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Course Catalog

BAN 500 Introduction to Business Analytics 3 Credits
As an introductory course to the program, the course will cover topics on the conceptual framework of business analytics, various sectoral application areas and a general introduction to analytical methods used. The course will also cover success stories from different sectors where business analytics is applied, and big data analytics in general, including its application areas, as a new and emerging area of interest.
Last Offered Terms Course Name SU Credit
Fall 2020-2021 Introduction to Business Analytics 3
Fall 2019-2020 Introduction to Business Analytics 3
Spring 2018-2019 Introduction to Business Analytics 3
Fall 2017-2018 Introduction to Business Analytics 3
Fall 2016-2017 Introduction to Business Analytics 3
Prerequisite: __
Corequisite: BAN 500R
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 500R Introduction to Business Analytics 0 Credit
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: BAN 500
ECTS Credit: NONE ECTS (NONE ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 502 Judgment and Decision Making 3 Credits
This course presents an overview of decision making support methodologies and emphasizes the design of decision support systems using management science models such as production planning, logistics, employee scheduling, stock trading simulation, and portfolio optimization. These systems are developed using Microsoft Excel and VBA. VBA fundamentals are also covered in the course.
Last Offered Terms Course Name SU Credit
Spring 2020-2021 Introduction to Decision Making 3
Spring 2019-2020 Introduction to Decision Making 3
Fall 2018-2019 Introduction to Decision Making 3
Fall 2017-2018 Introduction to Decision Making 3
Fall 2016-2017 Introduction to Decision Making 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 503 Management Information Systems 3 Credits
Informational roles of a manager include receiving, processing, and transmitting information for the purpose of organizational decision-making. This course covers topics such as basics of information technology, the concept of information itself within the context of organizational decision-making, information system design and implementation, managerial implications of information systems for competition and cooperation, e-business and information-decision systems.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 504 Data Mining with SAS Enterprise Miner 3 Credits
The ability to understand, analyze and interpret Big Data for business purposes has become ever more important in the last few years. In order to make intelligent decisions, one must have access to data and information. The main issue is thus, how does one approach large quantities of data with the purpose of intelligent decision- making? The purpose of this course is to introduce the concepts, techniques, tools, and applications of data mining, using a commercially available data-mining software. The material is approached from the perspective of a business analyst, with an emphasis on supporting tactical and strategic decisions. Students should expect to get hands dirty with real data and analysis software, to perform some common data-mining tasks and earn skill as a business analyst.
Last Offered Terms Course Name SU Credit
Spring 2019-2020 Data Mining with SAS Enterprise Miner 3
Spring 2018-2019 Data Mining with SAS Enterprise Miner 3
Spring 2017-2018 Data Mining with SAS Enterprise Miner 3
Spring 2016-2017 Data Mining with SAS Enterprise Miner 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 505 Predictive Analytics 3 Credits
This course introduces basic concepts and models of supervised and unsupervised statistical learning models . The topics include, multiple regression, logistic regression, classfication, resampling methods, subset selection, the ridge, the lasso, tree-based methods, support vector machines, principal component analysis, and clustering.
Last Offered Terms Course Name SU Credit
Fall 2020-2021 Predictive Analytics 3
Spring 2019-2020 Predictive Analytics 3
Spring 2018-2019 Predictive Analytics 3
Spring 2017-2018 Predictive Analytics 3
Spring 2016-2017 Predictive Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 520 Markov Decision Process 3 Credits
Markov Decision Process (MDP) is a decision-making framework solved by dynamic programming. This powerful mathematical tool optimizes decisions in situations where the state of the system dynamically evolves and the decision maker is not in full control of the outcome of her actions. This course is divided in three parts. The first part will focus on modelling business and engineering situaitons via MDPs. Problems such as inventory managemen, healthcare and medical decision-making, revenue management and production planning and control will be discussed and modelled as MDP. The second part discusses popular and effective solution algorithms such as linear programming, value iteration and policy iteration. Finally, in the third part scientific literature on various application of MDPs is reviewed and open problems are discussed.
Last Offered Terms Course Name SU Credit
Fall 2018-2019 Markov Decision Process 3
Fall 2017-2018 Markov Decision Process 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 521 Prescriptive Analytics 3 Credits
The main goal of this course is to present the basic principles and techniques of mathematical modeling that will aid managerial decisions. With case analyses, assignments, and classroom discussions, students will learn the assumptions, limitations and the effective use of the analytical methods such as optimization, Monte Carlo simulation, discrete-event simulation and decision trees. The focus will be on model formulation and interpretation of results, not on mathematical theory. This course is designed for program students with an interest in formal decision modeling. Therefore, the emphasis is on models that are widely used in diverse industries regardless of the functional areas.
Last Offered Terms Course Name SU Credit
Spring 2018-2019 Prescriptive Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 522 Revenue Management 3 Credits
Revenue management is concerned with two types of demand decision: quality (how to allocate capacity to different market segments, when to withhold a product from sale etc.) and price (how to set prices, how to price across product categories, over time etc.). This course aims to introduce students to the tools and conceptual frameworks of revenue management and its applications in diverse industries such as tourism, hospitality, manufacturing and fashion.
Last Offered Terms Course Name SU Credit
Spring 2018-2019 Revenue Management 3
Spring 2016-2017 Revenue Management 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 523 Group Decision Making under Multiple Criteria 3 Credits
This course introduces the students to various methods of enhancing creativity and group decision-making; the various phases and stages of group decision making, It provides students the context for; the scope of; the similarities and the differences in; the breadth and the depth of; Group decision making processes and techniques using hands-on learning techniques as much as possible and practicable. The content is based on pros and cons of group decision making, when and why’s, Classification of approaches , Analyzing Decision making methods for implicit(voting) and explicit multiattributes and multiple decision makers.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 524 CRM using Location Intelligence 3 Credits
This course combines customer relationship management (CRM), a key notion in modern-day customer-centric marketing activities, with the emerging field of location intelligence, i.e. use of location data in business decision making. The course is co-taught with a Division Manager in banking industry who is also a CRM expert. After introducing fundamental concepts in CRM as well as geographic data and Geographic Information Systems (GIS), the instructors cover several banking cases where location information is used in CRM and marketing activities, campaigns and promotions to increase the accuracy of customer segmentation and targeted marketing. A leading GIS software package is used throughout the course for hands-on exercises and project work. The final deliverable of the course is a project analysis team report.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 525 Microeconomics I 3 Credits
Consumer and demand theory, production and theory of the firm; competitive markets, partial and general equilibrium theory.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 526 Business Intelligence and Decision Support Systems 3 Credits
The main objective of this course is for the student to develop an understanding of the role of computer based information systems in direct support of managerial decision making (nowadays commonly referred as business intelligence). Spesifically, at the end of this course each student should develop : a) Knowledge about managerial decision making, business intelligence, decision support systems and how to they relate to other types of information systems, b) Knowledge about DSS development methodolies and enabling technologies (such as Expert Systems, Neural Networks, Knowledge Management, Data Warehousing and Data Mining) c) Knowledge about DSS enabling software packages -a general understanding and some hands-on capabilities.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 527 Descriptive Analytics 3 Credits
This course aims to provide a review of methods for statistical inference, and develop an understanding of how these tools can be applied in a variety of business problems. The emphasis of this course would be on applications, through practical examples and cases. A variety of statistical software will be introduced. Topics covered include descriptive statistics, probability distributions, hypothesis testing, regression, design of experiments and analysis of varience.
Last Offered Terms Course Name SU Credit
Fall 2020-2021 Descriptive Analytics 3
Fall 2019-2020 Descriptive Analytics 3
Fall 2018-2019 Descriptive Analytics 3
Fall 2017-2018 Descriptive Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 528 Microeconomics II 3 Credits
Choice under uncertainty; basic game theory; imperfect competition, strategic interaction, entry; adverse selection, signalling, screening, moral hazard; mechanism design; general equilibrium under uncertainty; axiomatic and coalitional bargaining, cooperative models.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 529 Econometrics 3 Credits
Classical linear regression model, generalized least squares generalized method of moments, qualitative dependent variable models, time series analysis.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 531 Systems Simulation 3 Credits
Modeling and analysis of production and service systems through the use of discrete-event simulation; world views in simulation; input modeling; random number and variate generation; output analysis; verification and validation issues.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 532 Machine Learning 3 Credits
Machine learning aims to develop computer programs that improve their performance through experience by capturing relevant abstractions of past training input. This course will cover topics in machine learning such as concept learning with version spaces, learning decision trees, statistical learning methods, genetic algorithms Bayesian learning methods, explanation-based learning, and reinforcement learning. Theoretical aspects such as inductive bias, the probably approximately correct learning, and minimum description length principle will also be covered.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 533 Stochastic Processes 3 Credits
Poisson and renewal processes; discrete and continuous Markov chains; applications in queuing, reliability, inventory, production, and telecommunication problems; introduction to queuing networks and network performance analysis.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 535 Neural Networks 3 Credits
This course covers neural networks as computational models. Topics include the classification problem and the modeling of a basic neuron as a classifier, perceptrons, perceptron convergence theorem, class separability, multi-layer perceptrons, backpropagation algorithm for training, recurrent networks, associative memory, Hopfield and Kohonen networks, applications to speech, vision and control problems.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 537 Systems Dynamics 3 Credits
Systems thinking and the system dynamics worldview; methods to elicit and map the structure of complex systems and relate those structures to their dynamics; tools for modeling and simulation of complex systems; applications including corporate growth and stagnation, the diffusion of new technologies, business cycles, the use and reliability of forecasts, the design of supply chains, service quality management, project management and product development, the dynamics of infectious diseases.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 539 Data Mining 3 Credits
Data mining can be viewed as lossy data reduction and learning techniques that are designed to handle massive data sets containing large numbers of categorical and numeric attributes. This course covers topics in data mining and knowledge discovery structured and unstructured databases such as data integration, mining, and interpretation of patterns, rule-based learning, decision trees, association rule mining, and statistical analysis for discovery of patterns, evaluation and interpretation of the mined patterns using visualization techniques.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 599 Graduate Seminar 0 Credit
This seminar course provides a non-credit framework for the continuous monitoring and collegial discussion of MA students' thesis research and writing, which they are expected to accomplish under the supervision of a Faculty member from the relevant field.
Last Offered Terms Course Name SU Credit
Spring 2020-2021 Graduate Seminar 0
Fall 2019-2020 Graduate Seminar 0
Fall 2018-2019 Graduate Seminar 0
Fall 2017-2018 Graduate Seminar 0
Fall 2016-2017 Graduate Seminar 0
Prerequisite: __
Corequisite: __
ECTS Credit: 3 ECTS (3 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 600 Master Thesis 0 Credit
Provides a non-credit framework for the continuous monitoring and collegial discussion of MA students' thesis research and writing, which they are expected to accomplish under the supervision of a Faculty member from the relevant field over the second year of their course-work.
Last Offered Terms Course Name SU Credit
Spring 2022-2023 Master Thesis 0
Fall 2022-2023 Master Thesis 0
Spring 2021-2022 Master Thesis 0
Fall 2021-2022 Master Thesis 0
Spring 2020-2021 Master Thesis 0
Fall 2020-2021 Master Thesis 0
Spring 2019-2020 Master Thesis 0
Fall 2019-2020 Master Thesis 0
Spring 2018-2019 Master Thesis 0
Fall 2018-2019 Master Thesis 0
Spring 2017-2018 Master Thesis 0
Fall 2017-2018 Master Thesis 0
Prerequisite: __
Corequisite: __
ECTS Credit: 30 ECTS (30 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 801 Marketing Analytics 3 Credits
This course is about generating marketing insights from empirical data in such areas as segmentation, targeting and positioning, satisfaction management, customer lifetime analysis, customer choice, and product and price decisions using conjoint analysis. This will be a hands-on course based on the Marketing Engineering approach and Excel software
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Marketing Analytics 3
Spring 2022-2023 Marketing Analytics 3
Spring 2021-2022 Marketing Analytics 3
Spring 2020-2021 Marketing Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 803 Operations Analytics 1.5 Credits
This course introduces analytical methods for various operational, tactical, and strategic decisions in operations management function of the firms. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, capacity and revenue management, and supply chain management
Last Offered Terms Course Name SU Credit
Spring 2022-2023 Operations Analytics 3
Fall 2022-2023 Operations Analytics 3
Spring 2021-2022 Operations Analytics 3
Spring 2020-2021 Operations Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 804 Artificial Intelligence 3 Credits
This course is a broad technical introduction to fundamental concepts and techniques in artificial intelligence. Topics include expert systems, rule based systems, knowledge representation, search, planning, managing uncertainty, machine learning, and neural networks. Important current application areas of artificial intelligence, such as computer vision, robotics, natural language understanding, and intelligent agents.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 805 Predictive Analytics 3 Credits
This course introduces basic concepts and models of supervised and unsupervised statistical learning models. The topics include, multiple regression, logistic regression, classfication, resampling methods, subset selection, the ridge, the lasso, tree- based methods, support vector machines, principal component analysis, and clustering.
Last Offered Terms Course Name SU Credit
Fall 2023-2024 Predictive Analytics 3
Fall 2022-2023 Predictive Analytics 3
Fall 2021-2022 Predictive Analytics 3
Fall 2020-2021 Predictive Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 806 Time Series Analysis 3 Credits
This course provides an overview of forecasting techniques and models. Models for time series: Time- dependent seasonal components. Autoregressive (AR), moving average (MA) and mixed ARMA- models. The Random Walk Model. Box-Jenkins methodology. Forecasts with ARIMA and VAR models. Dynamic models with time-shifted explanatory variables.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 807 Financial Analytics 3 Credits
An introduction to methods and tools useful in decision-making in the financial industry, including: macroeconomic event studies, analysis of term structures, Morningstar equity data, style analysis, credit card receivables, trading analytics, execution algorithms, etc. This course blends easy-to-use statistical tools with complex machine learning tools and algorithms to equip the participants with the requisite skill set in analyzing data.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Financial Analytics 3
Spring 2022-2023 Financial Analytics 3
Spring 2021-2022 Financial Analytics 3
Spring 2020-2021 Financial Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 809 Project Management in Analytics 3 Credits
This course introduces students to the theory and practice of project management. This course examines the management of complex projects and the tools are available to assist managers with such projects. Some of the specific topics we will discuss include project life cycle models, work break down structure, organization break down structure, cost break down structure, graphical presentations and precedence diagramming, network analysis and scheduling techniques, concepts of system life cycle costing, and cost estimation methods and trade-off analysis, risk management, and monitoring and control.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Project Management in Analytics 3
Spring 2022-2023 Project Management 3
Spring 2021-2022 Project Management 3
Spring 2020-2021 Project Management 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 810 Cyber Security Law 3 Credits
This course examines legal and policy challenges stemming from rapidly evolving cybersecurity threats. Topics include cybercrimes; digital signature law; intellectual property law; digital communication law; cybercrime incidences; laws and regulations for cyber security in the world; ethical issues in cyber security.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 816 Social Media Analytics 3 Credits
This course will examine topics in social data analysis, including influence and centrality in social media, information diffusion on networks, topic modeling and sentiment analysis, identifying social bots, and predicting behavior. This course will demonstrate how AI, network analysis, and statistical methods can be used to study these topics.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 821 Optimization and Simulation 3 Credits
This course introduces the basic principles and techniques of mathematical modeling that will aid managerial decisions. Students will learn how to develop analytical models and use techniques such as linear and mixed integer programming, Monte Carlo simulation, discrete-event simulation and decision trees. The applications are on models that are widely used in diverse business functional areas.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Optimization and Simulation 3
Spring 2022-2023 Optimization and Simulation 3
Spring 2021-2022 Optimization and Simulation 3
Spring 2020-2021 Optimization and Simulation 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 827 Descriptive Analytics 3 Credits
This course aims to provide a review of methods for statistical inference, and develop an understanding of how these tools can be applied in a variety of business problems. The emphasis of this course would be on applications, through practical examples and cases. A variety of statistical software will be introduced. Topics covered include descriptive statistics, probability distributions, hypothesis testing, regression, design of experiments and analysis of variance.
Last Offered Terms Course Name SU Credit
Fall 2023-2024 Descriptive Analytics 3
Fall 2022-2023 Descriptive Analytics 3
Fall 2021-2022 Descriptive Analytics 3
Fall 2020-2021 Descriptive Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 831 Data Warehousing and Business Intelligence 3 Credits
This course introduces the basics of structured data modeling, gain practical SQL coding experience, and develop an in-depth understanding of data warehouse design and data manipulation. It also allows working with large data sets in a data warehouse environment to create dashboards and introduces a variety of business intelligence solutions.
Last Offered Terms Course Name SU Credit
Fall 2023-2024 Data Warehousing and Business Intelligence 3
Spring 2022-2023 Data Warehousing and Business Intelligence 3
Fall 2021-2022 Data Warehousing and Business Intelligence 3
Fall 2020-2021 Data Warehousing and Business Intelligence 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 835 Computational Tools and IT for Analytics 3 Credits
This course explores both the functional and technical environment for the creation, storage, and use of the most prevalent source and type of data for business analysis. Students will learn how to access and leverage information via SQL for analysis, aggregation to visualization, MapReduce, Apache Spark and Graph databases. This course will also give an introduction to a set of tools and techniques for dealing with large data such as Python and R.
Last Offered Terms Course Name SU Credit
Fall 2023-2024 Computational Tools and IT for Analytics 3
Fall 2022-2023 Computational Tools and IT for Analytics 3
Fall 2021-2022 Computational Tools and IT for Analytics 3
Fall 2020-2021 Computational Tools and IT for Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 840 Digitial Transformation & Innovation 1.5 Credits
The digital transformation that has been happening in the industry is leading to the disappearance of borders between cyber and physical systems and creating synergies between them. In order to maintain and improve their firms’ competitiveness, decision makers need to know the technologies, approaches, and best practices that further this transformation. Digital transformation has also helped recognition of the role of innovation in global competitive environment among other operational priorities (cost, quality, flexibility, and delivery). This course, involve an in -depth discussion into such topics, cases, and best practices.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Digitial Transformation & Innovation 1.5
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 845 Digital Transformation 3 Credits
This course is an overview to prepare strategic and organizational transformation of the organizations in today’s digital age. It will cover such topics as environmental analyses for enablers for digital transformation, business transformation, business process management in the digital age, design thinking, the role of IT in business transformation, organization change management, and critical success factors for business transformation.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 853 Database Management 3 Credits
This course gives students hands-on practice and experience in database design and administration along with the fundamental concepts and techniques involved. Topics covered include the entity-relationship model, relational database theory, file structure, indexing and hashing, query processing, crash recovery, concurrency control/transaction processing security and integrity. Creation of tables, views, synonyms and indexes are examined in detail. The use of SQL is considered and highlighted with the help of examples, and used to build the underlining database of an application.
Last Offered Terms Course Name SU Credit
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 871 Design Thinking and Power of Story Telling in Business 1.5 Credits
This course aims at introducing students to new concepts and methods: design thinking and storytelling. Design thinking promotes user-centered innovation, experimentation to cope with the uncertainties that firms face during the innovation process, which rests on some principles, such involvement of users to the innovation or product/service development and design process, problem framing, leveraging empathy with users, experimentation, and diversity. Offering a new method of problem solving, Design Thinking emphasizes the importance of experimenting, learning-by-doing, listening customers, iterations until fin finding a satisfying solution to the problems. Entrepreneurs or managers challenge with not only creating viable solutions to the problems and solutions/innovations to customers and stakeholders where narratives and stories always helped to communicate their vision, and how their innovations would shape the future. Although these stories have improved the communication between and within the firms and their stakeholders, the power of storytelling in business has been widely ignored. Today, with the rise of social media and new communicational channels and tools, storytelling has become more and more critical talent/competence. Providing students with practice-based skills is critical in this course, for this aim, they are required to work on two projects. One of them is based on practicing design thinking process and principles, which students are requested to frame a problem, develop a viable solution, develop a prototype as ensuring user/customer involvement and conduct various experiments to understand the viability of the solution. Second project focuses on storytelling practices; tudents are required to craft an effective story for for the innovation/solution that they develop for the first project. They are also requested to deconstruct and analyze the stories told by classmates.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Design Thinking and Power of Story Telling in Business 1.5
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 892 Applied Advanced Analytics 3 Credits
This is a hands-on course to equip students with ways to prepare a culminating project that follows a multifaceted approach in business analytics. The course employs an end-to-end approach by following CRISP-DM (Cross-Industry Standard Process for Data Mining) throughout the module. The course also recapitulates earlier courses in the program and dives into further intricacies of descriptive, predictive and prescriptive analytics.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Applied Advanced Analytics 3
Spring 2022-2023 Applied Advanced Analytics 3
Spring 2021-2022 Applied Advanced Analytics 3
Spring 2020-2021 Applied Advanced Analytics 3
Prerequisite: __
Corequisite: __
ECTS Credit: 10 ECTS (10 ECTS for students admitted before 2013-14 Academic Year)
General Requirements:
 
BAN 899 Graduation Project 0 Credit
The program requires the conduct and completion of a project. The project topic and content is based on the interest and background of the student. It is to be approved by the faculty member serving as the project supervisor. At the completion of the project, the student is required to submit a final report. The report is to be approved by the project supervisor.
Last Offered Terms Course Name SU Credit
Spring 2023-2024 Graduation Project 0
Spring 2022-2023 Project 0
Spring 2021-2022 Project 0
Spring 2020-2021 Project 0
Prerequisite: __
Corequisite: __
ECTS Credit: 20 ECTS (20 ECTS for students admitted before 2013-14 Academic Year)
General Requirements: