed63ac2e065bf2495f7","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-0c40ecab4b6db9333d1f18aef816e3ee659e300c","semester":"1262","observed_at":"2025-05-25 18:02:34.797617+00:00","record_version_id":"e46925e67d96a037a56c3644bfeae12f653d728a77fff95128101e0422224e70","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-0cb4d29a588ec7f11c7e00b7cf5dc3a0d23d0349","semester":"1266","observed_at":"2026-01-22 01:00:26.555427+00:00","record_version_id":"a4fc947244598985fcab68056ac5a63bbf431cf6d53f1e8c85ae0467a6da6583","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-11c30ec39e91498cbfcf3a923c53a4b03e387280","semester":"1262","observed_at":"2025-07-20 04:58:26.847117+00:00","record_version_id":"13488e5810eb8054a2607c5e52b1ce933803079bad1089f63c5b37a5c403c211","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-13cd500edf2587e9a3428f326253738fbea965ea","semester":"1264","observed_at":"2025-12-14 04:50:04.653658+00:00","record_version_id":"93f77cef11d7c13cd4d23c994eb4c7e5e1ecc0db09287fc1e9c48b2293d32e6f","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-1da08b2de39f05e6bc877c189fc2fd6eeda563f5","semester":"1262","observed_at":"2025-07-01 00:03:56.707251+00:00","record_version_id":"16ab764053c6fb0c2b3c2c9e39c9718ea709481ed8e0711142862d29e99c0434","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-2031af47efe133b0e7877f433286ff07385afc41","semester":"1262","observed_at":"2025-07-02 20:16:45.749584+00:00","record_version_id":"72b148414aaca5b45325e707108fa9e466e850eb2a532aebeeca849b248b4d99","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-238eba6f32fbc30407f616dd0628ff02a2a824dd","semester":"1262","observed_at":"2025-08-17 05:15:07.788352+00:00","record_version_id":"680fc34da8660da9a25fa3ea4a1b6dd395b3047cf37e376b1f9249798f8f6c43","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-25f0ff56bc4d80f8a8afa1a0bcce8dae6fddb5e5","semester":"1262","observed_at":"2025-05-25 17:20:25.942090+00:00","record_version_id":"a11ee4c6e29a819492b95e07732e2abc404cbcca3df97db3f1037095806c6ca1","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-2923f91826ce253b7aeefa9f75b4a39fe4c80ff0","semester":"1262","observed_at":"2025-08-10 05:10:25.938416+00:00","record_version_id":"861b4c4e3663751928d3057ea78a2b36a382d0aabe4ec62dfac70b3de7b7f58d","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-2a05214a4023e0157fa6f7d188c85308f714176c","semester":"1262","observed_at":"2025-09-07 04:56:38.255315+00:00","record_version_id":"d72ba48bca00c491f9035dab13ebf6d8ba8599d2ecf9c770c0e6121e041a0914","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-2b1b8e39131de92b94620b25c5bbb25690efed4c","semester":"1262","observed_at":"2025-06-01 07:54:53.913454+00:00","record_version_id":"91a20fa0335026e5035d90a635bfc205c806134628e136f0463d1f6d0d8e581b","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-33a1225644e5a5855d60f66e65413946e72fedf4","semester":"1262","observed_at":"2025-06-22 01:52:05.152650+00:00","record_version_id":"e4c6982b38e73befa3a3fc3cd349beb2e8877e04d981d1e8e46a19880d8fdb7c","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-3b89c83dbf5208e13756184de1f2b2d0c09af35f","semester":"1262","observed_at":"2025-06-30 06:40:10.235522+00:00","record_version_id":"09cf4f693a2ccfb72e2d5ef6a7403d7f2e556925c53aca2a8c4b61861f15cf1a","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-3c3eebf7f35c39364b445a2fa5a112ce28dbb4a1","semester":"1262","observed_at":"2025-06-01 17:03:59.062985+00:00","record_version_id":"a9ecb20dc5e91cfe5f670400cba5f24535db5f1de3a480b0b06def4b82373498","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-3fa4dd71391f1c9cf4ab2216680bbc428689f92d","semester":"1262","observed_at":"2025-05-25 01:15:58.180530+00:00","record_version_id":"3147a38b593549480bf177afd4817cdaf381e60ce994f0c40c1c9c300e597e9a","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-4040aa216da722a34483f68fc1e129ea571ae08d","semester":"1262","observed_at":"2025-06-01 07:27:39.695506+00:00","record_version_id":"283738d21caaead02fe89c24f0f92ab86b957ddf65c738a17268b9f1f796606a","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-454d53cd690294459f8ad0793cc3ed562060e56c","semester":"1262","observed_at":"2025-05-25 01:34:00.536004+00:00","record_version_id":"10f650e94ef74d1cec616eb0bff9a2c6f63ba49c6c788c7c14d4ef62a2465dd0","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-486f091e051fdb4ccdb80ee7b4fb0a5814510324","semester":"1264","observed_at":"2025-11-16 05:32:31.273726+00:00","record_version_id":"f7245c5b74565e61287740a69d7de2a348a5d8d21d77995859beedb067b7b626","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-50393cfc5fd5598d4f4731ef1919f2ff53af07d9","semester":"1262","observed_at":"2025-06-30 06:05:57.438846+00:00","record_version_id":"6ae2802d33c91b1758ee426ee194e2968edd910db7c764b2e2d1e7b3232ac7a3","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-50f6947786b208c800db8bbd0cec84a8f0f187d2","semester":"1262","observed_at":"2025-06-02 03:49:42.915428+00:00","record_version_id":"48ecf229a648ef9ee31fda921a007872a7cd7fb8d7116c67ea5f1eb5bb913bb2","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-51d86ed4082dced3803c295419b0ec8440942016","semester":"1264","observed_at":"2025-11-12 04:50:41.141307+00:00","record_version_id":"d3363666c52b4a3ea7d263eff29c80b894c068cbfacce66b2f128404bc562c6e","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-53101bd1c10db0d00d094e4c9154d4c244ff23d9","semester":"1262","observed_at":"2025-10-05 04:58:05.366968+00:00","record_version_id":"0eb43c20db6342c33801f05ec59734cceec329990ff53a2e00c04f642cdc3b47","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-58751151c564e11c5cb8aedd3a2d3527c22f73eb","semester":"1262","observed_at":"2025-06-01 06:59:01.536576+00:00","record_version_id":"8c8ba429ccc0fe5e19a0267f149c2a31d5a0b98722cd1aca44e6a40803fa4261","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-5c4c8d3701a333ba4ddadef7d0b5dde8440fcdf0","semester":"1262","observed_at":"2025-06-01 01:57:51.256704+00:00","record_version_id":"4a72f41c7c8e95b7364101d24a95795d6275588d157a3575e20ee1d333a8bdae","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-5d20d9f81651d3c83fcaa2fd10870ce6e5d5a685","semester":"1262","observed_at":"2025-05-21 16:48:48.484741+00:00","record_version_id":"b2ea26c18472aacaca635b97901b833cc7d518e467e590737e10f1e62fda9e4c","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-5feaebf2ab37051c86ad360cc0fa0d7e0e2ea295","semester":"1262","observed_at":"2025-05-24 15:58:23.378859+00:00","record_version_id":"85a5963ca9800ec887adb08bc8a67a424df14ff9f94a23fdbdc99527848a63de","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-60949965544fce098cc006354e201149005fb77b","semester":"1264","observed_at":"2025-10-26 05:07:14.077864+00:00","record_version_id":"290582f2f49cbb1e97830e4e9904b0152eefd3cc411e1e25feee1f9c56b31c92","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-639225ac3addf8522f1d30cb4fe26054ea7f8c49","semester":"1262","observed_at":"2025-06-01 08:14:42.497608+00:00","record_version_id":"920600172dfb33f5b62581d67cb70e6bf4af7021f4b8b3dd9e1862ef9219a5b1","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-64093b17b3a9c67a41aa33eb37adb53ec38df714","semester":"1262","observed_at":"2025-05-22 02:11:02.531191+00:00","record_version_id":"8f82213ecf455d20c7caea9f377c78568439dc93c424413ad81f52dd7a12dc32","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-6885b56099f55e08aa0205546369fb0381d50e13","semester":"1262","observed_at":"2025-07-13 04:59:22.367394+00:00","record_version_id":"190972fadebb522cb09f0722e00067c4e5b487cd2b44f3821f7712f662bf19e0","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-69d4b2b16e8268f75b6390b3a0e2ce8bf73999da","semester":"1262","observed_at":"2025-06-30 03:21:29.925498+00:00","record_version_id":"905c6fa7d38ab3e29ca224e1c74c2bc2f9aac0e3022e7cef9ea3c273b79be808","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-6b29c3e0673b2f9a736d2363db5409e03d5b3ed4","semester":"1264","observed_at":"2025-12-03 12:33:41.418072+00:00","record_version_id":"8d42415987cb2c52b2099da9dbf965e3177aa04c0b041836065804a80c2e53bf","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-73259984748de484976ef2ad56ad7ab3d4cb4518","semester":"1262","observed_at":"2025-06-26 08:27:56.461216+00:00","record_version_id":"883f245cf4fedd8332e513d25d581d3612ca2cd67e961206553494bdfbf7b4d4","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-741602fbc6252c232a0b9e6bc1a9ae3101625f5a","semester":"1262","observed_at":"2025-06-30 22:49:32.522034+00:00","record_version_id":"72b148414aaca5b45325e707108fa9e466e850eb2a532aebeeca849b248b4d99","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-7774f45558276ec29bf626e58fffb4a27bdbb2a9","semester":"1264","observed_at":"2025-11-01 11:49:36.359151+00:00","record_version_id":"9217a21756f70a466d0914bfcdc3cd7c1b4d6b34e358aebfd17d079b639adaf2","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-7ab7c5e783fc8ebef8b8ee8cea019ee37a72ce74","semester":"1262","observed_at":"2025-09-28 04:58:07.340658+00:00","record_version_id":"4fc05212b5615f6e82808cb1833841e28a6b330b70dae5f778ab1c45f4d6161d","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-7b2f22d83ea05055ab90dcb77dc802382cccc6f7","semester":"1262","observed_at":"2025-05-21 17:56:46.096741+00:00","record_version_id":"76b280c39c1863c9636e2875307bdc42ef958f2b1bed74efe8d9f4197b4c076e","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-7ca1175502be18eb51f7b016818a884a43fd4ebb","semester":"1262","observed_at":"2025-07-29 06:28:23.644795+00:00","record_version_id":"bf0386ed036ec189753c45fa77fa5f54b9c771a5a88b54133509807f1ecc9b1f","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-7d8cee5e42602ad9ba5dc0322085af2fa0dffaac","semester":"1262","observed_at":"2025-05-22 01:15:19.291664+00:00","record_version_id":"3aa3fbd0ebe9f122a063806183dde3334791cc65d859976019d3f4c1fc3775d7","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-81a0d6a7f668c98c59bd05fa988aa079c5ad1f68","semester":"1262","observed_at":"2025-05-31 07:49:50.419452+00:00","record_version_id":"efb970bced3d24bf88b41b796ab9b7cc700564b68dd99f11d9b9c43d7d51afed","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-82f31c08c746a214872494cde6ecaeb9330441b4","semester":"1262","observed_at":"2025-06-17 21:26:08.547156+00:00","record_version_id":"aa3769d9b12db3f6ac6e04e0f2d8d022d2c1ca3e4860ca2e76f7159f8078229f","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-84c248343c5a99ab42c5616c594812a5e3e31cbb","semester":"1262","observed_at":"2025-07-04 06:00:07.487827+00:00","record_version_id":"7de0f8614d09faee2138ac8ab4775fec1cea2e7a84474413b8241f1c2d908831","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-86334e7275e315853287e8580c35456c68df800b","semester":"1262","observed_at":"2025-05-22 06:53:03.336354+00:00","record_version_id":"749a594095a02b0c65a649afc64c9ed6156c49563f05a1c258a2bf1d7f9f2c69","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-8686dc1664f93a6c05f50321410961b39b12f91b","semester":"1262","observed_at":"2025-05-28 10:17:41.595894+00:00","record_version_id":"79101720cbd7ececd452c98fed886f6b7fbfacc5f1321a8207501b7c6a825402","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-86d941366886c9a14a2581c04e20d0bdd25e1b02","semester":"1262","observed_at":"2025-08-31 04:54:14.656039+00:00","record_version_id":"2748e944a1f7cec8a594af5fe2557c54ff2fd997790fc94b6ed64e5850876100","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-8ba1d2e6cfbb1525b4f9f769ec164fb6ad0f237a","semester":"1262","observed_at":"2025-06-02 05:22:45.010398+00:00","record_version_id":"f30eee40ff8e8928badc257766f4367895a4d2a303f44f7e80dd351a8e082aa4","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-90e5aa456689a3b222016c05c319bbcd0bddd453","semester":"1262","observed_at":"2025-05-25 02:00:10.037905+00:00","record_version_id":"85a389cb7d27a1c31bf0aa7145d1d2d69ce7a35130f4e0dfbc7e9f223e92f487","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-92d960aa7e4cd6e4c9ac8b2e232092904038e9e4","semester":"1262","observed_at":"2025-06-08 01:50:46.451351+00:00","record_version_id":"b5dfb2023c67ef6b970e1124ddefd55ce72031a50d5b24bb898183e011572c67","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-94002ae588a8f71eca6d64105f2e603e8587293b","semester":"1262","observed_at":"2025-05-21 09:01:16.021508+00:00","record_version_id":"4df27ad38af89529e61f123614347c030a6d50c469dc6658ccdf156c994ee7ef","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-9dd575e013c5c6e000ab3ad2d1288cc1dd9c96dc","semester":"1264","observed_at":"2025-12-21 04:47:46.589081+00:00","record_version_id":"1ed5877079022ca327025c2384f0659d8cf375bae33cf17be13a1af79e306f2b","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-a1ea26ed78cbd6e94bc3221e50a6ed9b6bf43871","semester":"1262","observed_at":"2025-05-25 09:16:55.180581+00:00","record_version_id":"aadea1681d9a1e0999406709f399221bd1658aaf3c377a966ac93cc93717ea19","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-a76a3505853d5ea9c13bb98472b87361d469e2ee","semester":"1262","observed_at":"2025-05-28 18:03:41.793846+00:00","record_version_id":"7b6c42076616184a3664c73de181eb8e360819ac746b997f8040095e90c0dfac","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-b285d591f6acf3aa1910e02edc297664db86eb8c","semester":"1262","observed_at":"2025-06-10 22:48:22.460295+00:00","record_version_id":"625484b98dbdff3e8cf9a733f20c6b169a5ac3710187cf9cae5cd5986424c700","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-b79af756e4f00ba98231feb859ed8e98239eb811","semester":"1262","observed_at":"2025-05-22 02:47:56.931581+00:00","record_version_id":"5184f1c646a1c68344fb6971b919578b7982966b37628ba80d30f1cfce156cce","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-b89e05b94730c6471439082d9279c0a834a4aeec","semester":"1262","observed_at":"2025-08-26 20:25:42.325239+00:00","record_version_id":"2aef3bd8352304e886074064755157223e157cc49ad040500e394b03f106159c","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-b9279a12d1f2688df9e556183c94d41a89e431dc","semester":"1262","observed_at":"2025-07-06 01:49:47.203835+00:00","record_version_id":"5dcbc7b22bb146ac2df4dbca30391003321262ace1aea0c73ec49baf74df1c5d","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-bbdc905e96cc681a5e9a8b02c795b682206e36d1","semester":"1262","observed_at":"2025-06-25 08:02:45.445730+00:00","record_version_id":"7e02bf8339bd38d452b00b1dfeb77498572538a82d7e66d8dbd3ad91e5e62707","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-bde225b5243a13d81827aa173e39c78a652b54a0","semester":"1262","observed_at":"2025-05-23 07:22:33.219345+00:00","record_version_id":"8c5aa6777cdf92cefdd84766a700dca9d3f915a7c10fbcb10767f0da5ef166dc","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-c1fac4cbfde737f4f4ac02cdf15e6f5a9b5b676d","semester":"1262","observed_at":"2025-05-22 01:15:19.291664+00:00","record_version_id":"3aa3fbd0ebe9f122a063806183dde3334791cc65d859976019d3f4c1fc3775d7","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-c6b62be45f8a13cba109ef2a7741b9cd9a6b8272","semester":"1262","observed_at":"2025-08-24 08:48:09.035112+00:00","record_version_id":"c3cbaa65b0bb00ea8d0b3eb38d864a5523c5714293013d6617b2b6349c4a0c80","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-cc83f62cb6db61f39320916d819ad48ce55cd96d","semester":"1262","observed_at":"2025-05-22 00:45:35.735230+00:00","record_version_id":"8d7a7355615d5c812fd3ec21515747b99633c70779499ba5178c35a9ff4d3027","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-d33818a01b6b4c5e2902e704da6e5b713742d4dc","semester":"1262","observed_at":"2025-10-13 10:46:24.251475+00:00","record_version_id":"ee9340803f68a832ec1b41bedb3dcb5390a455bada9f9348c60b377657f15aec","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-d3c17db9832d3064916046caf891dd23f10b1bb9","semester":"1262","observed_at":"2025-08-26 11:30:42.288639+00:00","record_version_id":"d27e83522303c48a98b976c3386cb43d72a334c184688c6904e4c16932333b47","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-d4ee226dc44268e9b6767fb9f5f20343322f494b","semester":"1262","observed_at":"2025-06-15 01:52:23.911697+00:00","record_version_id":"b7049b0b12a96e863c51e901e826cb32b12f8ef5c1e0704a87dbe49af55d2901","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-d8f9fc7f136e2033556d9d65bbdbdf33425dbdc9","semester":"1262","observed_at":"2025-05-28 16:48:12.400438+00:00","record_version_id":"949ab6daf282616b5698fd8dc3d279559746d5a935d60326e9275728b51a7a59","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-dbbc9739c616c3846b2e796e00a36fa41225f7d8","semester":"1262","observed_at":"2025-06-01 17:47:19.325144+00:00","record_version_id":"4dfe4d41d3e6edbc2ba8e04588e3411bcf16fd36392420f0b76ea041aa94adb2","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-dbe5f2d24b48c3177297a3d69c553cc9d1194bac","semester":"1262","observed_at":"2025-09-14 05:00:27.366070+00:00","record_version_id":"40d33b0933ee4a8b4d72d02884f82685ae43624bb147dcd6b81cc6044935f16f","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-dd357dc2758e9e282c30988a527ce37de92593b9","semester":"1264","observed_at":"2025-10-19 01:39:41.951404+00:00","record_version_id":"15f737b556862b928d95e820f3a9a10868ec6d6f0174759e6dbcb87645553f5d","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-de74de559e409a3fe668685e28043b888d2e6841","semester":"1262","observed_at":"2025-06-27 07:38:50.882897+00:00","record_version_id":"4a733c92fcfee8aa4ae0791bfb6fbe17521a79f9b563030e5fba9e27b36c5578","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-deca7188cf536ade3203cd88b52a45e92a059617","semester":"1262","observed_at":"2025-06-27 00:37:46.087071+00:00","record_version_id":"3c72968a28294c3f6f0ab78c414166cdef79673d887e35068300ac9aafa1f2a6","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-df60fa9ac3931c5612b0fa641b7a6f5fbec86fab","semester":"1262","observed_at":"2025-05-24 06:36:37.554678+00:00","record_version_id":"b36d91b45d2c2d26546af211f210d09a9d8937b5ceb63378303eca63259948c1","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-e339da2d849aaf8bb4dceca07a84728af23cf755","semester":"1262","observed_at":"2025-07-23 08:49:22.748925+00:00","record_version_id":"c3cbaa65b0bb00ea8d0b3eb38d864a5523c5714293013d6617b2b6349c4a0c80","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-e934b1784835f5d36ae9b7ef0992b11e72c39142","semester":"1262","observed_at":"2025-06-01 06:21:25.219533+00:00","record_version_id":"4fd50416aa6821d628ef0ceb6273422bc7c0dff33f860b2afbce17191e846319","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-ef471690c082448deebd2687eab84b0b78813c36","semester":"1262","observed_at":"2025-06-29 01:47:56.317645+00:00","record_version_id":"57e50a4acd771c216e8286cde837549e5051804edd24f9823896a89b5be02c8e","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-efc4e8e2cd7c3f68e059a308990c00c77c7611eb","semester":"1264","observed_at":"2025-10-16 05:48:13.935141+00:00","record_version_id":"9b3be90c977736ccf6316aaa1f7aa14a1aa1089e75243cbc42d17ea81343424d","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"},{"run_id":"legacy-f18b7749725097d41f69c60779ff38cf073b93b3","semester":"1262","observed_at":"2025-05-21 19:52:03.259353+00:00","record_version_id":"3aa3fbd0ebe9f122a063806183dde3334791cc65d859976019d3f4c1fc3775d7","course_id":"STAT 606","course_uid":"course_cdc21e46ef21cd0cb632076c","catalog_version_id":"fe1c0401af14860869c99fb6f904e53df04fc717a4be23826f6a9a02991745d6","course_number":606,"subjects":["STAT"],"title":"COMPUTING IN DATA SCIENCE AND STATISTICS","description":"A survey of some of the tools and frameworks that are currently popular among data scientists and statisticians working in both academia and industry. Begins with an accelerated introduction to the Python programming language and brief introductions to object-oriented and functional programming. Covers some of the scientific computing platforms available in Python, including tools for numerical and scientific computing; training basic machine learning models; and data visualization. Discusses collecting data from the web both by scraping and using APIs. Concludes with a brief survey of distributed computing platforms, focusing on the MapReduce framework.","requirements_text":"Declared in Statistics MS or Statistics VISP (undergraduate)"}]