Wiki - Факультет компьютерных наук - Вклад [ru]

Data Science for Business 2020

2020-04-13T07:41:10Z

Anvar: edit schedule

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 Fridays, 6.10pm - 7.30pm
* Seminars link https://zoom.us/j/636910206 Fridays, 7.40pm - 9.00pm

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Pricing|| The goal of the case is to compute price elasticity.
'''Algorithms''': Supervised learning: linear and non-linear regression, predicting continuous variable. Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Customer segmentation ||The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': clustering – k-means, agglomerative, dimensionality reduction - PCA.
|-
| 7 || 05.06.2020||Case study 4: Personalizaton ||The goal of the case is to build a recommender system.
'''Algorithms''': association rules and collaborative filtering.
|-
| 8 || 12.06.2020 || Case study 5. Demand forecasting ||The goal of the case is to develop demand forecasting model.
'''Algorithms''': ARIMA, sliding window regression.
|-
| 9 || 19.06.2020 || Case study 6. Fraud detection ||The goal of the case is to find abnormal customer transactions.
'''Algorithms''': anomaly detection.
|-
| 10 || 26.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Seminar's materials===

[https://yadi.sk/i/eJm4z1dwLOAXhw Seminar 1. ]

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===

*For online lectures and seminars. [https://zoom.us/ zoom]
*Modelling package. [https://rapidminer.com/ RapidMiner]
 Apply for educational version https://rapidminer.com/get-started-educational/ 

*Email: Enter your university email (end with @edu.hse.ru)
*Job Function: Student
*University: Higher School of Economics
*Course Name: Data Science for Business
*Course Number: https://www.hse.ru/edu/courses/341840822
*Course Term: Summer Term
*Professor: Leonid Zhukov

Data Science for Business 2020

2020-04-10T18:35:43Z

Anvar:

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 Fridays, 6.10pm - 7.30pm
* Seminars link https://zoom.us/j/636910206 Fridays, 7.40pm - 9.00pm

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Seminar's materials===

[https://yadi.sk/i/eJm4z1dwLOAXhw Seminar 1. ]

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===

*For online lectures and seminars. [https://zoom.us/ zoom]
*Modelling package. [https://rapidminer.com/ RapidMiner]
 Apply for educational version https://rapidminer.com/get-started-educational/ 

*Email: Enter your university email (end with @edu.hse.ru)
*Job Function: Student
*University: Higher School of Economics
*Course Name: Data Science for Business
*Course Number: https://www.hse.ru/edu/courses/341840822
*Course Term: Summer Term
*Professor: Leonid Zhukov

Data Science for Business 2020

2020-04-10T18:35:31Z

Anvar:

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 Fridays, 6.10pm - 7.30pm
* Seminars link https://zoom.us/j/636910206 Fridays, 7.40pm - 9.00pm

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

==Seminar's materials==

[https://yadi.sk/i/eJm4z1dwLOAXhw Seminar 1. ]

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===

*For online lectures and seminars. [https://zoom.us/ zoom]
*Modelling package. [https://rapidminer.com/ RapidMiner]
 Apply for educational version https://rapidminer.com/get-started-educational/ 

*Email: Enter your university email (end with @edu.hse.ru)
*Job Function: Student
*University: Higher School of Economics
*Course Name: Data Science for Business
*Course Number: https://www.hse.ru/edu/courses/341840822
*Course Term: Summer Term
*Professor: Leonid Zhukov

Data Science for Business 2020

2020-04-10T18:35:13Z

Anvar: add seminar 1 recording

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 Fridays, 6.10pm - 7.30pm
* Seminars link https://zoom.us/j/636910206 Fridays, 7.40pm - 9.00pm

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Seminar's materials===

[https://yadi.sk/i/eJm4z1dwLOAXhw Seminar 1. ]

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===

*For online lectures and seminars. [https://zoom.us/ zoom]
*Modelling package. [https://rapidminer.com/ RapidMiner]
 Apply for educational version https://rapidminer.com/get-started-educational/ 

*Email: Enter your university email (end with @edu.hse.ru)
*Job Function: Student
*University: Higher School of Economics
*Course Name: Data Science for Business
*Course Number: https://www.hse.ru/edu/courses/341840822
*Course Term: Summer Term
*Professor: Leonid Zhukov

Технологии анализа больших данных ГМУ 1 курс 2019/2020

2020-04-07T07:27:55Z

Anvar:

== О курсе ==
Курс читается на 1 курсе ОП "Государственное и муниципальное управление", в 4 модуле.
==Преподаватели==
===Лекции===
Бурова Маргарита Борисовна

* [mailto:mbburova@gmail.com E-mail]
* [https://vk.com/burrita VK Маргарита Бурова]
* telegram: @burritas

===Семинары===

{| class="wikitable"
|-
! Группа !! Семинарист !! Ассистент
|-
| 191 || Бурова Маргарита Борисовна || [https://teleg.run/avdeevanika Авдеева Ника]
|-
| 192 || Бурова Маргарита Борисовна || [https://vk.com/tigger_roo Веселова Кристина]
|-
| 193|| [https://www.hse.ru/staff/intergalactic_admiral/ Курмуков Анвар Илдарович]|| Анна Глухова
|-
| 194|| [https://www.hse.ru/staff/intergalactic_admiral/ Курмуков Анвар Илдаровчи] || Марк Ермаков
|-
| 195|| ||
|-
| 194|| ||
|}

== Материалы курса ==
===Лекции===
{| class="wikitable"
|-
! № !! Дата лекции!! Тема !! Презентация
|-
| 1 || || ||
|-
| 2 || || ||
|-
| 3 || . || ||
|-
| 4 || || ||
|-
| 5 || || ||
|-
| 6 || || ||
|-
| 7 || || ||
|-
| 8 || || ||

|}

===Семинары===

{| class="wikitable"
|-
! № !! Дата семинара!! Тема !! Материалы к занятию
|-
| 1 || || ||
|-
| 2 || || ||
|-
| 3 || || ||
|-
| 4 || || ||
|-
| 5 || || ||
|-
| 6 || || ||
|-
| 6 || || ||
|-
| 7 || ||||
| 8 || || ||
|-
| 9 || || ||
|-
| 10 || || ||

|}
===Установка анаконды===
Мы рекомендуем вам пользоваться дистрибутивом (сборкой) Anaconda https://www.anaconda.com/download/, выбираем вариант Python или 3.7 version и нажимаем "Download". Запускаем exe-шный файл и следуем инструкциям установки.

Важно!!!

В процессе установки не забыть и нажать галочку "add to PATH"

Ура, у вас установился дистрибутив Python с целым рядом нужных библиотек!

Но вполне вероятно, что мы вас попросим установить какие-то еще библиотеки для курса. Если это понадобится, делаем следующее:

1) находим с помощью поиска по системе программку "anaconda promt"

2) набираем в появившейся строке conda install -c anaconda pip (это менеджер библиотек на питоне, через него обычно ставятся новые библиотеке)

3) набираем там же pip install <имя нужной библиотеки>
===Тесты===

===Домашние задания===
Домашние задания сдаются в систему [https://anytask.org anytask] (потребуется зарегистрироваться).

Все дедлайны и задания будут публиковаться там.

Как зарегистрироваться на курс:
# на главной странице выбрать Высшая школа экономики
# найти курс Технологии анализа больших данных
# ввести инвайт для своей группы.

Инвайты по группам:
{| class="wikitable"
|-
! № группы !! инвайт
|-
| 191 ||
|-
| 192 ||
|-
| 193 ||
|-
| 194 ||
|-
| 195 ||
|-
| 193 ||
|}

Технологии анализа больших данных ГМУ 1 курс 2019/2020

2020-04-07T07:27:04Z

Anvar: add 193,4 info

== О курсе ==
Курс читается на 1 курсе ОП "Государственное и муниципальное управление", в 4 модуле.
==Преподаватели==
===Лекции===
Бурова Маргарита Борисовна

* [mailto:mbburova@gmail.com E-mail]
* [https://vk.com/burrita VK Маргарита Бурова]
* telegram: @burritas

===Семинары===

{| class="wikitable"
|-
! Группа !! Семинарист !! Ассистент
|-
| 191 || Бурова Маргарита Борисовна || [https://teleg.run/avdeevanika Авдеева Ника]
|-
| 192 || Бурова Маргарита Борисовна || [https://vk.com/tigger_roo Веселова Кристина]
|-
| 193|| Курмуков Анвар Илдарович|| Анна Глухова
|-
| 194|| Курмуков Анвар Илдаровчи || Марк Ермаков
|-
| 195|| ||
|-
| 194|| ||
|}

== Материалы курса ==
===Лекции===
{| class="wikitable"
|-
! № !! Дата лекции!! Тема !! Презентация
|-
| 1 || || ||
|-
| 2 || || ||
|-
| 3 || . || ||
|-
| 4 || || ||
|-
| 5 || || ||
|-
| 6 || || ||
|-
| 7 || || ||
|-
| 8 || || ||

|}

===Семинары===

{| class="wikitable"
|-
! № !! Дата семинара!! Тема !! Материалы к занятию
|-
| 1 || || ||
|-
| 2 || || ||
|-
| 3 || || ||
|-
| 4 || || ||
|-
| 5 || || ||
|-
| 6 || || ||
|-
| 6 || || ||
|-
| 7 || ||||
| 8 || || ||
|-
| 9 || || ||
|-
| 10 || || ||

|}
===Установка анаконды===
Мы рекомендуем вам пользоваться дистрибутивом (сборкой) Anaconda https://www.anaconda.com/download/, выбираем вариант Python или 3.7 version и нажимаем "Download". Запускаем exe-шный файл и следуем инструкциям установки.

Важно!!!

В процессе установки не забыть и нажать галочку "add to PATH"

Ура, у вас установился дистрибутив Python с целым рядом нужных библиотек!

Но вполне вероятно, что мы вас попросим установить какие-то еще библиотеки для курса. Если это понадобится, делаем следующее:

1) находим с помощью поиска по системе программку "anaconda promt"

2) набираем в появившейся строке conda install -c anaconda pip (это менеджер библиотек на питоне, через него обычно ставятся новые библиотеке)

3) набираем там же pip install <имя нужной библиотеки>
===Тесты===

===Домашние задания===
Домашние задания сдаются в систему [https://anytask.org anytask] (потребуется зарегистрироваться).

Все дедлайны и задания будут публиковаться там.

Как зарегистрироваться на курс:
# на главной странице выбрать Высшая школа экономики
# найти курс Технологии анализа больших данных
# ввести инвайт для своей группы.

Инвайты по группам:
{| class="wikitable"
|-
! № группы !! инвайт
|-
| 191 ||
|-
| 192 ||
|-
| 193 ||
|-
| 194 ||
|-
| 195 ||
|-
| 193 ||
|}

Data Science for Business 2020

2020-03-28T18:39:59Z

Anvar: add RM download instructions

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 Fridays, 6.10pm - 7.30pm
* Seminars link https://zoom.us/j/636910206 Fridays, 7.40pm - 9.00pm

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===

*For online lectures and seminars. [https://zoom.us/ zoom]
*Modelling package. [https://rapidminer.com/ RapidMiner]
 Apply for educational version https://rapidminer.com/get-started-educational/ 

*Email: Enter your university email (end with @edu.hse.ru)
*Job Function: Student
*University: Higher School of Economics
*Course Name: Data Science for Business
*Course Number: https://www.hse.ru/edu/courses/341840822
*Course Term: Summer Term
*Professor: Leonid Zhukov

Data Science for Business 2020

2020-03-28T18:13:49Z

Anvar: add lectures, seminars links

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Links===
* Alternative Course website [weblink]
* Lectures link https://zoom.us/j/7723819319 (Fridays, 6.10pm - 7.30pm)
* Seminars link https://zoom.us/j/636910206 (Fridays, 7.40pm - 9.00pm)

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===
*Modelling package. [https://rapidminer.com/ RapidMiner]
*For online lectures and seminars. [https://zoom.us/ zoom]

Data Science for Business 2020

2020-03-28T18:04:59Z

Anvar: minor additions

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4.

Department of Data Analysis and Artificial Intelligence, School of Computer Science.
 Join our telegram channel [https://t.me/joinchat/ENzQEhr-hra2WhEjxvgayw Data science for business.]

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov] Alternative Course website [weblink]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===
[https://rapidminer.com/ RapidMiner]

Data Science for Business 2020

2020-03-28T17:48:00Z

Anvar: First draft

== About the Course ==

Data Science for Business. MAGoLEGO course.

Spring 2020. Module 4

Department of Data Analysis and Artificial Intelligence, School of Computer Science.

===Instructors===

[https://www.hse.ru/staff/lzhukov Prof. Leonid Zhukov]

[https://www.hse.ru/staff/iamakarov Ilya Makarov]

[https://www.hse.ru/staff/intergalactic_admiral/ Anvar Kurmukov]

===Course outline===

* Introduction to data science
* Data mining, statistics, machine learning, optimization
* Case studies
* Increasing business impact

===Content===

{| class="wikitable"
|-
! № !! Date !! Title !! Abstract
|-
| 1 || 10.04.2020 || Introduction to data science. || Introduction to data science and its role in industry. Examples of real world use cases.
|-
| 2 ||17.04.2020 || Working with data. || Data cleaning and preparation. ETL process. Basic data analysis and visualization.
|-
| 3 || 24.04.2020 || Data mining, machine learning, statistics || Types of ML algorithms, applicability, training and testing, solution quality.
|-
| 4 || 15.05.2020 || Case study 1: Customer segmentation|| The goal of the case is to group customers into clusters based on some customer similarity metrics.
'''Algorithms''': Unsupervised learning. Clustering: k-means, agglomerative; Dimensionality reduction: PCA.
|-
| 5 || 22.05.2020 ||Case study 2: Churn modeling||The goal of the case is to predict which customers are going to leave the service within a given time.
'''Algorithms''': Supervised learning. Classification: Logistic regression, Decision trees, Random forest.
|-
| 6 || 29.05.2020 ||Case study 3: Pricing||The goal of the case is to determine the optimal pricing for goods and services.
'''Algorithms''': Supervised learning. Regression: linear and non-linear models.
|-
| 7 || 05.06.2020||Case study 4: Industrial analytics||The goal of the case is to predict an output of the production line and find optimal parameter setting.
'''Algorithms''': Supervised learning. Regression: non-linear optimization.
|-
| 8 || 12.06.2020 || Case study 5. Sales territory design ||The goal of the case is to select locations of the sales offices to maximize the coverage under constrained resources.
'''Algorithms''': clustering and geo-analytics approaches.
|-
| 9 || 19.06.2020 ||Impacting the business ||How to create a visible impact on business with analytics
|-
|}

===Textbooks===

*Provost, Foster, Fawcett, Tom. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.", 2013.
*James, G. et al. An introduction to statistical learning. Springer, 2013.
*Siegel, E. Predictive analytics: The power to predict who will click, buy, lie, or die. John Wiley & Sons, 2016.

===Software===
[https://rapidminer.com/ RapidMiner]