Curriculum of Business Analytic

Program Map
First Year First Semester Classes
Course Code Course Title Credit
DSC 703 Statistics with R Application 3
DSC 705 Mathematical Application with MATLAB 3
DSC 707 Analysis and Design of Algorithms 3
BAN 703 Business Intelligence 3
BAN 705 Marketing Analytics 3
First Year Second Semester Classes
Course Code Course Title Credit
BAN 702 Financial Accounting 3
BAN 710 Digital Innovation and Transformation 3
AVL 702 Supply Chain Management 3
BAN 712 Business Strategy and Analytics 3
ELEC I Elective Course 3
Second Year First Semester Classes
Course Code Course Title Credit
ELEC II Elective Course 3
BAN 700 Project 3
BAN 701 Thesis 6
Required Courses
In this course, Descriptive and Inferential Statistics, Measurement Scales, Visualizing data, frequency histograms, Measures for central tendency: mean, median, mode, Measures of variability: range, quartiles, standard deviation, skewness and variance, Data Visualization, Probability Methods, Discrete Probability Distributions, Continuous Probability Distributions, Distributions of Sample Statistics, Confidence Interval Estimation, Hypothesis Testing, Simple Linear Regression, Multiple Linear Regression and Analysis of Variance will be taught by using R programming applications.
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This course focuses on the mathematical methods and models that are required to understand investigate models. Topics may include limits, sequences and series, set theory; univariate and multivariate calculus; matrix algebra and systems of linear equations; static and dynamic optimization; differential-difference equations and applications in models.
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This course covers the main approaches to design and analysis of algorithms including important algorithms and data structures, and results in complexity and computability. The main content consists of Search and Sorting, Eid Al-Adha break, Divide and Conquer Algorithms, Graphs, Project Proposal, Dynamic Programming, Greedy Algorithms, Randomized Algorithms, P and NP, Work with NP Hard Problems, Partial Recursive function, Computations and Unsolvable Problems.
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This course provides an understanding of the main accounting statements together with an awareness of basic accounting principles, terminology and techniques, so that students can be able to analyze financial reports and interpret financial information. In this course, special emphasis will be given to analyzing and controlling an airline’s financial performance together with an introduction to an airline’s capital structure and cost classification, cash management and financial planning.
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This course provides an understanding of the application of software technologies that enables business users to make better and faster decisions based on enterprise data. During the course, students are introduced to various topics, such as Data Warehousing and will be given the opportunity to create Business Intelligence solutions. Students will learn the principles and best practices for how to use data in order to support fact-based decision-making. Emphasis will be given to applications in marketing, where the use of BI helps in, e.g., analyzing campaign returns, promotional yields, or tracking social media marketing; in sales, where the use of BI helps performing sales analysis; and in application domains such as Customer Relationship Management and e-Commerce. Practical experience will be gained through developing a BI project (case-study) using leading BI software.

Learning Outcomes of the Course

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Topics Covered within the Course

  • Changing business environments and computerized decision support
  • Intelligence creation, use and governance
  • Data warehousing
  • Business performance management
  • Data mining
  • Text and web mining
  • BI implementation: integration and emerging trends

Prescribed Textbooks

Turban, E., Sharda, R., Delen, D., & King, D. (2010). Business intelligence: A managerial approach. 2nd Edition. Upper Saddle River, NJ: Pearson Prentice Hall.
Michalewicz, Z., Schmidt, M., Michalewicz, M., & Chiriac, C. (2007). Adaptive business intelligence. Springer, Berlin, Heidelberg.
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This course aims to cover topics in marketing analytics, an area that is of utmost importance for the development of sound marketing strategies and tactics. Due to technological developments and the proliferation of high-quality data, marketing is becoming an increasingly quantitative profession. This means that marketing professionals should not only be creative, they also must have a solid background in marketing analytical tools in order to make sense of all the available data. It will be particularly useful for students who want to pursue a career in quantitative marketing and marketing consulting. To recognize how analytics can be used to optimize all areas of marketing:
  • Consumer behavior prediction
  • Advertising targeting and optimization
  • Social media and new platforms
  • Mobile experience and outreach

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The goal of this course is designed to equip students to confidently help conceive, lead and execute digital innovation initiatives and develop new business models for existing and insurgent organizations. The basic premise of the course is that the digital revolution is rapidly transforming the fundamental nature of many companies in a wide range of industries and executives, entrepreneurs and general managers need to understand the economics, technology paradigms and management practices of innovating in digital-centric businesses to ensure corporate and personal success. The course is intended for students pursuing business careers in which digital technologies will be critical to the development of new products and services, e.g., entrepreneurial start-ups, consulting and venture capital, and senior positions in marketing, R&D, and strategy.
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The content of this course is based on drivers and obstacles of supply chain, theory and principles of supply chain management, network design in the supply chain, mathematical models for supply chain network design, managing uncertainty in the supply chain, stochastic models for inventory management and pricing and revenue management in the supply chain. In addition to that, the topic of supply chain risk management and sustainability will be covered and the ERP systems and applications in supply chain management will be emphasized in this course.
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Elective Courses in Marketing
This course provides a broad overview of the issues managers face in the selection, use, and management of information technology (IT). Increasingly, IT is being used as a tool to implement business strategies and gain competitive advantage, not merely to support business operations. Using a case study approach, topics include information technology and strategy, information technology and organization, and information technology assets management. 
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Elective Courses in Aviation Logistics
In this course, formulating, analyzing, and solving mathematical models that represent real-world problems will be examined. For the purpose of enabling good decision making in supply chain, business analysis in Excel, linear and integer programming, network flow problems, optimization problems will be provided and as an optimization software GAMS will be used.
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Elective Courses in Data Science
This course covers the statistical tools needed to understand empirical research and to plan and execute independent research projects. Topics include statistical inference, regression, dimension reduction, data clustering, similarity, neighbours and machine learning and evaluation of policies and programs.
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The course content includes statistical inference in nonlinear models, nonlinearity in regression, dimension reduction, data clustering, similarity, neighbours and homogeneity/heterogeneity and evaluation of policies and programs. Students will learn the principles and best practices for how to use big data in order to support fact-based decision-making. Emphasis will be given to applications in various data which has nonlinearity in big data.
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Topics may include essentials of stochastic integrals and stochastic differential equations. Probability distributions and heavy tails, ordering of risks, aggregate claim amount distributions, risk processes, renewal processes and random walks, markov chains, continuous Markov models, martingale techniques and Brownian motion, point processes, diffusion models, and applications in various subject related data science.
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This course covers machine learning and statistical pattern recognition. Topics include: supervised learning, unsupervised learning, learning theory; reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing and evaluation of policies and programs.
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This course provides an understanding of the application of software technologies that enables users to make better and faster decisions based on various data. This course covers the statistical tools needed to understand empirical research and to plan and execute independent research projects. Topics include statistical inference, regression, generalized least squares, instrumental variables, simultaneous equations models, and evaluation of policies and programs.
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This course covers the degree develops practical workplace competencies that meet current and future challenges through a real world coursework utilizing personalized academic mentoring and tutoring. The coursework focuses on introduction level of multivariate statistics, factor analysis, principal component analysis, period-grams, state space, frequency domain, Fourier function, functional regression analysis, bootstrapping and asymptotic theory of tests and estimators.
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