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| − | == О курсе ==
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| − | "Анализ данных в Python" читается на 3 курсе, в 3 и 4 модуле.
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| − | * [https://t.me/+9AazKY4rqOMxMTFi Чат курса в Telegram]
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| − | * [https://us02web.zoom.us/j/82353456596?pwd=eHg3dzJMdUNqWHc3emNKNVpIWHdZZz09 Zoom]
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| − | ==Преподаватель==
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| − | Бурова Маргарита Борисовна
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| − | * [mailto:mbburova@gmail.com E-mail]
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| − | | + | |
| − | ==Ассистент==
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| − | Лика Капустина
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| − | * @lika_kapustina в Telegram
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| − | | + | |
| − | == О курсе ==
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| − | "Анализ данных в Python" читается на 3 курсе, в 3 и 4 модуле.
| + | |
| − | | + | |
| − | * [https://t.me/+9AazKY4rqOMxMTFi Чат курса в Telegram]
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| − | * [https://us02web.zoom.us/j/82353456596?pwd=eHg3dzJMdUNqWHc3emNKNVpIWHdZZz09 Zoom]
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| − | | + | |
| − | ==Преподаватель==
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| − | Бурова Маргарита Борисовна
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| − | * [mailto:mbburova@gmail.com E-mail]
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| − | | + | |
| − | ==Ассистент==
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| − | Лика Капустина
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| − | * @lika_kapustina в Telegram
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| − | ==Материалы==
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| − | Записи занятий на youtube:
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| − | Репозиторий с материалами:
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| − | {| class="wikitable"
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| − | |-
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| − | ! Дата !! Презентация !! Тетрадка
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| − | | 21.01.2023 || ||
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| − | | 04.02.2023 || [https://docs.google.com/presentation/d/1CFkgHB9b-ceLfX0dLeUgn0FqH98_dvLwCksB0RdHN_g/edit?usp=sharing Регрессия]
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| − | || [https://drive.google.com/file/d/1hYc5SWPeMcN1RbpsPJg14FYp8pEu0I70/view?usp=sharing Продолжение Pandas] [https://drive.google.com/file/d/1eKLBYtBB68U7opNaFPyTMuzDiqEc2kWA/view?usp=sharing Данные]
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| − | | 11.02.2023 || || [https://drive.google.com/drive/folders/1CjTLEJOmEikLxE5wnVpBmgiIpfuz5X-k?usp=sharing Папка с регрессией]
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| − | |}
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| − | | + | |
| − | ===Контрольные работы===
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| − | == Список рекомендуемых материалов ==
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| − | ==== Линейная алгебра и статистика ====
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| − | * [https://web.stanford.edu/~hastie/ElemStatLearn/ Hastie, Tibshirani, Friedman. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction.]
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| − | * [http://people.math.umass.edu/~lavine/Book/book.pdf Lavine. (2013). Introduction to Statistical Thought]
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| − | * [https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ MIT Open course: Linear Algebra by Strang]
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| − | ====Python====
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| − | * [https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf Pandas Cheat Sheet]
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| − | * [http://www.pyregex.com/ Python RegExp Cheat Sheet]
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| − | * [http://opencarts.org/sachlaptrinh/pdf/28232.pdf O'Reilly: Python for Data Analysis]
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| − | * [https://stepik.org/course/67/syllabus Базовый курс по программированию на Stepik]
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| − | ==== Machine learning ====
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| − | * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop. (2006). Pattern Recognition and Machine Learning]
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| − | * [http://www.machinelearning.ru/wiki/index.php?title=Машинное_обучение_%28курс_лекций%2C_К.В.Воронцов%29 Курс по машинному обучению К.В. Воронцова]
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| − | * [https://www.coursera.org/learn/machine-learning Coursera: Machine Learning by Andrew Ng]
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| − | * [http://shop.oreilly.com/product/0636920018483.do O'Reilly: Machine Learning for Hackers]
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| − | ==== Материала по ML ====
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| − | * [https://www.kaggle.com/learn/machine-learning Simple Decision Tree]
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| − | * [https://books.google.ru/books?id=vbQlDQAAQBAJ&dq=Introduction+to+Machine+Learning+with+Python:+A+Guide+for+Data+Scientists&hl=ru&source=gbs_navlinks_s Andreas C. Müller, Sarah Guido. Introduction to Machine Learning with Python: A Guide for Data Scientists]
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| − | ====Статистика====
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| − | * Бослав С. Статистика для всех (Есть в библиотеке)
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| − | * Gravetter F, Wallnau L. Statistics for behavioral sciences (Есть в библиотеке)
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| − | | + | |
| − | ==== Web scraping ====
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| − | * [http://shop.oreilly.com/product/0636920028468.do O'Reilly: RESTful Web APIs]
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| − | | + | |
| − | == Список рекомендуемых материалов ==
| + | |
| − | | + | |
| − | ==== Линейная алгебра и статистика ====
| + | |
| − | * [https://web.stanford.edu/~hastie/ElemStatLearn/ Hastie, Tibshirani, Friedman. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction.]
| + | |
| − | * [http://people.math.umass.edu/~lavine/Book/book.pdf Lavine. (2013). Introduction to Statistical Thought]
| + | |
| − | * [https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/ MIT Open course: Linear Algebra by Strang]
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| − | | + | |
| − | ====Python====
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| − | * [https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf Pandas Cheat Sheet]
| + | |
| − | * [http://www.pyregex.com/ Python RegExp Cheat Sheet]
| + | |
| − | * [http://opencarts.org/sachlaptrinh/pdf/28232.pdf O'Reilly: Python for Data Analysis]
| + | |
| − | * [https://stepik.org/course/67/syllabus Базовый курс по программированию на Stepik]
| + | |
| − | | + | |
| − | ==== Machine learning ====
| + | |
| − | * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop. (2006). Pattern Recognition and Machine Learning]
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| − | * [http://www.machinelearning.ru/wiki/index.php?title=Машинное_обучение_%28курс_лекций%2C_К.В.Воронцов%29 Курс по машинному обучению К.В. Воронцова]
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| − | * [https://www.coursera.org/learn/machine-learning Coursera: Machine Learning by Andrew Ng]
| + | |
| − | * [http://shop.oreilly.com/product/0636920018483.do O'Reilly: Machine Learning for Hackers]
| + | |
| − | | + | |
| − | ==== Материала по ML ====
| + | |
| − | * [https://www.kaggle.com/learn/machine-learning Simple Decision Tree]
| + | |
| − | * [https://books.google.ru/books?id=vbQlDQAAQBAJ&dq=Introduction+to+Machine+Learning+with+Python:+A+Guide+for+Data+Scientists&hl=ru&source=gbs_navlinks_s Andreas C. Müller, Sarah Guido. Introduction to Machine Learning with Python: A Guide for Data Scientists]
| + | |
| − | | + | |
| − | ====Статистика====
| + | |
| − | * Бослав С. Статистика для всех (Есть в библиотеке)
| + | |
| − | * Gravetter F, Wallnau L. Statistics for behavioral sciences (Есть в библиотеке)
| + | |
| − | | + | |
| − | ==== Web scraping ====
| + | |
| − | * [http://shop.oreilly.com/product/0636920028468.do O'Reilly: RESTful Web APIs]
| + | |
