Просмотр исходного текста страницы SAS Technologies for Data Mining

== Schedule ==

'''Link for all lectures and seminars:''' https://teams.microsoft.com/l/meetup-join/19%3abt3YFk3CdUF4vwyWQsW_Jb6FdHUCzgLG_hAumdyaxfw1%40thread.tacv2/1641804643575?context=%7b%22Tid%22%3a%2221f26c24-0793-4b07-a73d-563cd2ec235f%22%2c%22Oid%22%3a%22afe1666c-56bf-4dfa-88bb-62652330bdca%22%7d


'''Schedule from 10.01.2022 to 28.02.2022 (incl.):'''

every Monday, 14:40 - 16:00 - lecture (online), Dmitriy Ilvovskiy.

every Monday, 16:20 - 17:40 - seminar (online), Dmitriy Ilvovskiy.


'''Schedule from 05.03.2022 to 02.04.2022 (incl.):'''

every Saturday, 14:40 - 16:00 - lecture (online), Nataliya Titova.

every Saturday, 16:20 - 17:40 - seminar (online), Nataliya Titova.


== About this course ==
This course is aimed to study the basics of data analysis using SAS software. These knowledge and skills are necessary in the professional activities of specialists in mathematical modeling and computer science.

As a result of studying the course, students should:
* Know the basics of the SAS Base language and be able to write and understand simple programs in this language;
* Study the basics of macro programming in SAS Base;
* Understand the principles of application of the main statistical methods of data analysis using SAS Software;
* Be able to launch and analyze the results of the implementation of the main statistical methods of data analysis on the SAS platform;
* Know the list of basic data analysis methods implemented on the SAS platform.


Course description (HSE) - https://www.hse.ru/edu/courses/470995302 
<br />

{| class="wikitable"
|-
| Tutors || Dmitriy Ilvovskiy, Nataliya Titova
|-
| Credits || 5
|-
| Supervised study (h.) || 60
|-
| Self-study (h.) || 130
|-
| Year || 3
|-
| Format || full-time
|}
<br />
additional links:
*[https://cs.hse.ru/sas/ SAS-HSE department ]
*[https://cs.hse.ru/electives/sas Course program]
*[https://www.sas.com/ru_ru/home.html SAS Institute]

*'''[https://www.sas.com/ru_ru/careers/internship-program.html SAS Internship 2021/2022]'''

== Curriculum ==

{| class="wikitable"
|-
! # !! Topic
|-
| 1 || SAS analytical platform. Technology review.
|-
| 2 || SAS BASE programming language
|-
| 2.1 || SAS BASE programming
|-
| 2.2 || Macros, SQL
|-
| 3 || Statistical analysis using SAS STAT
|-
| 3.1 || SAS STAT intro, ANOVA
|-
| 3.2 || Linear regression
|-
| 3.3 || Logistic regression
|-
| 3.4 || Generalized linear models
|-
| 3.5 || Data visualization
|-
| 3.6 || Clustering methods
|-
| 3.7 || Decision tree
|-
| 3.8 || Ensemble methods. Random forest, boosting.
|-
| 3.9 || Non-linear models. Neural networks.
|}

=== Educational material ===
10.01.2022, lecture - https://drive.google.com/drive/folders/1GnyXdsWVD3-CH1ikGjx7Y_JG31kBy5Bf?usp=sharing

17.01.2022, lecture - https://drive.google.com/drive/folders/1enuEVq9WSnF_5OI84yPt8IzsgQVUK50M?usp=sharing

17.01.2022, seminar - https://drive.google.com/drive/folders/1H6ncIQ-5J6aOT9MOLbFSYRPaEvIVNUOp?usp=sharing

24.01.2022, lecture - https://drive.google.com/drive/folders/1haGmYEdCjEri1PJLySY8pg74Y_oJxu9w?usp=sharing

24.01.2022, seminar - https://drive.google.com/drive/folders/1PCOrUHDhRbPFqhsKOEr83UWoC0d-aZs8?usp=sharing

31.01.2022, lecture - https://drive.google.com/drive/folders/1pJ3A1KC96oad8H5Xds1crX0fa2Tevu7h?usp=sharing

31.01.2022, seminar - https://drive.google.com/drive/folders/1_nCn2tx9-WCKBysS8I9VwoPT-wdCUyA1?usp=sharing

07.02.2022, lecture - https://drive.google.com/drive/folders/11S4cbFa4YfKM9ofcDBIIfy3neIcIp58e?usp=sharing

07.02.2022, seminar - https://drive.google.com/drive/folders/1xQxOvC4aQYm3KGibsFLC8bdg6OoY6l0-?usp=sharing

14.02.2022, lecture - https://drive.google.com/drive/folders/1t-f2WGNl5nvO13lzxE35AdRMOVChveJF?usp=sharing

14.02.2022, seminar - https://drive.google.com/drive/folders/17jc61xRwWdoiMHwaEHIBzLfnvKNuIrhx?usp=sharing

21.02.2022, lecture - https://drive.google.com/drive/folders/1JXQRpAT9mj7xQPwz1CGn27i0-TNApSfU?usp=sharing

21.02.2022, seminar - https://drive.google.com/drive/folders/1RuMCB0VZrGnODQP7JsX9rnU1PAEByuyr?usp=sharing

== SAS software ==
'''SAS environment:''' students are supposed to do tasks with SAS Viya. All software tools are cloud-based, it is enough to have an Internet browser.

SAS environment instruction and your personal access login and password will be sent to your student e-mails (in the "edu.hse.ru" domain).

'''Students who in-time and successfully do all tasks using SAS''', may attend to free online courses on additional topics.

''If you are interested in it, please, write to Nataliya Titova or send a request to the Telegram-chat.''

'''If a student have successfully done all homeworks (with excellence)''', he or she may receive:
* SAS academic program certificate
* Acclaim e-badge, confirming successful passing of this course (with the list of SAS technologies used).

'''''It is required to have a final grade at least 9 points.'''''


The following online courses are freely available:
*SAS Base programming [https://support.sas.com/edu/schedules.html?id=2588&ctry=RU link]
*Statistical analysis using SAS [https://support.sas.com/edu/schedules.html?id=5235&ctry=RU link]

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

== Assessment criteria ==

The course provides several forms of knowledge control:
* 4 homeworks;
* Exam.

'''Criteria'''

*All homework assignments are graded on a 10-point scale, minimum passing grade - 4.  

*Exam grade is also given on a 10-point scale. 


'''Final grade'''

'''Final grade''' formula: 

'''О_final = 0,5·О_exam + 0,5·О_HW'''

где
* О_exam – grade for the exam on a ten-point scale.
* О_HW – average grade for all homework assignments on a ten-point scale. 

Rounding occurs only at the very end - in the final assessment. Rounding is arithmetic.
 
Each task and exam is evaluated on a 10-point scale (fractional marks are allowed for tasks).

The exam is held in the form of homework defense: students answer the questions on the tasks they have completed and, if necessary, perform an additional written task. If a student for some reason cannot or refuses to defend himself or herself, he or she must pass a written exam.

== Homework ==

'''Homework №1'''

''To be announced later''


'''Homework №2'''

''To be announced later.''


'''Homework №3'''

''To be announced later''


'''Homework №4'''

''To be announced later.''


== Contacts ==
'''Questions''' may be addressed in Telegram-chat, to Dmitry Ilvovsky @dilv_ru dilvovsky@hse.ru or SAS-HSE department manager Tatiana Lobok (@tatianalobok) tlobok@hse.ru.
Be sure to add the tag [PMI FKN HSE / PAD FKN HSE / MIEF FKN HSE / Ek FEN HSE / Ext FEN HSE] to the title of the letter, and also indicate your last name and first name.


Telegram channel for announcements: https://t.me/+gI3IA1ucpok3MGUy

Telegram chat for discussions: https://t.me/+hSmHk-PTzXszOTk6


''' All announcements and materials will be available in Telegram chat! '''

All files provided are intended for use by students during their studies and are updated throughout the year.
For found typos, inaccuracies, malfunctions of the page, please write to e-mail tlobok@hse.ru.

== Additional materials ==
'''Course documents'''  
''NB: the content may be updated!''

*'''[https://sas-viya.cs.hse.ru Access to SAS software]'''

*[https://drive.google.com/drive/folders/1_ZcBAXDaAGRHL1TPjFTJIHyEtmu9sQeS?usp=sharing Lectures and seminars]  ''NB: the content may be updated!''

== Bibliography ==

'''Materials'''

*[https://support.sas.com/en/software/enterprise-guide.html#freetutorials SAS Enterprise Guide educational portal]
*[https://habr.com/company/sas/blog/ SAS Institute blog (SAS Base programming)]


'''Literature'''

*[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. ]
*[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]
* 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.]
* Christoffersen P. (2012) Elements of Financial Risk Management. 2nd ed. Elseiver Academic Press.
* Institute, SAS. Base SAS 9.4 Procedures Guide. SAS Institute, 2013. – 2050 pp.
* Institute, SAS. SAS 9.4 Functions and CALL Routines: Reference, Third Edition. SAS Institute, 2014. – 1100 pp.
* Institute, SAS. SAS 9.4 SQL Procedure User's Guide. SAS Institute, 2013. – 478 pp.
* Littell, R. C., Schlotzhauer, S. D. SAS System for Elementary Statistical Analysis. – SAS Institute, 1997. – 456 pp.