Program

THE FIFTH INTERNATIONAL WORKSHOP

Advanced Analytics and Data ScienceSGH Warsaw School of Economics, 7 November 2017

MEDIA PATRONAGE

PARTNER SPONSOR

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SGH Warsaw School of Economics

Building C

Al. Niepodległości 128, 02-554 Warszawa

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With heritage to progress

Academic year 2017/2018 is the 112th year of the SGH Warsaw School of Economics uninterrupted

operation. It has been established in 1906 as August Zieliński’s Private Courses of Commerce for

men. SGH Warsaw School of Economics is the oldest university of economics in Poland and at the

same time one of the Europe’s leading schools of economics. Almost 3000 students graduate

from the school each year. Statistically, every third economist in Poland in the 20th century was our

graduate.

Over 13 000 people are currently attending the SGH Warsaw School of Economics,

studying for a Bachelor’s or Master’s degree. First-cycle Bachelor’s studies offer 6 programmes

in Polish and 4 programmes in English, while second-cycle Master’s studies offer 14 programmes

in Polish and 5 programmes in English. A full description of each programme can be found

at oferta.sgh.waw.pl.

The new field of study Advanced Analytics – Big Data (MA) programme is offered at the SGH from

the middle of academic year 2014/2015. It educates future experts in data acquisition from variety

of sources and its analysis – people who will become one day data scientists. The programme’s

graduates obtain specialized knowledge and competences allowing them to work on a position

of advanced data analysts in production companies, banks, insurance and telecom companies,

public administration and research centres.

Every year the Advanced Analytics and Data Science conference brings together distinguished

speakers and is a place of important discussion on the subject of combining the science and

business needs in times of dynamic development of digital economy. I hope that the participation

in the 5th edition of the Conference, where we will discuss the role of predictive modelling for

business analytics in the era of Big Data, will be a source of many valuable experiences and

inspiration for all of you.

Professor Marek Rocki Rector of SGH Warsaw School of Economics

For 112 years we have been educating economy, science, politics and business professionals.

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Welcome to the Fifth International Workshop

Advanced Analytics & Data Science

For the fifth time the SGH Warsaw School of Economics has the honour to welcome and host the participants of the Advanced Analytics and Data Science International Workshop. The motto of this year’s conference is „Power of Predictive Modelling. Importance of Data Imputation”. It emphasizes the increasingly large role of analytics and predictive modelling and their significant impact on business, economy and the whole society.

We are delighted that Professor Marek Rocki, Ph.D., Rector of the SGH Warsaw School of Economics, has agreed to extend his honorary patronage to the event. Our conference would not have become reality without the huge commitment and active contribution from our partner SAS Polska as well as our sponsors: AMA Institute and Payback and media partners. We would like to thank the exquisite Polish and international speakers as well as the great number of participants from business and academia for having accepted our invitation. The fifth jubilee edition of the conference takes place, when SAS celebrates its 25th anniversary of operations on the Polish market led since the beginning by the Managing Director Alicja Wiecka. We also celebrate the 20th anniversary of SAS and the SGH Warsaw School of Economics cooperation.

Nowadays, data is everywhere, analytics is everywhere and strongly embed into our daily lives – Data is the fuel, analytics is the engine - said Jim Goodnight, SAS CEO, during his opening speech at the 2017 SAS Global Forum conference in Orlando.

In the age of digital transformation the use of business analytics gives us great possibilities to develop and create innovation - said Alicja Wiecka, SAS Poland Managing Director in an interview for ITwiz.

Good predictive analytics combines three elements: data, analytical tools and innovation. During our conference, through lectures and discussion we will try to answer many pressing questions, such as:

• Predictive Analytics - what it is and why it matters? • How business companies use predictive analytics to reduce risk, drive revenue and gain competitive advantage? • How to educate new generations of analysts and data scientists in response to the challenges of the evolving analytics world? • How to transform their knowledge and talents into business value using advanced predictive modelling?

We are living in times where companies and institutions are collaborating with academia to build a pipeline of skilled professionals and to transform the skills of their current employees. We are a part of this analytics world. Bring the Power of Predictive Modelling to your Job, your Organization, Your University and Your Students!

Workshop Information

Registration and Information Desk The Registration and Information Desk will be staffed throughout conference

hours.

Professor SGH Ewa Frątczak Head of Event History and Multilevel Analysis Unit, SGH Warsaw School of Economics

Distinguished Participants, Ladies and Gentlemen

The Important thing is not to stop questioning. Albert Einstein

Contact If you have any questions, please

contact: seminarium@spl.sas.com.

Badges Workshop badges must be worn at all

times to be admitted into sessions.

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Conference Registration

Welcome Address Speech Prof. Marek Rocki Rector of SGH Warsaw School of Economics

Prof. Joanna Plebaniak Dean of the Collegium of Economic Analysis, SGH Warsaw School of Economics

Prof. Irena E. Kotowska Director of the Institute of Statistics and Demography, SGH Warsaw School of Economics

09:00 - 9:30

Using SAS for Multiple Imputation and Analysis of Longitudinal Data language Patricia Berglund Senior Research Associate, Survey Methodology Program, Institute for Social Research, University of Michigan

Steven G. Heeringa Senior Research Scientist and Director, Statistical and Research Design Group, Survey Research Center, Institute for Social Research, University of Michigan, USA

Missing data and imputation in economics and business analytics language Adam Korczyński Assistant, Institute of Statistics and Demography, SGH Warsaw School of Economics, Statistician at QuintilesIMS, POLAND

Managers and analysts: the Chinese wall and how to break it? Siergiej Fuks Co-founder, AMA Institute

Karol Przanowski Co-founder, AMA Institute, POLAND

Social Network Analysis and Artificial Neural Networks to predict churn in telecommunications Carlos Andre Reis Pinheiro Principal Analytical Training Consultant, SAS, USA, Visiting Professor, Data ScienceTech Institute, FRANCE

Challenges in applications of advanced analytics in customer-centric retail marketing Rafał Latkowski Director of Analytics, PAYBACK Polska, POLAND

The SAS Platform - Empowering Analytics Innovations Scott Chastain Director, Global Technology Practice, SAS, USA

Improving predictive modeling, the way for personal carrier. Case based on Kaggle challenge Adam Karwan Boeing Global Services, Research & Rapid Development, Gdańsk, POLAND

9:30 - 9:45

9:45- 10:30

10:30 - 11:00

11:00 - 11:30

11:30 - 12:00

Crowdsourced knowledged as a means of prediction Tomasz Kołodziejczuk Advanced Analytics - Big Data graduate of SGH Warsaw School of Economics POLAND

Coffee Break

Lunch

12:00 - 12.40

12:40 - 13:20

13:20 - 14:00

14:00 - 14:30

14:30 - 15:00

15:00 - 15:20

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Closing Remarks Prof. SGH Ewa Frątczak Head of Event History and Multilevel Analysis Unit, SGH Warsaw School of Economics

Networking Coffee

15:20 - 15:30

15:30

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Agenda and speakers

Using SAS for Multiple Imputation and Analysis of Longitudinal Data language

9:30 - 9:45

Prof. Marek Rocki Rector of SGH Warsaw School of Economics

Prof. Joanna Plebaniak Dean of the Collegium of Economic Analysis, SGH Warsaw School of Economics

Prof. Irena E. Kotowska Director of the Institute of Statistics and Demography, SGH Warsaw School of Economics

Welcome Address Speech

9:45 – 10:30

Patricia Berglund Senior Research Associate, Survey Methodology Program, Institute for Social Research, University of Michigan

Steven G. Heeringa Senior Research Scientist and Director, Statistical and Research Design Group, Survey Research Center, Institute for Social Research, University of Michigan, USA

“Using SAS for Multiple Imputation and Analysis of Longitudinal Data” presents use of SAS to address missing data issues and analysis of longitudinal data. Appropriate multiple imputation and analytic methods are evaluated and demonstrated with an analysis application using longitudinal survey data with missing data issues. The techniques presented are applicable to health care and medical organizations, informational, retail and manufacturing businesses, and academic and governmental environments where missing data problems in longitudinal data exist. The analysis application demonstrates detailed data management steps required for imputation and analysis, multiple imputation of missing data values, subsequent analysis of imputed data, and finally, interpretation of longitudinal data analysis results. Key SAS tools including data management to produce needed data structures and use of PROC MI, PROC MIANALYZE, PROC MIXED, and PROC SGPLOT are highlighted.

Patricia Berglund is a Senior Research Associate in the Survey Methodology Program at the University of Michigan Institute for Social Research (ISR). She has extensive experience in the use of SAS for data management and analysis. She is a faculty member in the ISR's Summer Institute in Survey Research Techniques and also directs the ISR SAS training programs. Patricia is a co-author of "Multiple Imputation of Missing Data Using SAS" and "Applied Survey Data Analysis" and also teaches a SAS Business Knowledge Series class titled "Imputation Techniques in SAS". Her primary research interests are mental health, youth substance issues, and survey methodology. Ms Berglund holds an MBA in Market Research and a BA in Music from Northwestern University in Evanston, Illinois.

Steven G. Heeringa is a Senior Research Scientist at the University of Michigan Institute for Social Research (ISR). He is a member of the Faculty of the University of Michigan Program in Survey Methods and the Joint Program in Survey Methodology. He is a Fellow of the American Statistical Association and elected member of the International Statistical Institute. He is the author of many publications on statistical design and sampling methods for research in the fields of public health and the social sciences. He is the lead author of Applied Survey Data Analysis, Second Edition (Chapman & Hall, 2017), a comprehensive new text on methods for the statistical analysis of complex sample survey data. Steve has over 40 years of statistical sampling experience in the development of the SRC National Sample design, as well as research designs for ISR's major longitudinal and cross-sectional survey programs. Since 1985 Steve has collaborated extensively with scientific colleagues in the design and conduct of major studies in aging, psychiatric epidemiology and physical and mental health. He has been a teacher of survey sampling and statistical methods to U.S. and international students and has served as a sample design consultant to a wide variety of international research programs based in countries such as Russia, the Ukraine, Uzbekistan, Kazakhstan, India, Nepal, China, Egypt, Iran, the United Arab Emirates, Qatar, South Africa and Chile.

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Missing data and imputation in economics and business analytics language 10:30 - 11:00

Adam Korczyński Assistant, Institute of Statistics and Demography, SGH Warsaw School of Economics, Statistician at QuintilesIMS, POLAND

There has been a significant development in methods for missing data over the last few decades. Multiple imputation and likelihood-based approach are in the group of the most advanced methods assuring good properties of the estimates. Whenever business relies on statistical analysis, data is an asset of a company and imputation techniques can be considered as a tool working towards the improvement of the quality of the data. Technically, imputation enables to reduce the bias introduced by missing data and to be more precise about the estimated parameters. On the other hand imputation gives the opportunity to assess the magnitude of nonresponse bias as the difference between the populations of responders and non-responders. In the presentation, imputation methods along with examples of application will be discussed and illustrated.

Adam Korczyński is an assistant at Institute of Statistics and Demography Warsaw School of Economics and statistician at QuintilesIMS. He received master degrees in Quantitative Methods in Economics and Information Systems (2010) and Economics (2011) from SGH Warsaw School of Economics. His research interests are in missing data and imputation methods, Bayesian methods, statistical modeling for clinical trials and statistical programming. He is a co-author of a textbook in statistics and author of two papers in missing data analysis. Since his internship at Event History And Multilevel Analysis Unit SGH Warsaw School of Economics in 2010 he taught courses in statistics for graduate and post-graduate students. He has 5 years of experience in pharmaceutical industry working as a statistician in various therapeutic areas such as cardiology, rheumatology, respiratory diseases and oncology.

Managers and analysts: the Chinese wall and how to break it?11:00 - 11:30

Siergiej Fuks Co-founder, AMA Institute, POLAND

Karol Przanowski Co-founder, AMA Institute, POLAND

Rapid rate of service and product imitation shifts the focus from siloes and non-data driven organizations into entities that build long lasting competitive advantage by wisely transforming data and information into something that brings high value to all stakeholders. It opens a new era of organization-wide efforts, where managers and analysts can no longer rely only on skills and knowledge that were required up till now to make a successful career. This paper shows cumbersome analytical challenges that require to differentiate particular customers’ attributes from organizational ubiquitous operational processes, using practical live examples from several industries. The experience shows that such challenges may no longer be solved, unless managers would deeply understand analytical methodology and intellectual obstacles, whereas analysts would not only understand deeply the business processes, but also get the necessary skills to articulate their findings. In order to achieve this, organizations and general educational system require a significant paradigm change in preparing next-generation data scientists, who will break the Chinese wall and build a new era of organizational leadership.

Siergiej Fuks - experienced manager, who has working experience over 16 years in many countries: Ukraine, the Netherlands, United Kingdom, Czech Republic, Hungary and Poland. Holds 3 diplomas: MBA (Manchester Business School, United Kingdom), MA in Economics (CORIPE, Turin University, Italy) and MA in Mathematics (Lviv University, Ukraine). Analytical, strong in strategic development, sales and operational effectiveness management, as well as reengineering and change management - who values ethics, diversity, passion for teamwork and continuous development through challenging tasks and openness to continuously undermine ‘status quo’.

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Social Network Analysis and Artificial Neural Networks to predict churn in telecommunications

12:00 - 12.40

Carlos Andre Reis Pinheiro Principal Analytical Training Consultant, SAS, USA Visiting Professor, Data ScienceTech Institute, FRANCE

Churn is an inescapable reality in the telecom world. Customers come and go, swayed by the latest disappointments or the latest deals. This churn is costly. It is far more expensive to acquire a customer than to satisfy and retain an existing one. Analytics has proven its value in identifying customers at risk for churn and helping marketers understand how to retain them. In developed markets, major service providers have adopted churn propensity models that have succeeded in reducing the churn rate for post-paid subscribers in an effective way. However, it is possible to improve analytic model results even more, if you acknowledge that consumer behavior and loyalty are increasingly influenced by friends and acquaintances. Imagine the power of understanding social relationships and using that information to better target the customers who count the most. What if you could identify community leaders so you could stop them from influencing their followers to churn? If you knew who those leaders were, you could target them wisely and extend the impact of your marketing and retention efforts. Social network analysis describes customers' behavior, but not in terms of their individual attributes. Rather than basing models on static individual profiles, social network analysis depicts behavior in terms of how individuals relate to each other. In practical terms this approach highlights connections between individuals and how important they might be in viral effect throughout communities and particular groups. For business purposes, social network analysis can be employed to avoid churn, diffuse products and services, and detect fraud and abuse, among many other applications. Social network analysis can also reveals a set of additional input variables about customers' behavior in order to improve predictive models performance. In this way, the predictive propensity models go far beyond demographics and transaction history. These models factor in a host of social network analysis metrics, such as weighted detail about the number, closeness and strength of connections among customers and their communities characteristics. Artificial Neural Networks can therefore account for the traditional customers' descriptors and the new ones provided by the social network analysis outcome, the descriptors in relation to the customers' relationships and possible influence among each other.

Dr. Carlos Andre Reis Pinheiro is a Principal Analytical Training Consultant at SAS and a visiting professor at Data ScienceTech Institute, France. He has been working since 1996, first for a Brazilian shipping company and then for some of the largest telecommunications providers in Brazil, such as Embratel (1999-2000), Brasil Telecom (2000-2008) and Oi (2010-2012). He also worked as a Data Scientist for EMC2 (2014) and Teradata (2015). Dr. Pinheiro has examined business problems in database marketing, data warehousing, and data mining, including pattern recognition, predictive modeling, social networks analysis, optimization and human mobility behavior, across a wide range of departments, as IT, Marketing, CRM, Sales, Revenue Assurance, Fraud, Risk and Finance. Carlos Pinheiro holds a PhD in Engineering from Universidade Federal do Rio de Janeiro (2005). He has accomplished a series of Post-Doctoral research terms over the past years, such as in Optimization (IMPA, Brazil, 2006-2007), Social Network Analysis (Dublin City University, Ireland, 2008-2009), Human Mobility Behavior (Université de Savoie, France, 2012), Urban Mobility Behavior and Dynamic Social Networks (Katholieke Universiteit Leuven, Belgium, 2013-2014) and Urban Mobility and Multi-Modal Traffic (Fundação Getúlio Vargas, Brazil, 2014-2015).

Coffee Break11:30 - 12:00

Karol Przanowski - experienced manager with many years of work knowledge in Credit Scoring. Passionate about building, monitoring and validating analytical models. During his career repeatedly built effective business processes in terms of credit risk, analytical CRM and consumer finance. Open for change, who takes great satisfaction from knowledge sharing with others. The author of plenty scientific publications and great public speaker with plenty speeches delivered at lectures, workshops and conferences. Active teacher of SGH Warsaw School of Economics, holding 3 diplomas: MA in Mathematics (University of Łódź, Poland), Ph.D. of physics (University of Łódź, Poland) and MBA (Polish Academy of Science, Poland).

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Challenges in applications of advanced analytics in customer-centric retail marketing12:40 - 13:20

Rafał Latkowski Director of Analytics, PAYBACK Polska, POLAND

Successful direct marketing for online & offline retailers requires satisfying simultaneously customer and retailer needs under existing limitations eShop or physical point of sale. It implies accepting customer-centricity paradigm in direct marketing and yield in optimization problem that is rather complex to synthetically describe. This optimization problem can be partially supported with advanced analytics, but requires careful selection of applied methods and constantly driven applied research on how to improve each element of analytically supported information chain. Challenges like low-margin or low-volume make challenging selection of proper methods from business and analytical perspective. We need to carefully select methods, more precise and accurate than usually applied in one areas while rough and approximate in the others. We present some successes and failures of such applied research.

Rafał Latkowski is a Director of Data Delivery & Technology at PAYBACK Poland. He graduated in Computer Science and Mathematics at University of Warsaw. Since the beginning of PAYBACK in Poland, Rafał Latkowski has been responsible for Data Science and Business Intelligence projects for the PAYBACK Programme and its partners. For 18 years, he has been dealing with the commercial application of analytics and Data Mining in financial and telecommunication services, loyalty programmes and retail sales networks. Earlier, Rafał Latkowski worked in such companies as Nutech Solutions, SAS Institute and Raiffeisen Bank Polska. Besides business achievements, he has also published scientific papers on Data Mining regarding Missing Data, Distributed Computing and Classifiers’ Stability in production environments. In his free time, he continues his research and didactic work in the field of Data Mining/Data Science.

Lunch13:20 - 14:00

The SAS Platform - Empowering Analytics Innovations14:00 - 14:30

Scott Chastain Director, Global Technology Practice, SAS, USA

Organizations are working to drive the most out of their data, and analytics is the key to unlocking the value of data. It is no longer enough to only find insights in data, but it is critical to those insights into actions, decisions, and outcomes. Welcome to the analytics economy. Delivering on the value of data while maintaining governance is a key objective for many organizations, and SAS has the ability to help. The SAS Platform is purpose built for driving the most value from your data. The platform has consistently adapted to the changing needs of the market, and now SAS® Viya™ continues that heritage by adding new capabilities and user experiences to the platform. This presentation will cover how the SAS Platform has evolved to meet a more diverse set of needs. We will speak about 4 key themes: The open approach to unifying analytic communities, driving innovative approaches to solving new challenges, building for the Cloud, empowering the collaborative culture with a common user experience. Come see how the combination of SAS 9.4 and SAS® Viya™ power the SAS Platform.

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Scott Chastain currently leads several teams that focus on three core areas: Data Management, Business Visualization, and Data and Decision Science. The Data Management Practice encompasses Data Integration, Data Quality, Data Governance, and Master Data Management. The Business Visualization practice supports the breadth of traditional Business Intelligence, as well as the increasing domain of Data and Analytic Visualizations. Although the team works with any size data, a particular focus area is big data discovery and visualizations. The Data & Decision Science team spans multiple data science domains from data mining and machine learning to forecasting and optimization. The team delivers on applying analytics as an integral part of making business decisions in more real time. This included decision management and event stream processing approaches to address the growing need for a more diverse analytical ecosystem. Today, he focuses most of his expertise around big data

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Improving predictive modeling, the way for personal carrier. Case based on Kaggle challenge

Crowdsourced knowledged as a means of prediction

14:30 - 15:00

15:00 - 15:20

Adam Karwan Boeing Global Services, Research & Rapid Development, Gdańsk, POLAND

Tomasz Kołodziejczuk Advanced Analytics - Big Data graduate of SGH Warsaw School of Economics POLAND

Kaggle is a web platform for data science competitions. It allows to connect the world of science and business through online platform that links researchers with real data and problems. Besides creating community, kaggle challenges affect creation of world's best predictive data models. Next to recruitment competitions (e.g. Facebook, Walmart), there are problems defined by CERN and NASA scientists. During the talk I would describe my solution for kaggle Bike Sharing Demand Challenge. In this competition task was to use combined historical number of rented bicycles and weather data to forecast rental demand in the Capital Bikeshare program in Washington, D.C. The proposed model was in top 2% of the best solutions in the world. It was a weighted combination of models based on random forest and gradient boosting decision trees. The discussed solution will be enhanced with data analysis and visualizations.

Although we gather more and more data, engage more computing power in order to model the world, media describe recent times as surprising and unpredictable - especially in regards to economics and politics. What is the best way to arrange often immeasurable information into reliable forecasts? Socioeconomic theories of "wisdom of the crowds" are not that new, however only recently we have efficient tools to check them in the form of prediction markets. This talk will present foundations of the concept and its applications, presenting pros and cons of betting knowledge on the stock market, to be traded by experts in the fields as well as the masses... in order to predict the future.

technologies and SAS capabilities that combine to solve diverse business problems. Scott has helped some of the most recognized organizations globally address an array of challenges ranging from structured real time data integration and analytics to highly unstructured data and operational systems automation. This now leads to many IoT activities as well. A Georgia Tech composites and polymers engineer by education, Scott spend over 12 years in industry prior to joining SAS. He has worked in healthcare, manufacturing, and telecommunications.

15:20 - 15:30

Prof. SGH Ewa Frątczak Head of Event History and Multilevel Analysis Unit, SGH Warsaw School of Economics

Networking Coffee15:30

Closing Remarks

Adam Karwan is a Data Scientist at Jeppesen, a Boeing Company. He has several years of experience in research and teaching at the Silesian University of Technology in Gliwice. He is former employee of R&D departments of FIAT in Turin, Italy and Samsung R&D Institute in Warsaw. His main interest are image processing, recognition and machine learning with a passion for data analysis.

Tomasz Kołodziejczuk, Automatics and Robotics of Warsaw University of Technology and Advanced Analytics - Big Data graduate of Warsaw School of Economics. Runs Obiektywizm.pl, Politikon.org.pl and Non-aggression Institute. Libertarian activist, associated with European Students for Liberty, The Ayn Rand Institute and KoLiber Association. Enjoys backpacking, popculture and promoting rational ideas of the future.

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SGH Warsaw School of Economics is the oldest university of economics in Poland. Statistically, every third economist in Poland in the 20th century graduated from our School. Currently there are about 11 000 BA, MA and PhD students enrolled and every year about 3 000 graduate from SGH. They find employment in banks, financial institutions, consulting firms and multinational companies.

SGH Warsaw School of Economics offers several study programs on bachelor's and master's levels in Polish and English, as well as a choice of doctoral studies, over 150 postgraduate courses and three MBA programs. As the only university in Poland SGH Warsaw School of Economics is a member of CEMS Global Alliance in Management Education, which includes 30 business schools from around the world along with their corporate partners.

SGH Warsaw School of Economics

The number of students, including postgraduate and Ph.D. students:

• 19 000

Number of lecturers:

• 790

Accreditations:

• International Quality Accreditation CEEMAN

• CeQuInt by European Consortium

for Accreditation in higher education (ECA) for

International Economics

• ACCA accreditation for Finance and Accounting

Membership in international organizations:

• CEMS – Global Alliance in Management Education

• European University Association

• Partnership in Management

Address: Al. Niepodległości 162, 02-554 Warszawa

Rector: Marek Rocki

Founding year: 1906

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Brace for the future with the leading player in the local and global Analytics and Data Science Market!

SGH Warsaw School of Economics brings over 20 years of experience in teaching effective programming, new technologies and modelling using SAS software, coupled with a comprehensive offer of Certificate Programs & Specializations.

Calendar of most important dates and events

Two documents signed at the SGH Warsaw School of Economics: a new agreement and an annex to the agreement signed on 22nd February 2005 between SGH and SAS Poland.

New Certificates start at SGH: „SAS Statistical Analyst” for Post-Diploma Studies; “Statistical Analysis of Data using SAS Tools” – basic level, part of Bachelor’s degree course; “Data Scientist with SAS” – advanced level, part of Master’s degree course in “Data Analysis - Big Data”.

New document signed at the SGH Warsaw School of Economics: an agreement between SGH and SAS Poland regarding SAS Visual Analytics. SGH students will receive the Visual Analytics SAS Certificate.

20th September

1st October

17th November

2017

2014

SGH Warsaw School of Economics was granted the „SAS Enterprise Award” for the 20 years of innovative cooperation with SAS. The award was presented at the 20th edition of the annual SAS Forum Conference titled „Business Powered by Analytics”. SAS Poland honored customers who made a special contribution to the development of SAS solutions on the Polish market.

During annual conference editions of SAS Global Forum there is a special session dedicated to the presentation of the most interesting research projects created by students. We are proud to note that the list of winners includes also students of the SGH Warsaw School of Economics. In 2004 it was Przemysław Piotrowski and in 2006 Krzysztof Stępień. In 2014 a graduate of SGH, Rafał Wojdan, was awarded with a prestigious title of the SAS Student Ambassador for his work in the field of business applications and text-mining.

13th October

23rd - 26th March

2017 2004

2007

2014

2002

2014

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Current team of lecturers teaching SAS

New Specialization started at SGH: “Statistical Analysis and Data Mining” based on SAS for master degree students. The first edition of the Certificate.

1st October

2012

During the annual SAS users’ conference SAS Forum International in Copenhagen SGH Warsaw School of Economics was granted the SAS Academic Intelligence Award 2004. It was the recognition of the innovative teaching and research programs based on SAS software realized at SGH.

15 - 17th June

2007

2004

During the visit to Poland of dr. James Goodnight – founder and CEO of SAS – there was held an important meeting with academic community. Among many important points of the discussion with Polish scientists there was a debate about the effective collaboration between business and academia and the role of science in innovative support of modern economy development. During the meeting Certificates for the students of the first edition of the certification program were presented by dr. James Goodnight.

6th November

2002

An agreement regarding the granting of “SAS Statistical Analyst” certificate was signed, between the SGH Warsaw School of Economics, represented by prof. dr hab. Marek Rocki - Rector of the SGH Warsaw School of Economics and SAS Poland, represented by Alicja Wiecka, Managing Director. The Certificate’s coordinators are Ewa Frątczak, Professor of the SGH Warsaw School of Economics and Alicja Wiecka, Managing Director of the SAS Poland.

Team includes representatives of the Event History & Multilevel Analysis Unit – ISiD-KAE – as well as representatives from Colleges & Administrative Units of the SGH Warsaw School of Economics.

22nd February

Renata Małkowska

Wojciech Skwirz

Adam KorczyńskiAleksandra Iwanicka Stanisław ŁobejkoWioletta Grzenda

Piotr Rozenbajgier Urszula ZwierzBarbara SzewczakKarol Przanowski

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The First International Workshop Advanced Analytics and Data Science gathered 152 participants. 47% of them were representing academic sector (including 20 Polish and foreign Universities), 53% participants represented business organizations (45 companies). The participants came from 6 countries.

2013

The Second International Workshop Advanced Analytics and Data Science gathered 162 participants.

49% of them were representing academic sector (including 21 Polish and foreign Universities), 51% participants represented business organizations (42 companies). The participants came from 4 countries.

2014

The Third International Workshop Advanced Analytics and Data Science gathered 140 participants. 37% of them were representing academic sector (including 16 Polish and foreign Universities), 63% participants represented business organizations (41 companies). The participants came from 3 countries.

2015

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The Fourth International Workshop Advanced Analytics and Data Science gathered 134 participants. 46% of them were representing academic sector, 54% participants represented business organizations. The participants came

from 3 countries.

2016

14

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SGH Warsaw School of Economics

Al. Niepodległości 162, 02-554 Warszawa

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