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Business Analytics Minor Program

1.MOTIVATION

The main motivation to start this program is due to the recently growing need for professionals who are educated in decision-making and risk management processes that will take up positions in competitive public and private sector companies, as a result of the marketplaces becoming increasingly global and the digital transformation of business exchanges. While market competition is getting more and more fierce, and the variety and number of types of business risk requires creative strategies, the need to analyze and interpret large-scale databases as a result of technological advances becomes a natural part of using analytics to guide decisions and actions by decision makers. It is therefore of utmost importance for the future of our country to educate professionals who are equipped with tools and techniques that can be used for forecasting, identifying effective approaches for risk mitigation, implementing such approaches and assessing their results.

The program is expected to graduate students who will work in private and public sector companies as well as those who will fulfill the needs of firms and organizations which operate in the consulting and education service sectors. In addition, we anticipate that the graduates of this program will be equipped enough to continue, if they choose, their studies in related graduate programs.

2. PROGRAM

A. DESCRIPTION

The Business Analytics Minor Program offers content on basic applied mathematical economy, qualitative analysis and research techniques, decision analysis, macro and microeconomics, data mining and analysis, stochastic processes, system dynamics and alike, so that the program students can graduate with a strong “Decision Analytics” background and use this background to exploit business databases in making more sound decisions.

The program targets all undergraduate students who are interested in Business Analytics, while it is expected that it will appeal more to the students who are registered in programs such as management, management science, manufacturing systems, industrial engineering and economics.

B. REQUIREMENTS

Students may apply to this Minor program no earlier than the beginning of the semester that follows the semester where they declare their undergraduate major program, and no later than the beginning of the sixth semester of their studies. To get admitted, their overall cumulative GPA must be at least 2.72, and their application must be approved by the Faculty Executive Board.

Students must have successfully completed all credit courses taken in the undergraduate program prior to the semester in which the students apply for admission to the Minor program.

Students who are registered in a Minor Program must complete all courses required by that program and hence complete the total credit requirement, and fulfill other requirements of the program. The minimum required number of credits to complete is 18.

 The number of credits and courses required to graduate from the Business Analytics Minor Program are:

Course Category SU Credits ECTS Credits Courses
Required 6 12 2
Core-Elective 6 12 2
Area-Elective 6 12 2
Total 18 36 6

Students who successfully complete the course requirements with at least 2.72 GPA from these courses will be awarded a certificate.

C. CURRICULUM

Required Courses:

All courses below are required. It is enough to take one of the course MGMT 203 or MATH 306.

Course Code Course Name SU Credits ECTS Credits Faculty

MGMT 203 
      or
MATH 306

Introduction to Data Analysis and Research in Business 
  or 
Statistical Modeling
3 6

SOM
or
FENS

OPIM 390

Introduction to Business Analytics 3 6

SOM

SOM: School of Management
FENS: Faculty of Engineering and Natural Sciences

Core-Elective Courses: 2 Courses

2 course in total must be taken from the pool below. (Only one of OPIM 410 or IE 405 can be taken)

Course Code Course Name SU Credits ECTS Credits Faculty
OPIM 302 Management Information Systems 3 6 SOM
OPIM 402 Analytics for Business Decisions 3 6 SOM
OPIM 405 Management Decision Support Systems 3 6 SOM
OPIM 410 Decision Making Under Uncertainty 3 6 SOM
IE 405 Decision Analysis 3 6 FENS

SOM: School of Management

FENS: Faculty of Engineering and Natural Sciences

Area-Elective Courses: 2 Courses

2 course must be taken from the pool below. The extra courses taken from Core Elective pool, are directly counted towards "Area Elective".

Course Code Course Name SU Credits ECTS Credits Faculty
OPIM 404 Business Process Analysis and Design 3 6 SOM
OPIM 406 Customer Relationship Management Using Location Intelligence 3 6 SOM
OPIM 407 Business Intelligence and Decision Support Systems 3 6 SOM
OPIM 408 Business Intelligence & Marketing Analytic 3 6 SOM
OPIM 411 Fashion Industry Operations and Pricing 3 6 SOM
OPIM 450 Selected Topics in Operations Management I 3 6 SOM
MKTG 401 Marketing Research 3 6 SOM
MKTG 405 Marketing Strategy 3 6 SOM
CS 404 Artificial Intelligence 3 6 FENS
CS 412 Machine Learning 3 6 FENS
ECON 301 Econometrics 3 7 FASS
ECON 401 Applied Econometrics 3 6 FASS

SOM: School of Management
FENS: Faculty of Engineering and Natural Sciences
FASS:Faculty of Arts and Social Sciences 

COURSE DESCRIPTIONS

Required Courses

MGMT 203 - Introduction to Data Analysis and Research in Business: This course teaches practical perspectives on research design, data collection, and analysis useful for managers. The scientific method, defining a hypothesis, sources and validity of data, sampling, examining distributions of and relationships between data, basic inferential statistics, linear regression, and ethics in research are topics covered in the course. The course follows a hands-on, skill-building approach using mainly MS Excel and appropriate add-ins for statistical analysis.

Prerequisites: MATH 203

MATH 306 - Statistical Modeling: Statistical inference; estimation, confidence intervals, hypothesis testing; analysis of variance; goodness of fit tests; regression and correlation analysis; Bayesian methods; introduction to design of experiments; use of statistical software.

Prerequisites: MATH 203

OPIM 390 - Introduction to Business Analytics: As an introductory course to this Minor Honors 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.

Prerequisites: ---

Core-Elective Courses

OPIM 302 - Management Information Systems: This course provides a broad introduction to management of information systems. The course discusses tools and techniques for analyzing, designing, building and implementing information systems, taking into account both technological and business factors. Interaction between information systems, competitiveness and organizational design are also covered.

Prerequisites: ---

OPIM 402 - Analytics for Business Decisions: The primary goal of this course is to show the relevance and importance of mathematical modeling as a process of understanding and solving a large variety of complex business problems. The course is oriented toward both problem formulation and problem solving. It uses MS Excel spreadsheets to formulate and solve problems from a wide variety of business areas, such as operations management, finance, accounting, human resources, and marketing. The focus is on  mathematical programming techniques of linear programming and integer programming.

Prerequisites: MGMT 203

OPIM 405 - Management Decision Support Systems: This course presents an overview of decision 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.

Prerequisites: MGMT 203

OPIM 410 - Decision Making Under Uncertainty: This course introduces the theory and practice of decision processes under uncertainty. Under this main topic, it covers such sub-topics as the use of decision trees and influence diagrams in solving decision-making problems, assessing probabilities while modeling under uncertainty, analysis using Bayesian statistical models, value of sampling and perfect information, attitudes towards risk, and the utility theory.

Prerequisites: MGMT 203

Area-Elective Courses

OPIM 404 - Business Process Analysis and Design: This course presents the concepts and tools required for analyzing and designing business processes. It emphasizes the process view of organizations and how to manage processes based on this view. Important process performance measures, process redesign and the associated organizational implications are discussed. Basics of event simulation are introduced to provide students with a strong process analysis methodology. Simulation software is utilized to provide students with hands on experience.

Prerequisites: MGMT 203

OPIM 406 - Customer Relationship Management Using Location Intelligence: 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.

Prerequisites: ---

OPIM 407 - Business Intelligence and Decision Support Systems: 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). Specifically, at the end of this course each student should develop: a) Knowledge about managerial decision making, business intelligence, decision support systems and how they relate to other types of information systems. b) Knowledge about DSS development methodologies and enabling technologies (such as Analytical Hierarchy Processes, Group Support Systems, 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. 

Prerequisites: MGMT 203

OPIM 408 - Business Intelligence & Marketing Analytic: Business Intelligence (BI) mainly refers to extracting and analyzing business data in order to make more informed decisions in a rapidly changing business environment. Marketing Intelligence (MI) can be defined as the practice of gathering, and making sense of the gathered data by analyzing them to accurate decision making in determining relevant market opportunities relevant to a company. This course aims at introducing a variety of techniques and tools that can help businesses tackle problems that they face in today's rapidly changing business environment. The course relies on solving a variety of hands on business and marketing problems, as well as case studies.

OPIM 411 - Fashion Industry Operations and Pricing: For over a decade, the fashion industry has been moving towards a global model, which has brought about the need for advanced operations management and pricing methods. This course aims to provide an introduction to these applications. In the first part of the course, an overview of the global fashion retail industry will be provided, with focus on different business models. In the second part of the course, each part of the fashion industry value chain will be discussed, including innovation and new product development in fashion, supply chain management applications in fashion retail, consumer segmentation and pricing, logistics in fashion retail and distribution strategies, brand positioning, extension and growth and customer relationship management. 

OPIM 450 - Selected Topics in Operations Management I: Topics of these courses will be announced when they are offered. 

Prerequisites: ---

MKTG 401 - Marketing Research: This course covers planning, designing, conducting, and interpreting marketing research. All the concepts taught will be put in practice through team projects. The practical applications are intended to create an understanding of how market information can be used in solving marketing problems and designing marketing strategy. 

Prerequisites: MKTG 301 and MGMT 203

MKTG 405 - Marketing Strategy: This course develops an understanding of and the skills and experiences needed in formulating and planning a marketing strategy. It gives the students an opportunity to analyze a market, set objectives, developing a marketing strategy, and implement the strategy in a set of realistic situations provided by a computer simulation game.

Prerequisites: MKTG 301

CS 404 - Artificial Intelligence: 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, will be discussed. 

Prerequisites: CS 201

CS 412 - Machine Learning: This is double coded undergraduate/graduate course on machine learning and statistical pattern recognition. The course will start with an overview of probability and continue with the aim, types (supervised, unsupervised…), and limitations of machine learning. We will then cover i) supervised learning techniques (Bayesian decision theory, parametric methods, nearest neighbor methods, decision trees, neural networks, support vector machines) and ii) unsupervised learning techniques (clustering and dimensionality reduction). The course will be quite hands-on, with weekly homeworks. 

Prerequisites: ---

ECON 301 - Econometrics: Simple linear regression, least squares, generalized least squares; goodness of fit; prediction; inference, confidence intervals and hypothesis testing; empirical modeling of economic theory; introduction to econometric packages. 

Prerequisites: MATH 306 and ECON 204

ECON 401 - Applied Econometrics:The purpose of this course is to provide students with state of the art econometric methods for empirical analysis of micro data (individuals, households, firms etc.). Issues related to specification, estimation and identification of different models with cross-section and panel data will be studied. The course has an emphasis both on the econometric techniques and their applications to different topics. Students are expected to read assigned papers and undertake numerous practical assignments using a modern econometric software package.

Prerequisites: ECON 301