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: |
|
|