Wiki - Факультет компьютерных наук - Вклад [ru]

Data Science Case Studies (JD SAS) 21/22

2022-02-02T20:25:03Z

Fvderyabin: перевод на англ

[[Файл:Capture.PNG|1000px|безрамки|справа]]

=== Расписание занятий ===
Занятия проводятся по '''субботам''' 

'''Общая ссылка для всех лекций и семинаров:''' https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 

Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 
 
'''''9:30''''' - Семинар на русском - ФЭН 3 курс (совместно с БК ГК "Открытие") 
'''''11:10''''' - Семинар на русском - ФЭН 4 курс (совместно с БК ГК "Открытие") 
'''''13:00''''' - Лекция на английском (запись) - ПАД и МИЭФ 
'''''14:40''''' - Лекция на русском (запись) - ПМИ, ФЭН, МК 
'''''16:20''''' - Семинар на русском (запись) - ПМИ и МК 
'''''18:10''''' - Семинар на английском (запись) - ПАД1 и МИЭФ 
'''''19:40''''' - Семинар на английском - ПАД2 
 

При посещении лекции и семинара в Zoom просим студентов '''сделать Rename''' и подписаться в формате «Префикс_Фамилия Имя», указав один из префиксов (МИЭФ, МК, ПАД, ПМИ, ФЭН). Например, «БИ_Иванов Алексей»

=== О курсе ===
Данная страничка содержит ссылки на материалы по курсу в 2021/2022 учебном году на потоке образовательных программ :
 

{| class="wikitable"
! Образовательная программа !! Курс !! Факультет !! Ссылка на страницу курса АДвБ
|-
| [https://cs.hse.ru/ami Прикладная Математика и Информатика] || 3 курс || Факультета Компьютерных Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/339498590 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/data/about Прикладной анализ данных] || 3 курс || Факультета Компьютерных Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/339492551 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/economics/ Экономика] || 3 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904614 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/economics/ Экономика] || 4 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904606 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/stat/ Экономика и статистика] || 3 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904618 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/stat/ Экономика и статистика] || 4 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904610 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/icef// Программа двух дипломов по экономике НИУ ВШЭ и Лондонского университета] || 3 курс || Международный институт экономики и финансов НИУ ВШЭ || нет
|}
 
дополнительные ссылки:
*[https://cs.hse.ru/sas/ Базовая кафедра компании SAS на факультете компьютерных наук ВШЭ ]
*[https://cs.hse.ru/electives/sas_dscs Карточка курса и программа]
*[https://www.sas.com/ru_ru/home.html Сайт компании SAS]

*'''[https://www.sas.com/ru_ru/careers/internship-program.html Календарь стажерской программы на 2021/2022 год]'''

== Программа курса==

'''Первый модуль''' будет читаться для 3 и 4 курса - обзор разделов:
* Клиентская аналитика;
* Текстовая аналитика;
* Задачи анализа данных в розничных сетях продаж товаров;
* Основы оценки рисков;
* ModelOps.

Первый модуль сможет погрузить студентов в актуальные задачи в бизнесе, а также в особенности анализа данных и построения аналитических моделей по каждому разделу модуля.
В этом модуле студентов познакомят с программным обеспечением SAS.

'''Второй модуль''' – командный проект только для 3 курса.

Студенты будут разделены на группы по 2-7 человек и каждой группе будет дана практическая задача.
Данный модуль позволит получить студентам практический опыт в анализе данных, разработке и в построении аналитических моделей на реальных данных.

== Studying and using SAS software in «Data Analytics in Business» course ==

To perform practical tasks, a student is free of choice to pick any of the given software tools: SAS, R, Python.

'''Students who plan to perform practical tasks on SAS platform''', may take advanced online courses for free.

''To access the course, you must contact the course instructor - Natalia Titova via Telegram.''

Links to access the software SAS - 
https://sas-viya.cs.hse.ru/SASStudioV/main?locale=en_US 
https://sas-viya.cs.hse.ru/SASStudioV/main?locale=ru_RU

'''If a student has completed all the practical tasks on the SAS and passed the course with excellence''', then he will receive:
*academic SAS program completion certificate
*Acclaim electronic badge confirming completion of the course and a list of technologies used by SAS

All interested students can take basic SAS online courses for free:
*basics of programming on SAS Base [https://support.sas.com/edu/schedules.html?id=2588&ctry=RU link to the course]
*basics of statistical analysis using SAS software[https://support.sas.com/edu/schedules.html?id=5235&ctry=RU link to the course]

Students who are willing to spend extra time learning to program in SAS can try to take a professional certification within the SCYP program for free (SAS® Software Certified Young Professionals) [https://www.sas.com/sas/training/scyp.html link to the course].

== Лекции ==

'''суббота '''
{| class="wikitable"
''' ПАД и МИЭФ - 13:00 - 14:30; 
'''ПМИ, Экономика, Экономика и статистика, МК - 14:40 - 16:10 ''' 
 
'''ссылка на подключение к лекции''' - https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 
Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 
|-
! Название раздела !! Тема !! Дата для 3 и 4 курса !! Презентация!! Запись
|-
| Клиентская аналитика || Введение в клиентскую и онлайн аналитику || 15.01.2022 || || [https://drive.google.com/file/d/1nEbogpqbZBBrjOJy-sVmq4SdVn_svef6/view?usp=sharing Лекция №1 - рус]
|-
| Клиентская аналитика || Построение прогнозных моделей и визуализация данных || 22.01.2022 || ||
|-
| Текстовая аналитика || Введение в задачи анализа текстовых данных || 29.01.2022 || ||
|-
| Текстовая аналитика || Инструменты и методы текстовой аналитики || 05.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Введение в задачи анализа данных в ритейле. Прогнозирование спроса || 12.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Описательная аналитика в Ритейл: кластеризация магазинов, сегментация товаров, восстановление спроса|| 19.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Задачи оптимизации запасов товаров в ритейл-сети, оптимизация цен, оптимизация ассортимента || 26.02.2022 || ||
|-
| Основы оценки рисков || Введение в кредитные риски || 5.03.2022 || ||
|-
| Основы оценки рисков || Введение в рыночные риски || 12.03.2022 || ||
|-
| Основы оценки рисков || Валидация моделей || 19.03.2022 || ||
|-
| ModelOps || Операционализация моделей машинного обучения || 26.03.2022 || ||
|}

== Семинары ==

'''суббота
'''Группа ФЭН 3 курс - 9:30, Группы ФЭН 4 курс - 11:10, Группа ПМИ+МК - 16:20, 
'''Группа ПАД 1 и МИЭФ - 18:10, Группа ПАД 2 - 19:40 

'''ссылка на подключение к семинару''' - https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 
Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 

'''ссылка на доп.материалы''' - [https://drive.google.com/drive/folders/1UNGRXQLbulMkvc-yexPHqZauGvwVJF17?usp=sharing к семинарским занятиям] 

{| class="wikitable"
|-
! Название раздела !! Тема !! Дата для ПМИ, ПАД, МИЭФ и МК !! Дата для ФЭН !! Презентация !! Запись
|-
| Клиентская аналитика || Введение в клиентскую и онлайн аналитику || 15.01.2022 || 22.01.2022 || || [https://drive.google.com/file/d/1j9-eoZSxLfprqsg_cpqnUfTNLwt_Rhpn/view?usp=sharing Семинар №1 - рус]
|-
| Клиентская аналитика || Построение прогнозных моделей и визуализация данных ||22.01.2022 || 29.01.2022 || ||
|-
| Текстовая аналитика || Введение в задачи анализа текстовых данных || 29.01.2022 || 05.02.2022 || ||
|-
| Текстовая аналитика || Инструменты и методы текстовой аналитики || 05.02.2022 || 12.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Введение в задачи анализа данных в ритейле. Прогнозирование спроса || 12.02.2022 || 19.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Описательная аналитика в Ритейл: кластеризация магазинов, сегментация товаров, восстановление спроса || 19.02.2022 || 26.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Задачи оптимизации запасов товаров в ритейл-сети, оптимизация цен, оптимизация ассортимента || 26.02.2022 || 5.03.2022 || ||
|-
| Основы оценки рисков || Введение в кредитные риски || 5.03.2022 || 12.03.2022 || ||
|-
| Основы оценки рисков || Введение в рыночные риски || 12.03.2022 || 19.03.2022 || ||
|-
| Основы оценки рисков || Валидация моделей || 19.03.2022 || 26.03.2022 || ||
|-
| ModelOps || Операционализация моделей машинного обучения || 26.03.2022 || 2.04.2022 || ||
|}

== Course report and grade evaluation ==

The course includes several forms of knowledge control:
* 3 practical homework assignments
* Written exam, questions in the form of a test with multiple choice
* Team project (only for 3rd-year students)

'''Criteria for assessing knowledge, skills'''

*All homework assignments are graded on a 2-point scale, where «2» — task is fully completed, «1» — the task is not completely solved or with slight mistakes, «0» — task is not solved or solved incorrectly.

In case if the homework is divided into several parts, then each part is evaluated on a 2-point scale as described above, and then the marks are averaged with equal weights without rounding.

The transfer of grades for homework from a 2-point scale to a 10-point scale is done by means of multiplying the grade by 5 without any rounding.

*The grade for the exam is set on a 10-point scale.

*The grade for the team project is also set on a 10-point scale.

'''The order of the formation of grades for the discipline '''

Let's denote the grades for 3 homework assignments on a 10-point scale — '''O_1,O_2,O_3,''' and the grade for the exam at the end of the 1st module on a 10-point scale — '''O_ex'''.

The final grade for 4th-year students '''O_final''' is evaluated by the following formula

'''O_final = 0.225 * O_1 + 0.225 * O_2 + 0.225 * O_3 + 0.325 * O_ex'''

The final grade for 3rd-year students in the 1st module '''O_mod''' is evaluated by the following formula

'''O_mod = 0.1 * O_1 + 0.1 * O_2 + 0.1 * O_3 + 0.2 * O_ex'''

The grade for the project in the 2nd module '''O_prj''' is set on a 10-point scale by means of the project defense.

The final grade '''O_final''' is defined by the formula '''O_final = O_mod + 0.5 * O_prj'''

Rounding occurs only at the very end - in the final grade, i.e. arithmetic rounding.

Each task and exam is evaluated on a 10-point scale (fractional marks are allowed for tasks). For some tasks, it will be possible to receive bonus points, which will be announced when the task is issued.

== Home assignments ==

'''Home assignment #1'''

The home task #1 consists of 2 parts:

1. Data research and data processing for subsequent segmentation;

2. Making customer profiles based on segmentations (use at least 2 segmentation methods).

An example of a detailed description of Home assignment №1 2020-2021 with examples and results [https://drive.google.com/file/d/16WFqo1xfPvnlGLwyorWFGdiL2ZM8xp8D/view?usp=sharing read in the attached file]

Each student chooses the version that is indicated opposite his name in the list
[https://docs.google.com/spreadsheets/d/19HGjq6sUJ__0Cg0akOr7AIivKvIRh7ttas_9QE4Q3E0/edit?usp=sharing link]

Versions and data description are presented [https://drive.google.com/open?id=1jxwwFMEHVh91ZKx5_PqnMSUlsICytIbI in the folder at the link]

In order to receive an assessment, you need:

1. Send an archive with files where all calculations were made and a cover letter with conclusions and comments on
each part:

* Calculations can be done using code (python/sas/sql), pivot tables and formulas in excel or SAS Viya project;
* All conclusions must be supported by visually interpretable graphs and data.

2. The archive (.zip) with the files must be sent to ntitova@hse.ru with the email subject “FCS HSE”

3. The file name must be sent according to the template <First Name>_<Last name>_<group number>_hw1.zip.

For example, Alexander_Sharipov_156_hw1

'''Grade for Home assignment #1''' is given on a 10-point scale, where:

"8-10" - the task is completely solved, all 2 parts of homework are completed:
* data analysis was carried out, a code is working, and tables for data research were provided;
* constructed segmentation by 2 methods;
* provided clear conclusions with confirmed data (tables, graphs);

"6-7" - the task is solved incompletely or with shortcomings:
* data analysis was carried out, a working code and tables for data research were provided;
* built segmentation using at least one method;
* provided clear conclusions with confirmed data (tables, graphs);

"4-5" - the task was solved with significant shortcomings,
* data analysis was carried out, a working code and tables for data research were provided;
* revealed top-level dependencies and patterns for customers without building a segmentation model;

"0-3" - the task is not solved or solved incorrectly.

''Deadline – 2 weeks (February 19, 2022 23:59).''

'''Home assignment #2'''

Description of Home assignment #2 is in the file [https://drive.google.com/file/d/1R6BLH40CLk7uewRn02JlGj64Ov2peJgK/view?usp=sharing link]

''Deadline - March 12, 2022 23:59.''
Solutions should be sent to aromanenko@hse.ru

The subject of the letter must contain the following: HSE + Course number + version_number + full name.

The file name must include:

*Course number
*Version number
*Full name

Example: "AMIS_3course_Version_8_IvanovIvanIvanovich"

'''Home assignment #3'''

It is necessary to build a scoring model that assesses the probability of a client default at the stage of making a loan application.
To do this you need:

0. Download data from the link https://drive.google.com/drive/u/0/folders/16CMyPnLu7Fv7IgsYOZimQK-7MaFZEWEZ

Each student selects 2 data samples "accept" and "reject", which start with the student's HW version.
Version numbers for HW #3 must be taken the same as for HW #1.

The completed task must be sent in the following form:

1) File/scripts with built models (there must be comments, without comments the task is considered as unresolved)

2) Excel file with answers to the following questions:

1. What is the proportion of 1 in the "accept" sample?

2. It is necessary to calculate the following for all interval variables:
- Proportion of missing values
- Median
- Mean
- Standard deviation
- Are there any abnormal values, outliers?
- Information Value
3. It is necessary to calculate for all categorical variables the following:
- Mode
- Proportion of missing values
- Information Value
- Are there outliers, abnormal values?
4. Build logistic regression only on approved applications with transformed WoE variables. What is the meaning of GINI? F1 measure?

5. Conduct a Reject Inference analysis. What is the percentage of rejected applications?

6. Build a logistic regression on all applications with transformed WoE variables. What GINI, F1 mean? Has the model changed?

7. What model would you recommend for implementation in a productive environment? Give a detailed explanation

Assessment for HW #3 is set on a 2-point scale, where "2" - the task is solved completely, "1" - the task is not completely solved or with shortcomings, "0" - the task is not solved or solved incorrectly.
The transfer of grades for homework from a 2-point scale to a 10-point scale is carried out by multiplying the grade by 5 without rounding.

For HW #3, marks will be given:
"2" - the model is correctly built on both the accept and reject samples. Correct answers given.
"1.6" - the model is correctly built both on the accept sample and on the reject sample. 50% of answers are correct.
"1.4" - the model is correctly built both on the accept sample and on the reject sample. Wrong answers are given.
"1" are given - the model is built only on the accept sample.
"0.8" - the task is not completely solved. 50% of answers are correct.
"0" - the task is not solved or solved incorrectly.

Deadline - until 26.03.2022 (including 26.03.2022)

Decisions should be sent to msvorobeva@hse.ru
The subject of the letter must contain the following: HSE + Course number + version_number + full name.

The file name must include:

*Course number
*Version number
*Full name

Example: "AMIS_3course_Version_8_IvanovIvanIvanovich"

If the works will repeat each other, both works will be considered unresolved.

== Team project for 3rd course ==

'''Choosing a topic for a team project on the course "Data Science Case Studies (JD SAS)" '''

Students are divided into groups of 3 people. In the future, 2-3 groups can be combined into one. Within the group, the team captain must be determined - the responsible person of the group.

The responsible person is responsible for the performance and result of the following main functions:
* sending an application for a project topic and agreeing on a project topic on behalf of the whole group with professors;
* providing information to the curator about the current status of the project: accounting, distribution and control of the implementation of the project task;
* sending reports, presentations, technical documentation on the work performed by the group in electronic form.

Project topic descriptions: [https://docs.google.com/document/d/1p_gECsEVi95_3-WW2eBJaA3yEGCJBzejDILSkPQDX7c/edit?usp=sharing link to file]

Link to the application form on the topic: [https://forms.gle/QG2SwvYZ5tN2Pwow5 link to file]

'''Topic selection deadlines:'''

'''Until April 17, the group must select two topics from the specified list''', indicating the first and second priority for these topics.

'''April 18 - each group that has applied for the project, through the specified form, will be assigned a project topic and curator.''' While assigning topics, the priorities indicated by the students will be taken into account, as well as the average score of students in the group for the first module of the course. No more than 3 groups of students are assigned to one topic.

If you have any questions, write to Natalia Titova on Telegram.

The result of each project should be a presentation of the results of the project for 10-15 minutes.

The defense of the project for DSBA and ICEF will be in mid-May (May 17-22), as well as for everyone who is ready to defend themselves.

The final defense for AMIS, Economics, Economics and Statistics will take place in mid-June (June 15-19) before the start of the session.

The assessment for the project in the second module is set on a 10-point scale based on the results of the defense of the project.

Those students who did not find a group for themselves also apply for a topic, but for themselves alone. We ourselves will connect with a group on a similar topic.

'''Dates for the defense of educational projects in module 4:'''

Project defenses will be held from 17 to 19 June 2021.

In the file you will find a link to the list of project groups with reference to the date and time of project defense, as well as links to defenses.

[https://docs.google.com/spreadsheets/d/127uq1MwiwZlPahfLj1AwBAuxMdpp44EM6hlYiLnYKyI/edit?usp=sharing Team projects - dates for defenses by groups and links to join.]

== How to ask a question about the course ==

'''Questions''' about the course can be asked in the telegram chat of the course, the course professor Natalia Titova @Natalitics or the manager of the SAS department Tatiana Lobok @tatianalobok (tlobok@hse.ru).

Telegram channel for announcements: https://t.me/+Lj-yHhfNJTQxYWEy

Chat in telegram for discussions: https://t.me/+a1VMTe2xwNA5Mzcy

''' All announcements and course materials will be posted in the telegram chat and in the telegram channel! '''
There are professors in the chat, but not always.
For all important questions, you should write to Natalia Titova in the telegram chat @Natalitics or mail Natalia.Titova@sas.com. Be sure to add the tag [AMIS FCS HSE/DSBA FCS HSE/ICEF FCS HSE/Ec FES HSE/EcSt FES HSE] to the title of the letter, and also indicate your last name and first name.

All files provided are intended for use by students during their studies and are updated throughout the year.
If you find any typos, inaccuracies, malfunctions of the page, please send email tlobok@hse.ru.

== Course materials ==
'''Documents and program of the course'''
''Attention: files are being updated!''

*The working program of the discipline for the 3rd and 4th courses document is located at the following [https://docs.google.com/document/d/1Sh-KjlZlAmk1kIEndmM7mF7nAj6iL6ZI/edit?usp=sharing&ouid=109416058723510784590&rtpof=true&sd=true link].

*'''[https://drive.google.com/open?id=1J7MHkux4wBq828_6Lg3trCmkxdutXV1K Instructions for connecting to SAS OnDemand and downloading SAS software]'''

*[https://drive.google.com/open?id=1ANDlimb5hp4ds0sUcReR6xq5sVrcX5n9 Lectures and seminars] ''Attention: files in the folder are being updated!''

== Recommended literature and useful additional materials ==

'''Useful materials'''

*[https://support.sas.com/en/software/enterprise-guide.html#freetutorials Training portal with additional literature for working with SAS Enterprise Guide]
*[https://habr.com/company/sas/blog/ SAS Company's block on Habr (Basics of programming on SAS Base)]

'''Useful literature'''

''Section 1:''
*[https://www.sas.com/store/books/categories/business-concepts-leadership/data-mining-techniques-for-marketing-sales-and-customer-relationship-management-third-edition/prodBK_64447_en.html Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition Michael J. A. Berry Gordon S. Linoff ]
*[https://www.springer.com/gp/book/9780387725789 Database Marketing. Analyzing and Managing Customers, Blattberg, Robert C., Kim, Byung-Do, Neslin, Scott A. ]

*[http://10k.inc.hse.ru/files/McKINSEY_GUIDE_to_business_plan.pdf LeSueur J. (2007) McKinsey&Company (2000) How to write a business plan.]
*[http://support.sas.com/resources/papers/proceedings12/286-2012.pdf Shive W. and Mouton D. (2012) Improving Retail Decisions with Customer Analytics: Leveragin Actionable Customer Insights across the Retail Enterprise to Build Sales and Profits. Paper 286-2012, SAS Institute, Inc]
*[http://support.sas.com/resources/papers/proceedings17/SAS0733-2017.pdf Baer D. (2017) Using Segmentation to Build More Powerful Models with SAS® Visual Analytics. Paper 733-2017, SAS Institute, Inc. ]
*К. Уолш (2000) Ключевые показатели менеджмента: как анализировать, сравнивать и контролировать данные, определяющие стоимость компании. М.: Дело. - Есть в открытом доступе.
*[http://kaplan-publishing.kaplan.co.uk/cima/p2/pages/study-text.aspx Kaplan Publishing (2018) CIMA P2 Study Text. Advanced Management Accounting.]
*[http://support.sas.com/resources/papers/proceedings12/286-2012.pdf Shive W. and Mouton D. (2012) Improving Retail Decisions with Customer Analytics: Leveragin Actionable Customer Insights across the Retail Enterprise to Build Sales and Profits. Paper 2862012, SAS Institute, Inc., Cary, NC]
*[https://support.sas.com/resources/papers/proceedings16/SAS6222-2016.pdf Baer D. and Grover S. (2016) Enhanced Segmentation Using SAS® Visual Analytics and SAS® Visual Statistics. Paper 6222-2016, SAS Institute, Inc., Cary, NC.]

*[https://support.sas.com/documentation/cdl/en/vaamgs/71329/PDF/default/vaamgs.pdf SAS Documentation (2015). SAS® Visual Analytics 7.2, 7.3,and 7.4: Getting Started with Analytical Models]
*[https://support.sas.com/documentation/cdl/en/vaug/68648/HTML/default/viewer.htm#n0lfkxxrf3zhugn14g96lr8spatc.htm SAS(R) Visual Analytics 7.3: User's Guide]

''Section 2:''

* Шапиро Дж (2006). Моделирование цепи поставок. Питер. Серия «Теория менеджмента».
* Tijms H.C., Groenevelt H. (1984). Simple approximations for the reorder point in periodic and continuous review (s, S) inventory systems with service level constraints. European Journal of Operational Research, Vol. 17, Issue 2, August 1984, Pages 175-190.]

''Section 3:''

* Christoffersen P. (2012) Elements of Financial Risk Management. 2nd ed. Elseiver Academic Press.

''Section 4:''

* Мортон С. (2016) Лаборатория презентаций. Формула идеального выступления. Альпина Паблишер.

== Contacts ==

[[Файл:Титова Наталья Николаевна.jpg|микро|70px|безрамки|слева]] '''Titova Natalia Nikolaevna''' - Senior tutor
Natalia.Titova@sas.com

[[Файл:Лобок Татьяна Сергеевна.jpg|микро|70px|безрамки|слева]] '''Lobok Tatiana Sergeevna''' - Manager of the basic SAS Department
tlobok@hse.ru

Data Science Case Studies (JD SAS) 21/22

2022-02-02T20:21:17Z

Fvderyabin: перевод на англ

[[Файл:Capture.PNG|1000px|безрамки|справа]]

=== Расписание занятий ===
Занятия проводятся по '''субботам''' 

'''Общая ссылка для всех лекций и семинаров:''' https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 

Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 
 
'''''9:30''''' - Семинар на русском - ФЭН 3 курс (совместно с БК ГК "Открытие") 
'''''11:10''''' - Семинар на русском - ФЭН 4 курс (совместно с БК ГК "Открытие") 
'''''13:00''''' - Лекция на английском (запись) - ПАД и МИЭФ 
'''''14:40''''' - Лекция на русском (запись) - ПМИ, ФЭН, МК 
'''''16:20''''' - Семинар на русском (запись) - ПМИ и МК 
'''''18:10''''' - Семинар на английском (запись) - ПАД1 и МИЭФ 
'''''19:40''''' - Семинар на английском - ПАД2 
 

При посещении лекции и семинара в Zoom просим студентов '''сделать Rename''' и подписаться в формате «Префикс_Фамилия Имя», указав один из префиксов (МИЭФ, МК, ПАД, ПМИ, ФЭН). Например, «БИ_Иванов Алексей»

=== О курсе ===
Данная страничка содержит ссылки на материалы по курсу в 2021/2022 учебном году на потоке образовательных программ :
 

{| class="wikitable"
! Образовательная программа !! Курс !! Факультет !! Ссылка на страницу курса АДвБ
|-
| [https://cs.hse.ru/ami Прикладная Математика и Информатика] || 3 курс || Факультета Компьютерных Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/339498590 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/data/about Прикладной анализ данных] || 3 курс || Факультета Компьютерных Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/339492551 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/economics/ Экономика] || 3 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904614 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/economics/ Экономика] || 4 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904606 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/stat/ Экономика и статистика] || 3 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904618 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/stat/ Экономика и статистика] || 4 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904610 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/icef// Программа двух дипломов по экономике НИУ ВШЭ и Лондонского университета] || 3 курс || Международный институт экономики и финансов НИУ ВШЭ || нет
|}
 
дополнительные ссылки:
*[https://cs.hse.ru/sas/ Базовая кафедра компании SAS на факультете компьютерных наук ВШЭ ]
*[https://cs.hse.ru/electives/sas_dscs Карточка курса и программа]
*[https://www.sas.com/ru_ru/home.html Сайт компании SAS]

*'''[https://www.sas.com/ru_ru/careers/internship-program.html Календарь стажерской программы на 2021/2022 год]'''

== Программа курса==

'''Первый модуль''' будет читаться для 3 и 4 курса - обзор разделов:
* Клиентская аналитика;
* Текстовая аналитика;
* Задачи анализа данных в розничных сетях продаж товаров;
* Основы оценки рисков;
* ModelOps.

Первый модуль сможет погрузить студентов в актуальные задачи в бизнесе, а также в особенности анализа данных и построения аналитических моделей по каждому разделу модуля.
В этом модуле студентов познакомят с программным обеспечением SAS.

'''Второй модуль''' – командный проект только для 3 курса.

Студенты будут разделены на группы по 2-7 человек и каждой группе будет дана практическая задача.
Данный модуль позволит получить студентам практический опыт в анализе данных, разработке и в построении аналитических моделей на реальных данных.

== Studying and using SAS software in «Data Analytics in Business» course ==

To perform practical tasks, a student is free of choice to pick any of the given software tools: SAS, R, Python.

'''Students who plan to perform practical tasks on SAS platform''', may take advanced online courses for free.

''To access the course, you must contact the course instructor - Natalia Titova via Telegram.''

Links to access the software SAS - 
https://sas-viya.cs.hse.ru/SASStudioV/main?locale=en_US 
https://sas-viya.cs.hse.ru/SASStudioV/main?locale=ru_RU

'''If a student has completed all the practical tasks on the SAS and passed the course with excellence''', then he will receive:
*academic SAS program completion certificate
*Acclaim electronic badge confirming completion of the course and a list of technologies used by SAS

All interested students can take basic SAS online courses for free:
*basics of programming on SAS Base [https://support.sas.com/edu/schedules.html?id=2588&ctry=RU link to the course]
*basics of statistical analysis using SAS software[https://support.sas.com/edu/schedules.html?id=5235&ctry=RU link to the course]

Students who are willing to spend extra time learning to program in SAS can try to take a professional certification within the SCYP program for free (SAS® Software Certified Young Professionals) [https://www.sas.com/sas/training/scyp.html link to the course].

== Лекции ==

'''суббота '''
{| class="wikitable"
''' ПАД и МИЭФ - 13:00 - 14:30; 
'''ПМИ, Экономика, Экономика и статистика, МК - 14:40 - 16:10 ''' 
 
'''ссылка на подключение к лекции''' - https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 
Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 
|-
! Название раздела !! Тема !! Дата для 3 и 4 курса !! Презентация!! Запись
|-
| Клиентская аналитика || Введение в клиентскую и онлайн аналитику || 15.01.2022 || || [https://drive.google.com/file/d/1nEbogpqbZBBrjOJy-sVmq4SdVn_svef6/view?usp=sharing Лекция №1 - рус]
|-
| Клиентская аналитика || Построение прогнозных моделей и визуализация данных || 22.01.2022 || ||
|-
| Текстовая аналитика || Введение в задачи анализа текстовых данных || 29.01.2022 || ||
|-
| Текстовая аналитика || Инструменты и методы текстовой аналитики || 05.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Введение в задачи анализа данных в ритейле. Прогнозирование спроса || 12.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Описательная аналитика в Ритейл: кластеризация магазинов, сегментация товаров, восстановление спроса|| 19.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Задачи оптимизации запасов товаров в ритейл-сети, оптимизация цен, оптимизация ассортимента || 26.02.2022 || ||
|-
| Основы оценки рисков || Введение в кредитные риски || 5.03.2022 || ||
|-
| Основы оценки рисков || Введение в рыночные риски || 12.03.2022 || ||
|-
| Основы оценки рисков || Валидация моделей || 19.03.2022 || ||
|-
| ModelOps || Операционализация моделей машинного обучения || 26.03.2022 || ||
|}

== Семинары ==

'''суббота
'''Группа ФЭН 3 курс - 9:30, Группы ФЭН 4 курс - 11:10, Группа ПМИ+МК - 16:20, 
'''Группа ПАД 1 и МИЭФ - 18:10, Группа ПАД 2 - 19:40 

'''ссылка на подключение к семинару''' - https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 
Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 

'''ссылка на доп.материалы''' - [https://drive.google.com/drive/folders/1UNGRXQLbulMkvc-yexPHqZauGvwVJF17?usp=sharing к семинарским занятиям] 

{| class="wikitable"
|-
! Название раздела !! Тема !! Дата для ПМИ, ПАД, МИЭФ и МК !! Дата для ФЭН !! Презентация !! Запись
|-
| Клиентская аналитика || Введение в клиентскую и онлайн аналитику || 15.01.2022 || 22.01.2022 || || [https://drive.google.com/file/d/1j9-eoZSxLfprqsg_cpqnUfTNLwt_Rhpn/view?usp=sharing Семинар №1 - рус]
|-
| Клиентская аналитика || Построение прогнозных моделей и визуализация данных ||22.01.2022 || 29.01.2022 || ||
|-
| Текстовая аналитика || Введение в задачи анализа текстовых данных || 29.01.2022 || 05.02.2022 || ||
|-
| Текстовая аналитика || Инструменты и методы текстовой аналитики || 05.02.2022 || 12.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Введение в задачи анализа данных в ритейле. Прогнозирование спроса || 12.02.2022 || 19.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Описательная аналитика в Ритейл: кластеризация магазинов, сегментация товаров, восстановление спроса || 19.02.2022 || 26.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Задачи оптимизации запасов товаров в ритейл-сети, оптимизация цен, оптимизация ассортимента || 26.02.2022 || 5.03.2022 || ||
|-
| Основы оценки рисков || Введение в кредитные риски || 5.03.2022 || 12.03.2022 || ||
|-
| Основы оценки рисков || Введение в рыночные риски || 12.03.2022 || 19.03.2022 || ||
|-
| Основы оценки рисков || Валидация моделей || 19.03.2022 || 26.03.2022 || ||
|-
| ModelOps || Операционализация моделей машинного обучения || 26.03.2022 || 2.04.2022 || ||
|}

== Course report and grade evaluation ==

The course includes several forms of knowledge control:
* 3 practical homework assignments
* Written exam, questions in the form of a test with multiple choice
* Team project (only for 3rd-year students)

'''Criteria for assessing knowledge, skills'''

*All homework assignments are graded on a 2-point scale, where «2» — task is fully completed, «1» — the task is not completely solved or with slight mistakes, «0» — task is not solved or solved incorrectly.

In case if the homework is divided into several parts, then each part is evaluated on a 2-point scale as described above, and then the marks are averaged with equal weights without rounding.

The transfer of grades for homework from a 2-point scale to a 10-point scale is done by means of multiplying the grade by 5 without any rounding.

*The grade for the exam is set on a 10-point scale.

*The grade for the team project is also set on a 10-point scale.

'''The order of the formation of grades for the discipline '''

Let's denote the grades for 3 homework assignments on a 10-point scale — '''O_1,O_2,O_3,''' and the grade for the exam at the end of the 1st module on a 10-point scale — '''O_ex'''.

The final grade for 4th-year students '''O_final''' is evaluated by the following formula

'''O_final = 0.225 * O_1 + 0.225 * O_2 + 0.225 * O_3 + 0.325 * O_ex'''

The final grade for 3rd-year students in the 1st module '''O_mod''' is evaluated by the following formula

'''O_mod = 0.1 * O_1 + 0.1 * O_2 + 0.1 * O_3 + 0.2 * O_ex'''

The grade for the project in the 2nd module '''O_prj''' is set on a 10-point scale by means of the project defense.

The final grade '''O_final''' is defined by the formula '''O_final = O_mod + 0.5 * O_prj'''

Rounding occurs only at the very end - in the final grade, i.e. arithmetic rounding.

Each task and exam is evaluated on a 10-point scale (fractional marks are allowed for tasks). For some tasks, it will be possible to receive bonus points, which will be announced when the task is issued.

== Home assignments ==

'''Home assignment #1'''

The home task #1 consists of 2 parts:

1. Data research and data processing for subsequent segmentation;

2. Making customer profiles based on segmentations (use at least 2 segmentation methods).

An example of a detailed description of Home assignment №1 2020-2021 with examples and results [https://drive.google.com/file/d/16WFqo1xfPvnlGLwyorWFGdiL2ZM8xp8D/view?usp=sharing read in the attached file]

Each student chooses the version that is indicated opposite his name in the list
[https://docs.google.com/spreadsheets/d/19HGjq6sUJ__0Cg0akOr7AIivKvIRh7ttas_9QE4Q3E0/edit?usp=sharing link]

Versions and data description are presented [https://drive.google.com/open?id=1jxwwFMEHVh91ZKx5_PqnMSUlsICytIbI in the folder at the link]

In order to receive an assessment, you need:

1. Send an archive with files where all calculations were made and a cover letter with conclusions and comments on
each part:

* Calculations can be done using code (python/sas/sql), pivot tables and formulas in excel or SAS Viya project;
* All conclusions must be supported by visually interpretable graphs and data.

2. The archive (.zip) with the files must be sent to ntitova@hse.ru with the email subject “FCS HSE”

3. The file name must be sent according to the template <First Name>_<Last name>_<group number>_hw1.zip.

For example, Alexander_Sharipov_156_hw1

'''Grade for Home assignment #1''' is given on a 10-point scale, where:

"8-10" - the task is completely solved, all 2 parts of homework are completed:
* data analysis was carried out, a code is working, and tables for data research were provided;
* constructed segmentation by 2 methods;
* provided clear conclusions with confirmed data (tables, graphs);

"6-7" - the task is solved incompletely or with shortcomings:
* data analysis was carried out, a working code and tables for data research were provided;
* built segmentation using at least one method;
* provided clear conclusions with confirmed data (tables, graphs);

"4-5" - the task was solved with significant shortcomings,
* data analysis was carried out, a working code and tables for data research were provided;
* revealed top-level dependencies and patterns for customers without building a segmentation model;

"0-3" - the task is not solved or solved incorrectly.

''Deadline – 2 weeks (February 19, 2022 23:59).''

'''Home assignment #2'''

Description of Home assignment #2 is in the file [https://drive.google.com/file/d/1R6BLH40CLk7uewRn02JlGj64Ov2peJgK/view?usp=sharing link]

''Deadline - March 12, 2022 23:59.''
Solutions should be sent to aromanenko@hse.ru

The subject of the letter must contain the following: HSE + Course number + version_number + full name.

The file name must include:

*Course number
*Version number
*Full name

Example: "AMIS_3course_Version_8_IvanovIvanIvanovich"

'''Home assignment #3'''

It is necessary to build a scoring model that assesses the probability of a client default at the stage of making a loan application.
To do this you need:

0. Download data from the link https://drive.google.com/drive/u/0/folders/16CMyPnLu7Fv7IgsYOZimQK-7MaFZEWEZ

Each student selects 2 data samples "accept" and "reject", which start with the student's HW version.
Version numbers for HW #3 must be taken the same as for HW #1.

The completed task must be sent in the following form:

1) File/scripts with built models (there must be comments, without comments the task is considered as unresolved)

2) Excel file with answers to the following questions:

1. What is the proportion of 1 in the "accept" sample?

2. It is necessary to calculate the following for all interval variables:
- Proportion of missing values
- Median
- Mean
- Standard deviation
- Are there any abnormal values, outliers?
- Information Value
3. It is necessary to calculate for all categorical variables the following:
- Mode
- Proportion of missing values
- Information Value
- Are there outliers, abnormal values?
4. Build logistic regression only on approved applications with transformed WoE variables. What is the meaning of GINI? F1 measure?

5. Conduct a Reject Inference analysis. What is the percentage of rejected applications?

6. Build a logistic regression on all applications with transformed WoE variables. What GINI, F1 mean? Has the model changed?

7. What model would you recommend for implementation in a productive environment? Give a detailed explanation

Assessment for HW #3 is set on a 2-point scale, where "2" - the task is solved completely, "1" - the task is not completely solved or with shortcomings, "0" - the task is not solved or solved incorrectly.
The transfer of grades for homework from a 2-point scale to a 10-point scale is carried out by multiplying the grade by 5 without rounding.

For HW #3, marks will be given:
"2" - the model is correctly built on both the accept and reject samples. Correct answers given.
"1.6" - the model is correctly built both on the accept sample and on the reject sample. 50% of answers are correct.
"1.4" - the model is correctly built both on the accept sample and on the reject sample. Wrong answers are given.
"1" are given - the model is built only on the accept sample.
"0.8" - the task is not completely solved. 50% of answers are correct.
"0" - the task is not solved or solved incorrectly.

Deadline - until 26.03.2022 (including 26.03.2022)

Decisions should be sent to msvorobeva@hse.ru
The subject of the letter must contain the following: HSE + Course number + version_number + full name.

The file name must include:

*Course number
*Version number
*Full name

Example: "AMIS_3course_Version_8_IvanovIvanIvanovich"

If the works will repeat each other, both works will be considered unresolved.

== Team project for 3rd course ==

'''Choosing a topic for a team project on the course "Data Science Case Studies (JD SAS)" '''

Students are divided into groups of 3 people. In the future, 2-3 groups can be combined into one. Within the group, the team captain must be determined - the responsible person of the group.

The responsible person is responsible for the performance and result of the following main functions:
* sending an application for a project topic and agreeing on a project topic on behalf of the whole group with professors;
* providing information to the curator about the current status of the project: accounting, distribution and control of the implementation of the project task;
* sending reports, presentations, technical documentation on the work performed by the group in electronic form.

Project topic descriptions: [https://docs.google.com/document/d/1p_gECsEVi95_3-WW2eBJaA3yEGCJBzejDILSkPQDX7c/edit?usp=sharing link to file]

Link to the application form on the topic: [https://forms.gle/QG2SwvYZ5tN2Pwow5 link to file]

'''Topic selection deadlines:'''

'''Until April 17, the group must select two topics from the specified list''', indicating the first and second priority for these topics.

'''April 18 - each group that has applied for the project, through the specified form, will be assigned a project topic and curator.''' While assigning topics, the priorities indicated by the students will be taken into account, as well as the average score of students in the group for the first module of the course. No more than 3 groups of students are assigned to one topic.

If you have any questions, write to Natalia Titova on Telegram.

The result of each project should be a presentation of the results of the project for 10-15 minutes.

The defense of the project for DSBA and ICEF will be in mid-May (May 17-22), as well as for everyone who is ready to defend themselves.

The final defense for AMIS, Economics, Economics and Statistics will take place in mid-June (June 15-19) before the start of the session.

The assessment for the project in the second module is set on a 10-point scale based on the results of the defense of the project.

Those students who did not find a group for themselves also apply for a topic, but for themselves alone. We ourselves will connect with a group on a similar topic.

'''Dates for the defense of educational projects in module 4:'''

Project defenses will be held from 17 to 19 June 2021.

In the file you will find a link to the list of project groups with reference to the date and time of project defense, as well as links to defenses.

[https://docs.google.com/spreadsheets/d/127uq1MwiwZlPahfLj1AwBAuxMdpp44EM6hlYiLnYKyI/edit?usp=sharing Team projects - dates for defenses by groups and links to join.]

== How to ask a question about the course ==

'''Questions''' about the course can be asked in the telegram chat of the course, the course professor Natalia Titova @Natalitics or the manager of the SAS department Tatiana Lobok @tatianalobok (tlobok@hse.ru).

Telegram channel for announcements: https://t.me/+Lj-yHhfNJTQxYWEy

Chat in telegram for discussions: https://t.me/+a1VMTe2xwNA5Mzcy

''' All announcements and course materials will be posted in the telegram chat and in the telegram channel! '''
There are professors in the chat, but not always.
For all important questions, you should write to Natalia Titova in the telegram chat @Natalitics or mail Natalia.Titova@sas.com. Be sure to add the tag [AMIS FCS HSE/DSBA FCS HSE/ICEF FCS HSE/Ec FES HSE/EcSt FES HSE] to the title of the letter, and also indicate your last name and first name.

All files provided are intended for use by students during their studies and are updated throughout the year.
If you find any typos, inaccuracies, malfunctions of the page, please send email tlobok@hse.ru.

== Course materials ==
'''Documents and program of the course'''
''Attention: files are being updated!''

*The working program of the discipline for the 3rd and 4th courses document is located at the following [https://docs.google.com/document/d/1Sh-KjlZlAmk1kIEndmM7mF7nAj6iL6ZI/edit?usp=sharing&ouid=109416058723510784590&rtpof=true&sd=true link].

*'''[https://drive.google.com/open?id=1J7MHkux4wBq828_6Lg3trCmkxdutXV1K Instructions for connecting to SAS OnDemand and downloading SAS software]'''

*[https://drive.google.com/open?id=1ANDlimb5hp4ds0sUcReR6xq5sVrcX5n9 Lectures and seminars] ''Attention: files in the folder are being updated!''

== Рекомендуемая литература и полезные дополнительные материалы ==

'''Полезные материалы'''

*[https://support.sas.com/en/software/enterprise-guide.html#freetutorials Обучающий портал с дополнительной литературой для работы с SAS Enterprise Guide]
*[https://habr.com/company/sas/blog/ Блок компании SAS на хабре (Основы программирования на SAS Base)]

'''Полезная литература'''

''К разделу 1:''
*[https://www.sas.com/store/books/categories/business-concepts-leadership/data-mining-techniques-for-marketing-sales-and-customer-relationship-management-third-edition/prodBK_64447_en.html Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition Michael J. A. Berry Gordon S. Linoff ]
*[https://www.springer.com/gp/book/9780387725789 Database Marketing. Analyzing and Managing Customers, Blattberg, Robert C., Kim, Byung-Do, Neslin, Scott A. ]

*[http://10k.inc.hse.ru/files/McKINSEY_GUIDE_to_business_plan.pdf LeSueur J. (2007) McKinsey&Company (2000) How to write a business plan.]
*[http://support.sas.com/resources/papers/proceedings12/286-2012.pdf Shive W. and Mouton D. (2012) Improving Retail Decisions with Customer Analytics: Leveragin Actionable Customer Insights across the Retail Enterprise to Build Sales and Profits. Paper 286-2012, SAS Institute, Inc]
*[http://support.sas.com/resources/papers/proceedings17/SAS0733-2017.pdf Baer D. (2017) Using Segmentation to Build More Powerful Models with SAS® Visual Analytics. Paper 733-2017, SAS Institute, Inc. ]
*К. Уолш (2000) Ключевые показатели менеджмента: как анализировать, сравнивать и контролировать данные, определяющие стоимость компании. М.: Дело. - Есть в открытом доступе.
*[http://kaplan-publishing.kaplan.co.uk/cima/p2/pages/study-text.aspx Kaplan Publishing (2018) CIMA P2 Study Text. Advanced Management Accounting.]
*[http://support.sas.com/resources/papers/proceedings12/286-2012.pdf Shive W. and Mouton D. (2012) Improving Retail Decisions with Customer Analytics: Leveragin Actionable Customer Insights across the Retail Enterprise to Build Sales and Profits. Paper 2862012, SAS Institute, Inc., Cary, NC]
*[https://support.sas.com/resources/papers/proceedings16/SAS6222-2016.pdf Baer D. and Grover S. (2016) Enhanced Segmentation Using SAS® Visual Analytics and SAS® Visual Statistics. Paper 6222-2016, SAS Institute, Inc., Cary, NC.]

*[https://support.sas.com/documentation/cdl/en/vaamgs/71329/PDF/default/vaamgs.pdf SAS Documentation (2015). SAS® Visual Analytics 7.2, 7.3,and 7.4: Getting Started with Analytical Models]
*[https://support.sas.com/documentation/cdl/en/vaug/68648/HTML/default/viewer.htm#n0lfkxxrf3zhugn14g96lr8spatc.htm SAS(R) Visual Analytics 7.3: User's Guide]

''К разделу 2:''

* Шапиро Дж (2006). Моделирование цепи поставок. Питер. Серия «Теория менеджмента».
* Tijms H.C., Groenevelt H. (1984). Simple approximations for the reorder point in periodic and continuous review (s, S) inventory systems with service level constraints. European Journal of Operational Research, Vol. 17, Issue 2, August 1984, Pages 175-190.]

''К разделу 3:''

* Christoffersen P. (2012) Elements of Financial Risk Management. 2nd ed. Elseiver Academic Press.

''К разделу 4:''

* Мортон С. (2016) Лаборатория презентаций. Формула идеального выступления. Альпина Паблишер.

== Contacts ==

[[Файл:Титова Наталья Николаевна.jpg|микро|70px|безрамки|слева]] '''Titova Natalia Nikolaevna''' - Senior tutor
Natalia.Titova@sas.com

[[Файл:Лобок Татьяна Сергеевна.jpg|микро|70px|безрамки|слева]] '''Lobok Tatiana Sergeevna''' - Manager of the basic SAS Department
tlobok@hse.ru

Data Science Case Studies (JD SAS) 21/22

2022-02-02T20:17:55Z

Fvderyabin: перевод на англ версию

[[Файл:Capture.PNG|1000px|безрамки|справа]]

=== Расписание занятий ===
Занятия проводятся по '''субботам''' 

'''Общая ссылка для всех лекций и семинаров:''' https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 

Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 
 
'''''9:30''''' - Семинар на русском - ФЭН 3 курс (совместно с БК ГК "Открытие") 
'''''11:10''''' - Семинар на русском - ФЭН 4 курс (совместно с БК ГК "Открытие") 
'''''13:00''''' - Лекция на английском (запись) - ПАД и МИЭФ 
'''''14:40''''' - Лекция на русском (запись) - ПМИ, ФЭН, МК 
'''''16:20''''' - Семинар на русском (запись) - ПМИ и МК 
'''''18:10''''' - Семинар на английском (запись) - ПАД1 и МИЭФ 
'''''19:40''''' - Семинар на английском - ПАД2 
 

При посещении лекции и семинара в Zoom просим студентов '''сделать Rename''' и подписаться в формате «Префикс_Фамилия Имя», указав один из префиксов (МИЭФ, МК, ПАД, ПМИ, ФЭН). Например, «БИ_Иванов Алексей»

=== О курсе ===
Данная страничка содержит ссылки на материалы по курсу в 2021/2022 учебном году на потоке образовательных программ :
 

{| class="wikitable"
! Образовательная программа !! Курс !! Факультет !! Ссылка на страницу курса АДвБ
|-
| [https://cs.hse.ru/ami Прикладная Математика и Информатика] || 3 курс || Факультета Компьютерных Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/339498590 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/data/about Прикладной анализ данных] || 3 курс || Факультета Компьютерных Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/339492551 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/economics/ Экономика] || 3 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904614 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/economics/ Экономика] || 4 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904606 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/stat/ Экономика и статистика] || 3 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904618 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/stat/ Экономика и статистика] || 4 курс || Факультета Экономических Наук НИУ ВШЭ || [https://www.hse.ru/edu/courses/470904610 Анализ данных в бизнесе]
|-
| [https://www.hse.ru/ba/icef// Программа двух дипломов по экономике НИУ ВШЭ и Лондонского университета] || 3 курс || Международный институт экономики и финансов НИУ ВШЭ || нет
|}
 
дополнительные ссылки:
*[https://cs.hse.ru/sas/ Базовая кафедра компании SAS на факультете компьютерных наук ВШЭ ]
*[https://cs.hse.ru/electives/sas_dscs Карточка курса и программа]
*[https://www.sas.com/ru_ru/home.html Сайт компании SAS]

*'''[https://www.sas.com/ru_ru/careers/internship-program.html Календарь стажерской программы на 2021/2022 год]'''

== Программа курса==

'''Первый модуль''' будет читаться для 3 и 4 курса - обзор разделов:
* Клиентская аналитика;
* Текстовая аналитика;
* Задачи анализа данных в розничных сетях продаж товаров;
* Основы оценки рисков;
* ModelOps.

Первый модуль сможет погрузить студентов в актуальные задачи в бизнесе, а также в особенности анализа данных и построения аналитических моделей по каждому разделу модуля.
В этом модуле студентов познакомят с программным обеспечением SAS.

'''Второй модуль''' – командный проект только для 3 курса.

Студенты будут разделены на группы по 2-7 человек и каждой группе будет дана практическая задача.
Данный модуль позволит получить студентам практический опыт в анализе данных, разработке и в построении аналитических моделей на реальных данных.

== Studying and using SAS software in «Data Analytics in Business» course ==

To perform practical tasks, a student is free of choice to pick any of the given software tools: SAS, R, Python.

'''Students who plan to perform practical tasks on SAS platform''', may take advanced online courses for free.

''To access the course, you must contact the course instructor - Natalia Titova via Telegram.''

Links to access the software SAS - 
https://sas-viya.cs.hse.ru/SASStudioV/main?locale=en_US 
https://sas-viya.cs.hse.ru/SASStudioV/main?locale=ru_RU

'''If a student has completed all the practical tasks on the SAS and passed the course with excellence''', then he will receive:
*academic SAS program completion certificate
*Acclaim electronic badge confirming completion of the course and a list of technologies used by SAS

All interested students can take basic SAS online courses for free:
*basics of programming on SAS Base [https://support.sas.com/edu/schedules.html?id=2588&ctry=RU link to the course]
*basics of statistical analysis using SAS software[https://support.sas.com/edu/schedules.html?id=5235&ctry=RU link to the course]

Students who are willing to spend extra time learning to program in SAS can try to take a professional certification within the SCYP program for free (SAS® Software Certified Young Professionals) [https://www.sas.com/sas/training/scyp.html link to the course].

== Лекции ==

'''суббота '''
{| class="wikitable"
''' ПАД и МИЭФ - 13:00 - 14:30; 
'''ПМИ, Экономика, Экономика и статистика, МК - 14:40 - 16:10 ''' 
 
'''ссылка на подключение к лекции''' - https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 
Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 
|-
! Название раздела !! Тема !! Дата для 3 и 4 курса !! Презентация!! Запись
|-
| Клиентская аналитика || Введение в клиентскую и онлайн аналитику || 15.01.2022 || || [https://drive.google.com/file/d/1nEbogpqbZBBrjOJy-sVmq4SdVn_svef6/view?usp=sharing Лекция №1 - рус]
|-
| Клиентская аналитика || Построение прогнозных моделей и визуализация данных || 22.01.2022 || ||
|-
| Текстовая аналитика || Введение в задачи анализа текстовых данных || 29.01.2022 || ||
|-
| Текстовая аналитика || Инструменты и методы текстовой аналитики || 05.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Введение в задачи анализа данных в ритейле. Прогнозирование спроса || 12.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Описательная аналитика в Ритейл: кластеризация магазинов, сегментация товаров, восстановление спроса|| 19.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Задачи оптимизации запасов товаров в ритейл-сети, оптимизация цен, оптимизация ассортимента || 26.02.2022 || ||
|-
| Основы оценки рисков || Введение в кредитные риски || 5.03.2022 || ||
|-
| Основы оценки рисков || Введение в рыночные риски || 12.03.2022 || ||
|-
| Основы оценки рисков || Валидация моделей || 19.03.2022 || ||
|-
| ModelOps || Операционализация моделей машинного обучения || 26.03.2022 || ||
|}

== Семинары ==

'''суббота
'''Группа ФЭН 3 курс - 9:30, Группы ФЭН 4 курс - 11:10, Группа ПМИ+МК - 16:20, 
'''Группа ПАД 1 и МИЭФ - 18:10, Группа ПАД 2 - 19:40 

'''ссылка на подключение к семинару''' - https://zoom.us/j/99220349786?pwd=WjFZTEFiQzA4b1lzVmVDbXdmNVMwUT09 
Идентификатор конференции: 992 2034 9786 
Код доступа: 476860 

'''ссылка на доп.материалы''' - [https://drive.google.com/drive/folders/1UNGRXQLbulMkvc-yexPHqZauGvwVJF17?usp=sharing к семинарским занятиям] 

{| class="wikitable"
|-
! Название раздела !! Тема !! Дата для ПМИ, ПАД, МИЭФ и МК !! Дата для ФЭН !! Презентация !! Запись
|-
| Клиентская аналитика || Введение в клиентскую и онлайн аналитику || 15.01.2022 || 22.01.2022 || || [https://drive.google.com/file/d/1j9-eoZSxLfprqsg_cpqnUfTNLwt_Rhpn/view?usp=sharing Семинар №1 - рус]
|-
| Клиентская аналитика || Построение прогнозных моделей и визуализация данных ||22.01.2022 || 29.01.2022 || ||
|-
| Текстовая аналитика || Введение в задачи анализа текстовых данных || 29.01.2022 || 05.02.2022 || ||
|-
| Текстовая аналитика || Инструменты и методы текстовой аналитики || 05.02.2022 || 12.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Введение в задачи анализа данных в ритейле. Прогнозирование спроса || 12.02.2022 || 19.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Описательная аналитика в Ритейл: кластеризация магазинов, сегментация товаров, восстановление спроса || 19.02.2022 || 26.02.2022 || ||
|-
| Задачи анализа данных в розничных сетях продаж товаров || Задачи оптимизации запасов товаров в ритейл-сети, оптимизация цен, оптимизация ассортимента || 26.02.2022 || 5.03.2022 || ||
|-
| Основы оценки рисков || Введение в кредитные риски || 5.03.2022 || 12.03.2022 || ||
|-
| Основы оценки рисков || Введение в рыночные риски || 12.03.2022 || 19.03.2022 || ||
|-
| Основы оценки рисков || Валидация моделей || 19.03.2022 || 26.03.2022 || ||
|-
| ModelOps || Операционализация моделей машинного обучения || 26.03.2022 || 2.04.2022 || ||
|}

== Course report and grade evaluation ==

The course includes several forms of knowledge control:
* 3 practical homework assignments
* Written exam, questions in the form of a test with multiple choice
* Team project (only for 3rd-year students)

'''Criteria for assessing knowledge, skills'''

*All homework assignments are graded on a 2-point scale, where «2» — task is fully completed, «1» — the task is not completely solved or with slight mistakes, «0» — task is not solved or solved incorrectly.

In case if the homework is divided into several parts, then each part is evaluated on a 2-point scale as described above, and then the marks are averaged with equal weights without rounding.

The transfer of grades for homework from a 2-point scale to a 10-point scale is done by means of multiplying the grade by 5 without any rounding.

*The grade for the exam is set on a 10-point scale.

*The grade for the team project is also set on a 10-point scale.

'''The order of the formation of grades for the discipline '''

Let's denote the grades for 3 homework assignments on a 10-point scale — '''O_1,O_2,O_3,''' and the grade for the exam at the end of the 1st module on a 10-point scale — '''O_ex'''.

The final grade for 4th-year students '''O_final''' is evaluated by the following formula

'''O_final = 0.225 * O_1 + 0.225 * O_2 + 0.225 * O_3 + 0.325 * O_ex'''

The final grade for 3rd-year students in the 1st module '''O_mod''' is evaluated by the following formula

'''O_mod = 0.1 * O_1 + 0.1 * O_2 + 0.1 * O_3 + 0.2 * O_ex'''

The grade for the project in the 2nd module '''O_prj''' is set on a 10-point scale by means of the project defense.

The final grade '''O_final''' is defined by the formula '''O_final = O_mod + 0.5 * O_prj'''

Rounding occurs only at the very end - in the final grade, i.e. arithmetic rounding.

Each task and exam is evaluated on a 10-point scale (fractional marks are allowed for tasks). For some tasks, it will be possible to receive bonus points, which will be announced when the task is issued.

== Home assignments ==

'''Home assignment #1'''

The home task #1 consists of 2 parts:

1. Data research and data processing for subsequent segmentation;

2. Making customer profiles based on segmentations (use at least 2 segmentation methods).

An example of a detailed description of Home assignment №1 2020-2021 with examples and results [https://drive.google.com/file/d/16WFqo1xfPvnlGLwyorWFGdiL2ZM8xp8D/view?usp=sharing read in the attached file]

Each student chooses the version that is indicated opposite his name in the list
[https://docs.google.com/spreadsheets/d/19HGjq6sUJ__0Cg0akOr7AIivKvIRh7ttas_9QE4Q3E0/edit?usp=sharing link]

Versions and data description are presented [https://drive.google.com/open?id=1jxwwFMEHVh91ZKx5_PqnMSUlsICytIbI in the folder at the link]

In order to receive an assessment, you need:

1. Send an archive with files where all calculations were made and a cover letter with conclusions and comments on
each part:

* Calculations can be done using code (python/sas/sql), pivot tables and formulas in excel or SAS Viya project;
* All conclusions must be supported by visually interpretable graphs and data.

2. The archive (.zip) with the files must be sent to ntitova@hse.ru with the email subject “FCS HSE”

3. The file name must be sent according to the template <First Name>_<Last name>_<group number>_hw1.zip.

For example, Alexander_Sharipov_156_hw1

'''Grade for Home assignment #1''' is given on a 10-point scale, where:

"8-10" - the task is completely solved, all 2 parts of homework are completed:
* data analysis was carried out, a code is working, and tables for data research were provided;
* constructed segmentation by 2 methods;
* provided clear conclusions with confirmed data (tables, graphs);

"6-7" - the task is solved incompletely or with shortcomings:
* data analysis was carried out, a working code and tables for data research were provided;
* built segmentation using at least one method;
* provided clear conclusions with confirmed data (tables, graphs);

"4-5" - the task was solved with significant shortcomings,
* data analysis was carried out, a working code and tables for data research were provided;
* revealed top-level dependencies and patterns for customers without building a segmentation model;

"0-3" - the task is not solved or solved incorrectly.

''Deadline – 2 weeks (February 19, 2022 23:59).''

'''Home assignment #2'''

Description of Home assignment #2 is in the file [https://drive.google.com/file/d/1R6BLH40CLk7uewRn02JlGj64Ov2peJgK/view?usp=sharing link]

''Deadline - March 12, 2022 23:59.''
Solutions should be sent to aromanenko@hse.ru

The subject of the letter must contain the following: HSE + Course number + version_number + full name.

The file name must include:

*Course number
*Version number
*Full name

Example: "AMIS_3course_Version_8_IvanovIvanIvanovich"

'''Home assignment #3'''

It is necessary to build a scoring model that assesses the probability of a client default at the stage of making a loan application.
To do this you need:

0. Download data from the link https://drive.google.com/drive/u/0/folders/16CMyPnLu7Fv7IgsYOZimQK-7MaFZEWEZ

Each student selects 2 data samples "accept" and "reject", which start with the student's HW version.
Version numbers for HW #3 must be taken the same as for HW #1.

The completed task must be sent in the following form:

1) File/scripts with built models (there must be comments, without comments the task is considered as unresolved)

2) Excel file with answers to the following questions:

1. What is the proportion of 1 in the "accept" sample?

2. It is necessary to calculate the following for all interval variables:
- Proportion of missing values
- Median
- Mean
- Standard deviation
- Are there any abnormal values, outliers?
- Information Value
3. It is necessary to calculate for all categorical variables the following:
- Mode
- Proportion of missing values
- Information Value
- Are there outliers, abnormal values?
4. Build logistic regression only on approved applications with transformed WoE variables. What is the meaning of GINI? F1 measure?

5. Conduct a Reject Inference analysis. What is the percentage of rejected applications?

6. Build a logistic regression on all applications with transformed WoE variables. What GINI, F1 mean? Has the model changed?

7. What model would you recommend for implementation in a productive environment? Give a detailed explanation

Assessment for HW #3 is set on a 2-point scale, where "2" - the task is solved completely, "1" - the task is not completely solved or with shortcomings, "0" - the task is not solved or solved incorrectly.
The transfer of grades for homework from a 2-point scale to a 10-point scale is carried out by multiplying the grade by 5 without rounding.

For HW #3, marks will be given:
"2" - the model is correctly built on both the accept and reject samples. Correct answers given.
"1.6" - the model is correctly built both on the accept sample and on the reject sample. 50% of answers are correct.
"1.4" - the model is correctly built both on the accept sample and on the reject sample. Wrong answers are given.
"1" are given - the model is built only on the accept sample.
"0.8" - the task is not completely solved. 50% of answers are correct.
"0" - the task is not solved or solved incorrectly.

Deadline - until 26.03.2022 (including 26.03.2022)

Decisions should be sent to msvorobeva@hse.ru
The subject of the letter must contain the following: HSE + Course number + version_number + full name.

The file name must include:

*Course number
*Version number
*Full name

Example: "AMIS_3course_Version_8_IvanovIvanIvanovich"

If the works will repeat each other, both works will be considered unresolved.

== Team project for 3rd course ==

'''Choosing a topic for a team project on the course "Data Science Case Studies (JD SAS)" '''

Students are divided into groups of 3 people. In the future, 2-3 groups can be combined into one. Within the group, the team captain must be determined - the responsible person of the group.

The responsible person is responsible for the performance and result of the following main functions:
* sending an application for a project topic and agreeing on a project topic on behalf of the whole group with professors;
* providing information to the curator about the current status of the project: accounting, distribution and control of the implementation of the project task;
* sending reports, presentations, technical documentation on the work performed by the group in electronic form.

Project topic descriptions: [https://docs.google.com/document/d/1p_gECsEVi95_3-WW2eBJaA3yEGCJBzejDILSkPQDX7c/edit?usp=sharing link to file]

Link to the application form on the topic: [https://forms.gle/QG2SwvYZ5tN2Pwow5 link to file]

'''Topic selection deadlines:'''

'''Until April 17, the group must select two topics from the specified list''', indicating the first and second priority for these topics.

'''April 18 - each group that has applied for the project, through the specified form, will be assigned a project topic and curator.''' While assigning topics, the priorities indicated by the students will be taken into account, as well as the average score of students in the group for the first module of the course. No more than 3 groups of students are assigned to one topic.

If you have any questions, write to Natalia Titova on Telegram.

The result of each project should be a presentation of the results of the project for 10-15 minutes.

The defense of the project for DSBA and ICEF will be in mid-May (May 17-22), as well as for everyone who is ready to defend themselves.

The final defense for AMIS, Economics, Economics and Statistics will take place in mid-June (June 15-19) before the start of the session.

The assessment for the project in the second module is set on a 10-point scale based on the results of the defense of the project.

Those students who did not find a group for themselves also apply for a topic, but for themselves alone. We ourselves will connect with a group on a similar topic.

'''Dates for the defense of educational projects in module 4:'''

Project defenses will be held from 17 to 19 June 2021.

In the file you will find a link to the list of project groups with reference to the date and time of project defense, as well as links to defenses.

[https://docs.google.com/spreadsheets/d/127uq1MwiwZlPahfLj1AwBAuxMdpp44EM6hlYiLnYKyI/edit?usp=sharing Team projects - dates for defenses by groups and links to join.]

== How to ask a question about the course ==

'''Questions''' about the course can be asked in the telegram chat of the course, the course professor Natalia Titova @Natalitics or the manager of the SAS department Tatiana Lobok @tatianalobok (tlobok@hse.ru).

Telegram channel for announcements: https://t.me/+Lj-yHhfNJTQxYWEy

Chat in telegram for discussions: https://t.me/+a1VMTe2xwNA5Mzcy

''' All announcements and course materials will be posted in the telegram chat and in the telegram channel! '''
There are professors in the chat, but not always.
For all important questions, you should write to Natalia Titova in the telegram chat @Natalitics or mail Natalia.Titova@sas.com. Be sure to add the tag [AMIS FCS HSE/DSBA FCS HSE/ICEF FCS HSE/Ec FES HSE/EcSt FES HSE] to the title of the letter, and also indicate your last name and first name.

All files provided are intended for use by students during their studies and are updated throughout the year.
If you find any typos, inaccuracies, malfunctions of the page, please send email tlobok@hse.ru.

== Course materials ==
'''Documents and program of the course'''
''Attention: files are being updated!''

*The working program of the discipline for the 3rd and 4th courses document is located at the following [https://docs.google.com/document/d/1Sh-KjlZlAmk1kIEndmM7mF7nAj6iL6ZI/edit?usp=sharing&ouid=109416058723510784590&rtpof=true&sd=true link].

*'''[https://drive.google.com/open?id=1J7MHkux4wBq828_6Lg3trCmkxdutXV1K Instructions for connecting to SAS OnDemand and downloading SAS software]'''

*[https://drive.google.com/open?id=1ANDlimb5hp4ds0sUcReR6xq5sVrcX5n9 Lectures and seminars] ''Attention: files in the folder are being updated!''

== Рекомендуемая литература и полезные дополнительные материалы ==

'''Полезные материалы'''

*[https://support.sas.com/en/software/enterprise-guide.html#freetutorials Обучающий портал с дополнительной литературой для работы с SAS Enterprise Guide]
*[https://habr.com/company/sas/blog/ Блок компании SAS на хабре (Основы программирования на SAS Base)]

'''Полезная литература'''

''К разделу 1:''
*[https://www.sas.com/store/books/categories/business-concepts-leadership/data-mining-techniques-for-marketing-sales-and-customer-relationship-management-third-edition/prodBK_64447_en.html Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Third Edition Michael J. A. Berry Gordon S. Linoff ]
*[https://www.springer.com/gp/book/9780387725789 Database Marketing. Analyzing and Managing Customers, Blattberg, Robert C., Kim, Byung-Do, Neslin, Scott A. ]

*[http://10k.inc.hse.ru/files/McKINSEY_GUIDE_to_business_plan.pdf LeSueur J. (2007) McKinsey&Company (2000) How to write a business plan.]
*[http://support.sas.com/resources/papers/proceedings12/286-2012.pdf Shive W. and Mouton D. (2012) Improving Retail Decisions with Customer Analytics: Leveragin Actionable Customer Insights across the Retail Enterprise to Build Sales and Profits. Paper 286-2012, SAS Institute, Inc]
*[http://support.sas.com/resources/papers/proceedings17/SAS0733-2017.pdf Baer D. (2017) Using Segmentation to Build More Powerful Models with SAS® Visual Analytics. Paper 733-2017, SAS Institute, Inc. ]
*К. Уолш (2000) Ключевые показатели менеджмента: как анализировать, сравнивать и контролировать данные, определяющие стоимость компании. М.: Дело. - Есть в открытом доступе.
*[http://kaplan-publishing.kaplan.co.uk/cima/p2/pages/study-text.aspx Kaplan Publishing (2018) CIMA P2 Study Text. Advanced Management Accounting.]
*[http://support.sas.com/resources/papers/proceedings12/286-2012.pdf Shive W. and Mouton D. (2012) Improving Retail Decisions with Customer Analytics: Leveragin Actionable Customer Insights across the Retail Enterprise to Build Sales and Profits. Paper 2862012, SAS Institute, Inc., Cary, NC]
*[https://support.sas.com/resources/papers/proceedings16/SAS6222-2016.pdf Baer D. and Grover S. (2016) Enhanced Segmentation Using SAS® Visual Analytics and SAS® Visual Statistics. Paper 6222-2016, SAS Institute, Inc., Cary, NC.]

*[https://support.sas.com/documentation/cdl/en/vaamgs/71329/PDF/default/vaamgs.pdf SAS Documentation (2015). SAS® Visual Analytics 7.2, 7.3,and 7.4: Getting Started with Analytical Models]
*[https://support.sas.com/documentation/cdl/en/vaug/68648/HTML/default/viewer.htm#n0lfkxxrf3zhugn14g96lr8spatc.htm SAS(R) Visual Analytics 7.3: User's Guide]

''К разделу 2:''

* Шапиро Дж (2006). Моделирование цепи поставок. Питер. Серия «Теория менеджмента».
* Tijms H.C., Groenevelt H. (1984). Simple approximations for the reorder point in periodic and continuous review (s, S) inventory systems with service level constraints. European Journal of Operational Research, Vol. 17, Issue 2, August 1984, Pages 175-190.]

''К разделу 3:''

* Christoffersen P. (2012) Elements of Financial Risk Management. 2nd ed. Elseiver Academic Press.

''К разделу 4:''

* Мортон С. (2016) Лаборатория презентаций. Формула идеального выступления. Альпина Паблишер.

== Контакты ==

[[Файл:Титова Наталья Николаевна.jpg|микро|70px|безрамки|слева]] '''Титова Наталия Николаевна''' - старший преподаватель
Natalia.Titova@sas.com

[[Файл:Лобок Татьяна Сергеевна.jpg|микро|70px|безрамки|слева]] '''Лобок Татьяна Сергеевна''' - менеджер базовой кафедры SAS
tlobok@hse.ru