Просмотр исходного текста страницы Анализ Данных 22/23 Политологи

== О курсе ==
"Анализ данных в Python" читается на 3 курсе, в 3 и 4 модуле.

* [https://t.me/+9AazKY4rqOMxMTFi Чат курса в Telegram]
* [https://us02web.zoom.us/j/82353456596?pwd=eHg3dzJMdUNqWHc3emNKNVpIWHdZZz09 Zoom]

==Преподаватель==
Бурова Маргарита Борисовна
* [mailto:mbburova@gmail.com E-mail]

==Ассистент==
Лика Капустина
* @lika_kapustina в Telegram

== О курсе ==
"Анализ данных в Python" читается на 3 курсе, в 3 и 4 модуле.

* [https://t.me/+9AazKY4rqOMxMTFi Чат курса в Telegram]
* [https://us02web.zoom.us/j/82353456596?pwd=eHg3dzJMdUNqWHc3emNKNVpIWHdZZz09 Zoom]

==Преподаватель==
Бурова Маргарита Борисовна
* [mailto:mbburova@gmail.com E-mail]

==Ассистент==
Лика Капустина
* @lika_kapustina в Telegram

==Материалы==
Записи занятий на youtube: 
Репозиторий с материалами: 



{| class="wikitable"
|-
! Дата !! Презентация !! Тетрадка !! Запись
|-
| 21.01.2023 ||  || || [https://youtu.be/PP0e5-GKiVA Семинары 1-2]
|-
| 04.02.2023 || [https://docs.google.com/presentation/d/1CFkgHB9b-ceLfX0dLeUgn0FqH98_dvLwCksB0RdHN_g/edit?usp=sharing Регрессия]
 || [https://drive.google.com/file/d/1hYc5SWPeMcN1RbpsPJg14FYp8pEu0I70/view?usp=sharing Продолжение Pandas] [https://drive.google.com/file/d/1eKLBYtBB68U7opNaFPyTMuzDiqEc2kWA/view?usp=sharing Данные]|| [https://youtu.be/cuu07hf5TYE Семинары 3-4]
|-
| 11.02.2023 ||  || [https://drive.google.com/drive/folders/1CjTLEJOmEikLxE5wnVpBmgiIpfuz5X-k?usp=sharing Папка с регрессией]||
|-
| 18.02.2023 || [https://docs.google.com/presentation/d/18sBX1MQVwL0Re5aaKgCzLtF3NFpbbX86e-c5yEvvsQs/edit?usp=sharing Классификация]  || [https://drive.google.com/drive/folders/1FrGLq1m7t83jnsMB9qtyTeHpp2-Pdz6N?usp=sharing Папка с материалами]||
|-
| 25.02.2023 ||   || [https://drive.google.com/drive/folders/1Yso5JTwik4QMz1QkV6Udo9e_b-wYrdZ4?usp=sharing Папка с материалами]||
|}



===Контрольные работы===

== 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]

====Python====
* [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]
* [http://www.machinelearning.ru/wiki/index.php?title=Машинное_обучение_%28курс_лекций%2C_К.В.Воронцов%29 Курс по машинному обучению К.В. Воронцова]
* [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]

== 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]

====Python====
* [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]
* [http://www.machinelearning.ru/wiki/index.php?title=Машинное_обучение_%28курс_лекций%2C_К.В.Воронцов%29 Курс по машинному обучению К.В. Воронцова]
* [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]