Szkoła Główna Handlowa w Warszawie - Centralny System Uwierzytelniania
Strona główna

Advanced Business Analytics, Data Imputation Techniques

Informacje ogólne

Kod przedmiotu: 229091-D
Kod Erasmus / ISCED: 11.2 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0542) Statystyka Kod ISCED - Międzynarodowa Standardowa Klasyfikacja Kształcenia (International Standard Classification of Education) została opracowana przez UNESCO.
Nazwa przedmiotu: Advanced Business Analytics, Data Imputation Techniques
Jednostka: Szkoła Główna Handlowa w Warszawie
Grupy: Major courses for AAB - masters
Przedmioty obowiązkowe na programie SMMD-ADA
Punkty ECTS i inne: 4.50 (zmienne w czasie) Podstawowe informacje o zasadach przyporządkowania punktów ECTS:
  • roczny wymiar godzinowy nakładu pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się dla danego etapu studiów wynosi 1500-1800 h, co odpowiada 60 ECTS;
  • tygodniowy wymiar godzinowy nakładu pracy studenta wynosi 45 h;
  • 1 punkt ECTS odpowiada 25-30 godzinom pracy studenta potrzebnej do osiągnięcia zakładanych efektów uczenia się;
  • tygodniowy nakład pracy studenta konieczny do osiągnięcia zakładanych efektów uczenia się pozwala uzyskać 1,5 ECTS;
  • nakład pracy potrzebny do zaliczenia przedmiotu, któremu przypisano 3 ECTS, stanowi 10% semestralnego obciążenia studenta.

zobacz reguły punktacji
Język prowadzenia: angielski
Efekty uczenia się:

Wiedza:

Student should: know history and philosophy of advanced business analytics;

know the potential and the areas of application of linear models for various data types and data imputation methods, segmentation and customer lifetime value models.

Student should: know and understand the concept of missing data pattern, missing data mechanism know how to carry out the imputation and how to interpret the results; know the basic methods of the estimation of the advanced business analytics; know how to estimate and evaluate results of the advanced business analytics models.

Student should: know the methods of model verification and evaluation for the advanced analytics models and how to improve their quality; know how to estimate predictive models, know how to perform client segmentation and assessment within Customer Lifetime Value models.

Umiejętności:

Student should: know the pros and cons of the discussed advanced analytics models; be able to choose appropriate imputation method and implement it using the computer software; know how to prepare data set for the advanced analytics models.

Student should:

understand the limitation of the estimation method;

build various types of regression models depending on the data structures;

build a model of k-means segmentation;

build a retention model (assuming constant and variable in time retention rates); calculate the lifetime value and interpret business effect of the results.

Student should: know how to interpret results of model estimation; implement estimate procedures, models verification and evaluation models quality;

evaluate the model, as well as recognize the areas of potential problems.

Kompetencje społeczne:

Ability to estimate advanced models using SAS (programming using 4GL language and SAS Studio module) and to extend the analysis using other packages: R, Python.

Knowledge on soft skills and work in team.

Zajęcia w cyklu "Preferencje - Semestr letni 2024/25" (jeszcze nie rozpoczęty)

Okres: 2025-02-15 - 2025-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Zajęcia prowadzącego więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Adam Korczyński
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Zajęcia prowadzącego - Ocena

Zajęcia w cyklu "Semestr letni 2024/25" (jeszcze nie rozpoczęty)

Okres: 2025-02-15 - 2025-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

Advanced business analytics. Data imputation. Advanced regression techniques. Customer Life Time Value.

Pełny opis:

The aim of the advanced course is to provide student with a complex knowledge in advanced business analytics based on cross-sectional and longitudinal data in various contexts such as patient records, customer history and economic surveys. Students learn about the fundamental concepts, scope of use and meaning of the advanced business analytics, data imputation methods, advanced regression techniques and lifetime value models. The concepts are presented along with their practical business applications.

The objective of the advanced business course is to present a complex approach concerning advanced business analytics. During the lectures the philosophy and theoretical background of the advanced statistical business models and their evaluation, as well as practical aspects of using models are presented. The course is made of three main parts: data imputation methods, advanced regression models, mixed models and segmentation and lifetime value models. Students learn about the practice of data imputation and the importance of imputation in the business decision making process. During computer labs students have possibility to experience a complete process of modeling starting with the data preparation, including data imputation, variable specification, variable selection, estimation and verification, evaluation of the quality and finally prediction. The context of BIG DATA for advanced Business Analytics is included. The laboratories will be mainly using SAS. Examples in other software packages (R, Python) will be provided. Classes are part of the Certificate Program: "Data Scientist with SAS System".

Literatura:

Literatura podstawowa:

Aanderud T., Collum R., Kumpfmiller R., An Introduction to SAS Visual Analytics: How to Explore Numbers , Design Reports, and Gain Insight into Your Data. SAS Institute, Cary , 2017.

Advanced Business Analytics - SAS Course Materials. SAS Institute , Cary 2015.

Allison A., Missing Data, Thousand Oaks: Sage University Paper, 2002.

Fader P., Hardie B.G., How to project customer retention, J. of Interactive Marketing, 2007

Hastie T., Tibshirani R., Friedman J., The elements of statistical learning. Data Mining, Inference, and Prediction. Second Edition, Springer: New York, 2009.

James G., Witten D., Hastie T., Tibshirani R., An Introduction to Statistical Learning with Applications in R, Springer: New York, 2013.

Korczyński A, Review of methods for data sets with missing values and practical applications, Śląski Przegląd Statystyczny, Wrocław, Uniwersytet Ekonomiczny we Wrocławiu, 2014, s. 83-104.

Kuhn M., K. Johnson, Applied Predictive Modeling, Springer, New York, 2013.

Linoff S.G., Data Analysis Using SQL and Excel, Second Edition, Wiley, 2016

Little A, Rubin D., Statistical Analysis with Missing Data. John Wiley & Sons: Hoboken 2002.

Malthouse E.C., Segmentation and Lifetime Value Models Using SAS, SAS Institute, 2013

Molenberghs G., Kenward M. G , Missing Data in Clinical Studies, John Wiley & Sons: Chichester, 2007

Ribeiro J., Business Survival Analysis Using SAS: An Introduction to Lifetime Probabilities. SAS Institute, Cary , 2017.

Silvia J., Iqbal A., House S. Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets, SAS Institute, Cary 2017.

Svolba G., Applying Data Science. Business Case Studies, SAS Institute: Cary, NC, 2017.

Svolba G., Data Quality for Analytics Using SAS, SAS Institute: Cary, NC, 2012.

Literatura uzupełniająca:

Berger P., Nasr N., Customer lifetime value: Marketing models and applications, J. of Interactive Marketing, 1998

Clarke B.S. and Clarke J.L., Predictive Statistics. Analysis and Inference beyond Models, Cambridge University Press, Cambridge UK, 2018.

Frątczak E. Statistics for Management and Economics, SGH, Warszawa, 2015.

Maimon O. and Rokach L. (red.), Data Mining and Knowledge Discovery Handbook, Springer, New York, 2005.

Rubin D., Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons: Hoboken, 1997.

Schafer J. L., Analysis of Multivariate Incomplete Data, Chapman & Hall: London 1997.

Publikacje własne:

Korczyński A., Modelowanie statystyczne dla biznesu. Teoria i zastosowania z wykorzystaniem SAS Viya, R i Python, Oficyna Wydawnicza SGH, Warszawa, 2023.

Korczyński A., Screening wariancji jako narzędzie wykrywania zmowy cenowej. Istota i znaczenie imputacji danych, Oficyna wydawnicza SGH, Warszawa, 2018.

Korczyński A, Review of methods for data sets with missing values and practical applications, Śląski Przegląd Statystyczny , Wrocław, Uniwersytet Ekonomiczny we Wrocławiu, 2014, s. 83-104.

Korczyński A., Własności estymatorów - porównanie kalibracji i imputacji wielokrotnej, Statystyka - zastosowania biznesowe i społeczne, Warszawa, Wydawnictwo Wyższej Szkoły Menedżerskiej w Warszawie, 2014, s. 183-194.

Uwagi:

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

ocena z ćwiczeń: 50.00%

Zajęcia w cyklu "Semestr zimowy 2024/25" (w trakcie)

Okres: 2024-10-01 - 2025-02-14
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Łukasz Głąb, Adam Korczyński
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

Advanced business analytics. Data imputation. Advanced regression techniques. Customer Life Time Value.

Pełny opis:

The aim of the advanced course is to provide student with a complex knowledge in advanced business analytics based on cross-sectional and longitudinal data in various contexts such as patient records, customer history and economic surveys. Students learn about the fundamental concepts, scope of use and meaning of the advanced business analytics, data imputation methods, advanced regression techniques and lifetime value models. The concepts are presented along with their practical business applications.

The objective of the advanced business course is to present a complex approach concerning advanced business analytics. During the lectures the philosophy and theoretical background of the advanced statistical business models and their evaluation, as well as practical aspects of using models are presented. The course is made of three main parts: data imputation methods, advanced regression models, mixed models and segmentation and lifetime value models. Students learn about the practice of data imputation and the importance of imputation in the business decision making process. During computer labs students have possibility to experience a complete process of modeling starting with the data preparation, including data imputation, variable specification, variable selection, estimation and verification, evaluation of the quality and finally prediction. The context of BIG DATA for advanced Business Analytics is included. The laboratories will be mainly using SAS. Examples in other software packages (R, Python) will be provided. Classes are part of the Certificate Program: "Data Scientist with SAS System".

Literatura:

Literatura podstawowa:

Aanderud T., Collum R., Kumpfmiller R., An Introduction to SAS Visual Analytics: How to Explore Numbers , Design Reports, and Gain Insight into Your Data. SAS Institute, Cary , 2017.

Advanced Business Analytics - SAS Course Materials. SAS Institute , Cary 2015.

Allison A., Missing Data, Thousand Oaks: Sage University Paper, 2002.

Fader P., Hardie B.G., How to project customer retention, J. of Interactive Marketing, 2007

Hastie T., Tibshirani R., Friedman J., The elements of statistical learning. Data Mining, Inference, and Prediction. Second Edition, Springer: New York, 2009.

James G., Witten D., Hastie T., Tibshirani R., An Introduction to Statistical Learning with Applications in R, Springer: New York, 2013.

Korczyński A, Review of methods for data sets with missing values and practical applications, Śląski Przegląd Statystyczny, Wrocław, Uniwersytet Ekonomiczny we Wrocławiu, 2014, s. 83-104.

Kuhn M., K. Johnson, Applied Predictive Modeling, Springer, New York, 2013.

Linoff S.G., Data Analysis Using SQL and Excel, Second Edition, Wiley, 2016

Little A, Rubin D., Statistical Analysis with Missing Data. John Wiley & Sons: Hoboken 2002.

Malthouse E.C., Segmentation and Lifetime Value Models Using SAS, SAS Institute, 2013

Molenberghs G., Kenward M. G , Missing Data in Clinical Studies, John Wiley & Sons: Chichester, 2007

Ribeiro J., Business Survival Analysis Using SAS: An Introduction to Lifetime Probabilities. SAS Institute, Cary , 2017.

Silvia J., Iqbal A., House S. Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets, SAS Institute, Cary 2017.

Svolba G., Applying Data Science. Business Case Studies, SAS Institute: Cary, NC, 2017.

Svolba G., Data Quality for Analytics Using SAS, SAS Institute: Cary, NC, 2012.

Literatura uzupełniająca:

Berger P., Nasr N., Customer lifetime value: Marketing models and applications, J. of Interactive Marketing, 1998

Clarke B.S. and Clarke J.L., Predictive Statistics. Analysis and Inference beyond Models, Cambridge University Press, Cambridge UK, 2018.

Frątczak E. Statistics for Management and Economics, SGH, Warszawa, 2015.

Maimon O. and Rokach L. (red.), Data Mining and Knowledge Discovery Handbook, Springer, New York, 2005.

Rubin D., Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons: Hoboken, 1997.

Schafer J. L., Analysis of Multivariate Incomplete Data, Chapman & Hall: London 1997.

Publikacje własne:

Korczyński A., Modelowanie statystyczne dla biznesu. Teoria i zastosowania z wykorzystaniem SAS Viya, R i Python, Oficyna Wydawnicza SGH, Warszawa, 2023.

Korczyński A., Screening wariancji jako narzędzie wykrywania zmowy cenowej. Istota i znaczenie imputacji danych, Oficyna wydawnicza SGH, Warszawa, 2018.

Korczyński A, Review of methods for data sets with missing values and practical applications, Śląski Przegląd Statystyczny , Wrocław, Uniwersytet Ekonomiczny we Wrocławiu, 2014, s. 83-104.

Korczyński A., Własności estymatorów - porównanie kalibracji i imputacji wielokrotnej, Statystyka - zastosowania biznesowe i społeczne, Warszawa, Wydawnictwo Wyższej Szkoły Menedżerskiej w Warszawie, 2014, s. 183-194.

Uwagi:

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

ocena z ćwiczeń: 50.00%

Zajęcia w cyklu "Semestr letni 2023/24" (zakończony)

Okres: 2024-02-24 - 2024-09-30
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: Łukasz Głąb, Adam Korczyński
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

Advanced analytics. Predictive modeling. Data imputation. Advanced regression techniques. Client Life Time Value.

Pełny opis:

The aim of the advanced course is to provide student with a complex knowledge in advanced business analytics based on cross-sectional and longitudinal data in various contexts such as patient records, customer history and economic surveys. Students learn about the fundamental concepts, scope of use and meaning of the advanced business analytics, data imputation methods, advanced regression techniques and lifetime value models. The concepts are presented along with their practical business applications.

The objective of the advanced business course is to present a complex approach concerning advanced business analytics. During the lectures the philosophy and theoretical background of the advanced statistical business models and their evaluation, as well as practical aspects of using models are presented. The course is made of three main parts: data imputation methods, advanced regression models, mixed models and segmentation and lifetime value models. Students learn about the practice of data imputation and the importance of imputation in the business decision making process. During computer labs students have possibility to experience a complete process of modeling starting with the data preparation, including data imputation, variable specification, variable selection, estimation and verification, evaluation of the quality and finally prediction. The context of BIG DATA for advanced Business Analytics is included. The laboratories will be mainly using SAS. Examples in other software packages (R, Python) will be provided. Classes are part of the Certificate Program: "Data Scientist with SAS System".

Literatura:

Literatura podstawowa:

Aanderud T., Collum R., Kumpfmiller R., An Introduction to SAS Visual Analytics: How to Explore Numbers , Design Reports, and Gain Insight into Your Data. SAS Institute, Cary , 2017.

Advanced Business Analytics - SAS Course Materials. SAS Institute , Cary 2015.

Allison A., Missing Data, Thousand Oaks: Sage University Paper, 2002.

Fader P., Hardie B.G., How to project customer retention, J. of Interactive Marketing, 2007

Hastie T., Tibshirani R., Friedman J., The elements of statistical learning. Data Mining, Inference, and Prediction. Second Edition, Springer: New York, 2009.

James G., Witten D., Hastie T., Tibshirani R., An Introduction to Statistical Learning with Applications in R, Springer: New York, 2013.

Korczyński A, Review of methods for data sets with missing values and practical applications, Śląski Przegląd Statystyczny, Wrocław, Uniwersytet Ekonomiczny we Wrocławiu, 2014, s. 83-104.

Kuhn M., K. Johnson, Applied Predictive Modeling, Springer, New York, 2013.

Linoff S.G., Data Analysis Using SQL and Excel, Second Edition, Wiley, 2016

Little A, Rubin D., Statistical Analysis with Missing Data. John Wiley & Sons: Hoboken 2002.

Malthouse E.C., Segmentation and Lifetime Value Models Using SAS, SAS Institute, 2013

Molenberghs G., Kenward M. G , Missing Data in Clinical Studies, John Wiley & Sons: Chichester, 2007

Ribeiro J., Business Survival Analysis Using SAS: An Introduction to Lifetime Probabilities. SAS Institute, Cary , 2017.

Silvia J., Iqbal A., House S. Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets, SAS Institute, Cary 2017.

Svolba G., Applying Data Science. Business Case Studies, SAS Institute: Cary, NC, 2017.

Svolba G., Data Quality for Analytics Using SAS, SAS Institute: Cary, NC, 2012.

Literatura uzupełniająca:

Berger P., Nasr N., Customer lifetime value: Marketing models and applications, J. of Interactive Marketing, 1998

Clarke B.S. and Clarke J.L., Predictive Statistics. Analysis and Inference beyond Models, Cambridge University Press, Cambridge UK, 2018.

Frątczak E. Statistics for Management and Economics, SGH , Warszawa , 2015

Rubin D., Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons: Hoboken, 1997.

Schafer J. L., Analysis of Multivariate Incomplete Data, Chapman & Hall: London 1997.

Publikacje własne:

-

Uwagi:

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

ocena z ćwiczeń: 50.00%

Zajęcia w cyklu "Semestr zimowy 2023/24" (zakończony)

Okres: 2023-10-01 - 2024-02-23
Wybrany podział planu:
Przejdź do planu
Typ zajęć:
Laboratorium, 30 godzin więcej informacji
Koordynatorzy: (brak danych)
Prowadzący grup: (brak danych)
Lista studentów: (nie masz dostępu)
Zaliczenie: Przedmiot - Ocena
Laboratorium - Ocena
Skrócony opis:

Advanced analytics. Predictive modeling. Data imputation. Advanced regression techniques. Client Life Time Value.

Pełny opis:

The aim of the advanced course is to provide student with a complex knowledge in advanced business analytics based on cross-sectional and longitudinal data in various contexts such as patient records, customer history and economic surveys. Students learn about the fundamental concepts, scope of use and meaning of the advanced business analytics, data imputation methods, advanced regression techniques and lifetime value models. The concepts are presented along with their practical business applications.

The objective of the advanced business course is to present a complex approach concerning advanced business analytics. During the lectures the philosophy and theoretical background of the advanced statistical business models and their evaluation, as well as practical aspects of using models are presented. The course is made of three main parts: data imputation methods, advanced regression models, mixed models and segmentation and lifetime value models. Students learn about the practice of data imputation and the importance of imputation in the business decision making process. During computer labs students have possibility to experience a complete process of modeling starting with the data preparation, including data imputation, variable specification, variable selection, estimation and verification, evaluation of the quality and finally prediction. The context of BIG DATA for advanced Business Analytics is included. The laboratories will be mainly using SAS. Examples in other software packages (R, Python) will be provided. Classes are part of the Certificate Program: "Data Scientist with SAS System".

Literatura:

Literatura podstawowa:

Aanderud T., Collum R., Kumpfmiller R., An Introduction to SAS Visual Analytics: How to Explore Numbers , Design Reports, and Gain Insight into Your Data. SAS Institute, Cary , 2017.

Advanced Business Analytics - SAS Course Materials. SAS Institute , Cary 2015.

Allison A., Missing Data, Thousand Oaks: Sage University Paper, 2002.

Fader P., Hardie B.G., How to project customer retention, J. of Interactive Marketing, 2007

Hastie T., Tibshirani R., Friedman J., The elements of statistical learning. Data Mining, Inference, and Prediction. Second Edition, Springer: New York, 2009.

James G., Witten D., Hastie T., Tibshirani R., An Introduction to Statistical Learning with Applications in R, Springer: New York, 2013.

Korczyński A, Review of methods for data sets with missing values and practical applications, Śląski Przegląd Statystyczny, Wrocław, Uniwersytet Ekonomiczny we Wrocławiu, 2014, s. 83-104.

Kuhn M., K. Johnson, Applied Predictive Modeling, Springer, New York, 2013.

Linoff S.G., Data Analysis Using SQL and Excel, Second Edition, Wiley, 2016

Little A, Rubin D., Statistical Analysis with Missing Data. John Wiley & Sons: Hoboken 2002.

Malthouse E.C., Segmentation and Lifetime Value Models Using SAS, SAS Institute, 2013

Molenberghs G., Kenward M. G , Missing Data in Clinical Studies, John Wiley & Sons: Chichester, 2007

Ribeiro J., Business Survival Analysis Using SAS: An Introduction to Lifetime Probabilities. SAS Institute, Cary , 2017.

Silvia J., Iqbal A., House S. Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets, SAS Institute, Cary 2017.

Svolba G., Applying Data Science. Business Case Studies, SAS Institute: Cary, NC, 2017.

Svolba G., Data Quality for Analytics Using SAS, SAS Institute: Cary, NC, 2012.

Literatura uzupełniająca:

Berger P., Nasr N., Customer lifetime value: Marketing models and applications, J. of Interactive Marketing, 1998

Clarke B.S. and Clarke J.L., Predictive Statistics. Analysis and Inference beyond Models, Cambridge University Press, Cambridge UK, 2018.

Frątczak E. Statistics for Management and Economics, SGH , Warszawa , 2015

Rubin D., Multiple Imputation for Nonresponse in Surveys, John Wiley & Sons: Hoboken, 1997.

Schafer J. L., Analysis of Multivariate Incomplete Data, Chapman & Hall: London 1997.

Publikacje własne:

-

Uwagi:

Kryteria oceniania:

egzamin tradycyjny-pisemny: 50.00%

ocena z ćwiczeń: 50.00%

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