Managing with Analytics (BANA200)

Every business is now a data business. Managing with analytics starts with deciding strategic data needs. Then this data is used to improve business decisions and operations and to yield benefits and profits. This course describes the concepts and processes of sourcing & collecting data and turning data into insights. Managing and competing with analytics requires creating the technology and data infrastructure and building data competencies in the organization. In the meanwhile, data ethics and governance ensures that data does not become a liability. To this end, the course also reviews organizational and technological infrastructure, as well as data governance. Throughout the course, illustrations and case problems are provided for demonstrating how data strategy is executed in practice.

Credit Hours : 3

Course Learning Outcomes

1. Describe the significance of data in today’s business, and how business analytics plays a critical role in creating value in decision making.
2. Explain how data helps organizations improve decisions and operations, and increase revenues.
3. Determine the sources of data, and how to ethically capture, capitalize and leverage on data, in order to increase the return on investment (ROI).
4. Apply various types of statistical and analytics concepts, techniques, and tools in the data analytics process, and being able to evaluate their applicability and performance.
5. Analyze how technology infrastructure and human capital can be built for managing with analytics.

Foundation of Business Information Management (BANA220)

This course introduces business school students to the concepts and practices in the field of Information Management (IM) with particular emphasis on how today’s firms utilize emerging digital technologies and systems to achieve corporate objectives. Students will be provided with opportunities to explore the basic concepts and principles of IM as well as apply these concepts and principles to practical business cases. The course explores key topics such as data governance, information security, data quality management, information lifecycle, and the ethical and legal considerations surrounding information management. Advanced topics, such as Artificial Business Intelligence, Cloud Computing, Big Data, and Internet-of-Things will also be introduced throughout the course to foster greater depth of understanding, to challenge students to think about established issues in new ways, and to highlight gaps in our current understanding of the unique challenges associated with the management of information in global digital firms.

Credit Hours : 3

Course Learning Outcomes

1. Define and explain key concepts and terminologies related to business information management and related technologies.
2. Describe how various types of information management systems provide the information needed to gain critical business information to support data driven decision making for the different levels and functions of the organization.
3. Explain how enterprise information management fosters stronger 360-view of customers and suppliers and how the enterprise systems are widely used to enforce organizational structures and business processes.
4. Explain the usage and value of cloud computing, big data, IoT, artificial Intelligence (AI) and machine learning in business.
5. Recognize the importance of information ethics, governance, security and privacy in an organizational context.

Business Intelligence (BANA250)

Data has become an essential strategic asset for many organizations in achieving competitive advantage and success. This course is designed to deliver a comprehensive introduction to visual analytics and business intelligence concepts and provide students with the knowledge and technical skills to support data-driven decision-making. Topics covered include data preparation and quality, dashboard implementation, and spatial analysis. The course uses state-of-the-art visual analytics software Tableau to provide hands-on experience. Students will work in groups to learn how to apply analytical techniques to sift through data and provide actionable business insights.

Credit Hours : 3

Prerequisites

• BANA200 with a minimum grade D

Course Learning Outcomes

1. Define the physical elements of IT landscape, including hardware and software, and how they are arranged to support business operations of an enterprise.
2. Describe the process of visual analytics.
3. Distinguish between the various types of data visualizations and the types of information they produce.
4. Solve a data driven problem by analyzing business data through an integration of modeling and computational skills and communicating the results of the analysis to a professional audience.
5. Construct BI dashboards for displaying business KPI (Key Performance Indicators) and visual reports, while developing comprehensive solutions to data-driven problems.

Data Management and Organization (BANA310)

The objective of this course is to provide students with an introduction to the core concepts in data and information management. Enterprise data management systems are at the heart of modern business information systems. They facilitate information sharing across the organization and, therefore, support the notion that data is a corporate asset. Corporate data must be managed effectively to ensure the continued success of the organization. Data management, which focuses on data collection, storage, and retrieval, is a central activity for any organization. Topics covered include the principles of database design, modeling using the entity relationship model, the relational data model and relational database constraints, design techniques of relational database systems, and the Structured Query Language (SQL). In addition to developing database applications, the course helps the students understand how large-scale packaged systems such as business intelligence are highly dependent on the use of database systems.

Credit Hours : 3

Prerequisites

• BANA250 with a minimum grade D

Course Learning Outcomes

1. Outline the fundamental concepts of databases and the role of database management systems in managing organizational information.
2. Design conceptual models of relational databases based on requirement specifications, for solving data-driven problems.
3. Implement relational databases using the Structured Query Language (SQL), while developing comprehensive analytics solutions.
4. Apply database security and integrity, while minimizing issues related to code of ethics.
5. Communicate, orally and in writing, the design and implementation of relational databases in analytics projects.
6. Execute database development projects effectively as a team.

Business Analytics (BANA380)

Business analytics is the application of visual, statistical, and computational models and methods systematically, for developing new business insights and for improving performance. Business analytics projects and processes are empowered by data-analytic thinking and executed through data science: Data is collected, processed, modeled, and analyzed through descriptive, predictive, and prescriptive methods. Besides providing the definition, goals, and process of business analytics, this course presents a discussion of various technical topics of analytics. Topics covered include machine learning, predictive modeling, and model evaluation. Methodological foundations are supported by case study discussions and illustrated through experiential learning, where real-world datasets are analyzed with state-of-the-art analytical modeling & analysis software.

Credit Hours : 3

Prerequisites

• (STAT202 with a minimum grade D and BANA250 with a minimum grade D) or STAT210 with a minimum grade D
• STAT380 with a minimum grade D

Course Learning Outcomes

1. Explain the concepts and methods of business analytics.
2. Compare the various supervised and unsupervised machine learning methods, including classification, regression, clustering, and association mining.
3. Apply descriptive and predictive analytical models with real-world data sets, through data science workflows.
4. Solve issues that arise during modeling, while avoiding pitfalls of analytical models, such as overfitting.
5. Communicate, orally and in writing, the evaluation of analytical methods and models used in analytics projects.
6. Execute business analytics projects effectively as a team.

Business Analytics Applications (BANA400)

Analytics is vital for any organization, because every organization, regardless of its size or industry, has data that can be harnessed for benefit and sustainable success. In this course, a multitude of real-world cases and data are discussed. A multitude of business functions, such as marketing and finance, and industry domains are covered. The KNIME software suite is used as the analytical modeling environment.

Credit Hours : 3

Prerequisites

• BANA380 with a minimum grade D

Course Learning Outcomes

1. Explain how business analytics is applied across different industries and fields.
2. Apply descriptive and predictive analytical models with real-world data sets from various industries, through data science workflows.
3. Identify the pitfalls/limitations/ethical issues related to business analytics projects.
4. Deduce actionable insights for various business domains and industries through data analytics modeling using state-of-the-art software.
5. Communicate, orally and in writing, the application of analytical methods and models in business and industry
6. Execute business analytics projects effectively as a team using data from diverse functional areas of business.

Text Analytics (BANA410)

Text analytics is used to extract meaningful information and actionable insights from the text data, to improve decision making. For example, a company can assess positive and negative trends by monitoring how customers discuss products on social media and user-generated content websites. This course aims to be a primer for text analysis, at both conceptual and practical dimensions. After completing this course with success, students gain skills to independently collect, process, and analyze text data to uncover hidden patterns. Topics discussed in the course include: capturing textual data sets, stemming text documents, duplicate detection, cleaning data sets, document clustering, text classification, sentiment analysis, and topic modeling.

Credit Hours : 3

Prerequisites

• BANA380 with a minimum grade D

Course Learning Outcomes

1. Apply and integrate text processing methods through capturing, sorting, sifting, stemming and matching text data.
2. Construct visualizations of text data, through word clouds and other methods towards developing analytics solutions.
3. Develop analytical models that use relevant text analytics techniques to solve data-driven problems.
4. Communicate, orally and in writing, the application of text analytics in business and industry.
5. Execute text analytics projects effectively as a team.

Graph Analytics (BANA420)

What do social networks, road networks, electric networks, protein networks, food webs, and the Internet all have in common? They are all networks, i.e. graphs. A graph is a structure that represents relations between entities, where entities are shown as nodes (vertices) and their relations are shown with links (edges). Graph analytics is the application of statistical and computational techniques for the analysis of graph data, for obtaining insights into the relations between the entities and the full graph. The course introduces the various types of graphs and the metrics, methods, and software tools for analyzing them. Throughout the course, graphs from a multitude of domains will be introduced and analyzed through constructing graph visualizations and computing graph metrics with state-of-the-art software.

Credit Hours : 3

Prerequisites

• BANA380 with a minimum grade D

Course Learning Outcomes

1. Compare various types of graphs.
2. Select the most suitable graph drawing/layout methods for visualizing a graph, based on the objectives of the analysis.
3. Examine the characteristics of graphs through graph metrics for developing comprehensive analytics solutions.
4. Interpret graph visualizations for insights
5. Utilize graph analytics software for computing graph metrics and constructing effective visualizations.

Applied Optimization (BANA430)

Optimization problems are real-world problems we encounter in many areas such as manufacturing, transportation, financial planning, and scheduling. Optimization is an analytical technique for finding the best solution from within a set of solutions or solution space. A fundamental structure in optimization is to minimize an objective function under a set of constraints. This course introduces linear programming (LP), a modeling technique for optimization problems where the objective function and constraints are all linear. Practical modeling of LP problems with spreadsheet software is illustrated through a collection of illustrations and case studies. Other topics include what-If analysis for LP, binary integer programming, and mixed integer programming.

Credit Hours : 3

Prerequisites

• BANA250 with a minimum grade D

Course Learning Outcomes

1. Demonstrate knowledge of basic concepts of linear programming.
2. Formulate optimization models to solve problems in different production and service industries.
3. Utilize spreadsheet models in solving linear programming problems.
4. Develop and implement what-if analysis to gain valuable business insights, while evaluating the implications of various scenarios.
5. Communicate, orally and in writing, the application of optimization methods in business and industry.
6. Execute optimization projects effectively as a team.

Digital Business Innovation (BANA520)

This course introduces the students to digital business transformation and innovation. The dimensions of digital businesses, namely, customers, competition, data, innovation, and value, are introduced. The course describes the harnessing of customer networks, building of platforms, transforming of data into assets, and innovating by rapid experimentation to create business value. Other introduced topics include e-commerce, disruptive business models and self-assessment.

Credit Hours : 3

Course Learning Outcomes

1. Demonstrate knowledge or the dimensions of digital transformation.
2. Discuss the theoretical principles and the practical processes of digital platform development.
3. Evaluate competing disruptive business models in the digital economy.
4. Justify the application of analytics to transform data into valuable assets.
5. Plan for digital transformation, including the definition of deliverables and dimensions, as well as privacy, ethics, and governance aspects.

Visual Analytics & Business Intelligence (BANA540)

Data has become an essential strategic asset for many organizations to achieve competitive advantages. The course is designed to deliver a comprehensive introduction to visual analytics and business intelligence concepts and provide students with the knowledge and technical skills to support data-driven decision making. Upon successful completion of this course, students gain insights into managerial, strategic, and technical issues associated with developing and deploying BI solutions. The course advances the understanding of corporate performance management metrics and key performance indicators (KPIs). Topics covered include dimensional modeling principles, data extraction techniques from source systems, data profiling, data transformation methods, data preparation and quality, dashboard implementation, and spatial analysis. The course uses state-of-the-art BI software tool Tableau to provide hands-on experience. Students work individually and in groups to learn how to apply visual analytics to sift through massive amounts of data and discover actionable business insights.

Credit Hours : 3

Course Learning Outcomes

1. Discuss the basic concepts, technologies, and techniques of business intelligence, visual analytics, and ERP systems.
2. Analyze data through visual analytics for deriving actionable insights and supporting effective decisions.
3. Develop innovative visual analytics workflows to summarize and present data, and consequently interpret and explain results.
4. Design effective visual presentations of data as dashboards for supporting sustainable decisions.
5. Apply the techniques of business intelligence and visual analytics using state-of-the-art software.
6. Utilize project management techniques for planning, incubating and executing visual analytics projects.
7. Communicate the methods, processes, and analysis results of visual analytics, verbally and in writing, as an effective and collaborative team member.

Applied Optimization (BANA560)

Optimization is an analytical method for finding the best solution from within a set of solutions or a solution space. A fundamental structure in optimization is to minimize an objective under a set of constraints. This course introduces linear programming (LP), an optimization modeling technique for problems where the objective function and constraints are linear. Practical modeling of LP problems with spreadsheet software is illustrated through a collection of illustrations and case studies. Other topics include what-If analysis for LP, network optimization, binary integer programming, mixed integer programming, and non-linear programming.

Credit Hours : 3

Course Learning Outcomes

1. Identify whether and how business decision making problems can be formulated through optimization.
2. Formulate real-life decision problems as mathematical programming models, including linear, binary, mixed integer, nonlinear programming, and network optimization.
3. Implement appropriate solution for a given optimization problem using the most suitable state-of-the-art tools.
4. Interpret the results of optimization models to develop recommendations to managers.
5. Communicate models, methods, and analysis results of optimization projects, verbally and in writing, in individual and team work.
6. Defend the importance and application of privacy, ethics, and governance in optimization.

Business Analytics Applications (BANA600)

Analytics is vital for any organization, because every organization, regardless of its size or industry, has data that can be harnessed for benefit and sustainable success. In the course, real-world data and the related business cases are introduced, covering a comprehensive spectrum of business functions and industries. Functional application domains include accounting, finance, marketing, social networks, human resources, and operations. The fundamental concepts of each business function are introduced in parallel with the presented case studies. A visual modeling software suite is used as the data modeling environment, for modeling and analyses in the presented cases.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Contrast the Business Analytics landscape through example applications in diverse real-world problems.
2. Implement analytics techniques innovatively to real-world problems and data from diverse functional areas of business administration.
3. Apply analytics models, methods, and processes, using state-of-the-art analytics software.
4. Conduct applied analytics research projects by analyzing real-world data from diverse business problems.
5. Utilize project management techniques for planning, incubating and executing successful business analytics projects.
6. Communicate effectively and efficiently the models, methods, processes, and analysis results of data analytics, verbally and in writing.

Marketing Intelligence and Analytics (BANA645)

This course introduces the application of business intelligence to the marketing function for better decision-making. Students gain hands-on experience with business intelligence techniques and tools to analyze customer and market data, develop marketing strategies, and allocate resources. Topics covered include visual analytics, data visualizations, dashboards, lifetime customer value, market segmentation, and digital & social media marketing.

Credit Hours : 3

Prerequisites

• MKTG615

Course Learning Outcomes

1. Discuss the basic concepts, technologies, and techniques of marketing intelligence, visual analytics, and information systems.
2. Analyze marketing-related data through visual analytics for deriving actionable insights and supporting effective decisions.
3. Develop innovative visual analytics workflows to summarize and present marketing data, and consequently interpret and explain results.
4. Design effective visual presentations of marketing data as dashboards for supporting sustainable decisions.
5. Apply the techniques of marketing intelligence and analytics using state-of-the-art software.

Analytics for Accounting & Finance (BANA652)

This course is designed to cover the data analytics technologies used in the accounting and finance. Data analysis has become a crucial skill for all accounting and finance professionals. It is especially important for accountants to grasp a better understanding of internal and external data. In this course, students develop an understanding of the data available to finance and accounting managers, its use and limitations, and the measurement of financial performance. Students gain hands-on analytics experience with audit data, using statistical and predictive methods. Upon successful completion of this course, the students acquire the skills for cleaning and visualizing data from accounting and finance, and applying analytical methods for financial risk modeling, credit risk analysis, investment modeling, and auditing.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• BANA560 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Describe the data types, as well as analytics models and methods as applied in accounting and finance.
2. Implement analytics techniques innovatively to real-world problems and data from accounting and finance.
3. Apply analytics models, methods, and processes, in the domains of accounting and finance, using spreadsheets and state-of-the-art analytics software.
4. Conduct applied analytics research projects by analyzing real-world data from accounting and finance.
5. Utilize project management techniques for planning, incubating and executing analytics projects for accounting and finance.
6. Communicate effectively and efficiently the models, methods, processes, and analysis results of data analytics as applied to accounting and finance, verbally and in writing.

People Analytics (BANA655)

Human Resource Management (HRM) aims at providing sustainable growth and competitive market advantage through people. People drive organizational performance, and performance depends on measurement. HR professionals should be good at planning and interpreting the "person statistics" of an organization. This requires a solid understanding of HR analytics, such as systematic data collection, analysis, and interpretation, to improve decision making for people and organizations. This course introduces the principles and strategic concepts of HR analytics, which utilizes data-driven metrics and models to measure and improve decisions to attract, manage, and retain employees. Students learn quantitative decision-making techniques such as data-driven recruitment, employee engagement, turnover, reward mechanism, educational evaluation, and performance management. The skills learned in this learning process allow HR managers to make evidence-based decisions through data collection, analysis, and presentation. The use of HR analytics is and will continue to shape the way HR professionals quantify and develop an organization's most valuable asset, namely, the human talent.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Describe the data types, as well as analytics models and methods as applied in Human Resource Management (HRM).
2. Discuss data privacy, transparency, and security, in the context of analytics for Human Resource Management (HRM).
3. Implement analytics techniques innovatively to real-world problems and data from Human Resource Management (HRM).
4. Apply analytics models, methods, and processes, in the domain of Human Resource Management (HRM), using state-of-the-art analytics software.
5. Conduct applied analytics research projects by analyzing real-world data from Human Resource Management (HRM).
6. Utilize project management techniques for planning, incubating and executing analytics projects for Human Resources Management (HRM).
7. Communicate effectively and efficiently the models, methods, processes, and analysis results of data analytics as applied to Human Resource Management (HRM), verbally and in writing.

Analytics for Operations & Supply Chains (BANA656)

Operations are manufacturing and service processes are used to transform resources into products/services. A supply chain is the complete set of processes, as well as the physical system itself, that delivers products and services. This course introduces the concepts of operations and supply chains, along with the goal of sustainability and in consideration of risks. Topics covered include queueing systems, material requirements planning (MRP), and quality management. Operations and analytics along the supply chain are discussed; including forecasting, inventory management, sourcing, transportation, and warehousing. Analytics software are used for modeling and analysis throughout the course, for applying the analyses presented in the cases.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• BANA560 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Demonstrate knowledge of the basic and contemporary concepts and principles of operations and supply chain management, including manufacturing and logistics.
2. Construct analytics models for planning, organizing and controlling manufacturing and/or service operations along supply chains.
3. Make use of statistical and machine learning models and techniques for quantitative forecasting.
4. Apply analytics models, methods, and processes, in the domains of operations and supply chain management, using spreadsheets and state-of-the-art analytics software.
5. Utilize project management techniques for planning, incubating and executing analytics projects for operations and supply chains.
6. Communicate effectively and efficiently the concepts, terminology, principles, models, methods, and processes of operations and supply chain management, verbally and in writing.

Marketing Analytics (BANA661)

Business organizations are increasingly relying on data-driven marketing to better understand the customers' needs and wants. Many companies have readily harnessed extensive data on consumer’s purchasing behavior, social relationships, or attitudes. Through analyzing this data, companies can gain customer and market insights and strengthen their marketing decisions. Yet, few organizations have the expertise to intelligently manage and thrive upon such data and information. In this course, students learn systematic and analytical approaches to marketing decision-making. Students gain hands-on experience with marketing analytics techniques and tools to analyze customer and market data, develop marketing strategies, and allocate resources. Topics covered include market segmentation, market basket analysis, customer profitability, product recommendation systems, mobile geo-location analysis, and digital & social media marketing.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• BANA560 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Demonstrate knowledge of the basic and contemporary concepts and principles of marketing analytics.
2. Analyze sales transactions data using pivot tables.
3. Develop effective pricing through analytics techniques.
4. Apply analytics models, methods, and processes, for marketing, using spreadsheets and state-of-the-art analytics software.
5. Plan the tasks, resources, and schedule of marketing projects through effective team work
6. Utilize project management techniques for planning, incubating and executing marketing analytics projects.
7. Communicate effectively and efficiently the concepts, terminology, principles, models, methods, and processes of marketing analytics, verbally and in writing.

Analytics Project (BANA690)

This course challenges students with applying, for a real-world case study, the new knowledge they gained in their program courses. This single course requires the student(s) to conduct the complete life cycle for an analytics project with an independent research-oriented mindset, using readily available data. Students apply the proper research methods and the principles of project management, while gaining practical experience and extracting value from data with business analytics and its tools. Students who take this course/path cannot take BANA 691 & 692 Capstone Project I & II courses, and are required to take three electives, from the pool of restricted electives.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• BANA560 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Demonstrate advanced knowledge and practice of analytics models and methods.
2. Formulate data-driven real-world problems in business and industry innovatively as analytics projects.
3. Analyze real-world problems and data through innovative adaption of analytical models and methods, for actionable insights and sustainable decisions.
4. Apply project management techniques for planning and ethically executing research-oriented analytics projects, individually or in teams.
5. Communicate effectively and efficiently the concepts, terminology, principles, models, methods, and processes of business analytics, and analysis results of analytics projects, verbally and in writing.

Capstone Project I (BANA691)

This series of two capstone courses uses the case teaching approach to enable students to synthesize and deepen their knowledge of business analytics methods tools learned from the previous courses. These two courses require the student(s) to conduct the complete life cycle for an analytics project, using data obtained from a company or collected as a part of research. Students are expected to apply the most suitable research methods, adhere to professional conduct, and communicate the results in a clear and comprehensive way through technical reports and presentations. They apply the proper research methods and the principles of project management throughout the project. Students with an industry project are expected to complete both of these courses, not only the first one. Students who take this path of two courses cannot take the BANA 690 Analytics Project course and are required to take two electives, from the pool of restricted electives.

Credit Hours : 3

Prerequisites

• BANA520 with a minimum grade D
• BANA540 with a minimum grade D
• BANA560 with a minimum grade D
• STAT555 with a minimum grade D

Course Learning Outcomes

1. Analyze data-driven real-world problems in business and industry for developing analytics projects.
2. Apply the principles of data governance to acquire, prepare, organize, and process data for business analytics projects.
3. Select appropriate analytical models and methods for analytics projects at hand.
4. Plan the full life cycle of research-oriented analytics projects through the application of project management techniques.
5. Communicate effectively and efficiently the concepts, terminology, principles, models, and methods of business analytics, verbally and in writing.

Capstone Project II (BANA692)

This series of two capstone courses uses the case teaching approach to enable students to synthesize and deepen their knowledge of business analytics methods tools learned from the previous courses. These two courses require the student(s) to conduct the complete life cycle for an analytics project, using data obtained from a company or collected as a part of research. Students are expected to apply the most suitable research methods, adhere to professional conduct, and communicate the results in a clear and comprehensive way through technical reports and presentations. They apply the proper research methods and the principles of project management throughout the project. Students with an industry project are expected to complete both of these courses, not only the first one. Students who take this path of two courses cannot take the BANA 690 Analytics Project course and are required to take two electives, from the pool of restricted electives.

Credit Hours : 3

Prerequisites

• BANA691 with a minimum grade D

Course Learning Outcomes

1. Demonstrate advanced knowledge and practice of analytics models and methods.
2. Analyze real-world problems and data through innovative adaption of analytical models and methods, for actionable insights and sustainable decisions.
3. Apply analytics models, methods, and processes, using state-of-the-art analytics software.
4. Utilize project management techniques for ethically executing the full life cycle of research-oriented analytics projects, individually or in teams.
5. Communicate effectively and efficiently the models, methods, and processes of business analytics, and analysis results of analytics projects, verbally and in writing.

Foundation of MIS & Technologies (MIST200)

Management information systems and technologies (MIST) are an integral part of all business activities and careers. This course is designed to introduce students to contemporary information systems and demonstrate how these systems are used throughout organizations. The focus of this course is on the key components of management information systems and technologies - people, processes, software, hardware, data, and communication technologies, and how these components can be integrated and managed to create competitive advantage. Through the knowledge of how MIST provides a competitive advantage, students gain an understanding of how information is used in businesses and how business information technologies enable improvement in quality, speed, and agility. This course also provides an introduction to business information systems and development concepts, business information technology acquisition, and various types of application software that have become prevalent or are emerging in modern organizations and society.

Credit Hours : 3

Course Learning Outcomes

1. Assess The Ethical And Security Concerns That Information Systems Raise In Society.
2. Discuss Globalization And The Role Information Systems Has Played In This Evolution To Provide Businesses With Competitive Advantage.
3. Explain How Enterprise Systems Foster Stronger Relationships With Customers And Suppliers And How These Systems Are Widely Used To Enforce Organizational Structures And Business Processes.
4. Explain The Technology, People, And Organizational Dimensions Of Information Systems And How Organizations Develop And Acquire Information Systems And Technologies.
5. Illustrate How Information Systems Are Enabling New Forms Of Commerce Between Individuals, Organizations, And Governments.
6. State How Various Types Of Information Systems Provide The Information Needed To Gain Business Intelligence To Support The Decision Making For The Different Levels And Functions Of The Organization.

Computer Application in Business (MIST215)

Information Technology (IT) and information systems (IS) are becoming core elements of any business. This course is directly concerned with the role of computers in business systems and different business functions. It takes a structured view of managerial decision making. Everyday examples of finance, marketing, supply chain management and logistics, and human resource management and development are studied using hands-on and learn-by-example model development. The emphasis of this course is the practical implementation of real world model rather than traditional theoretical approach. This course helps students to put theoretical concepts into practical applications. It focuses on the ingredients of student knowledge necessary for success in business administration and to cope with the challenges inherent in the implementation of rapidly advanced information technologies and systems. The course’s active learning approach encourages the student to focus on developing skills in “how” to build a model while summarizing the mathematical logic as to “why” the model is constructed. Microsoft Excel and Access are the main tools used in this course.

Credit Hours : 3

Prerequisites

• MIST200 with a minimum grade D

Course Learning Outcomes

1. Develop Skills In How To Build A Model And Why It Is Constructed.
2. Discuss The Role Of Computers In Business Systems And Different Business Functions
3. Employ Conventional Business Applications Software Used In The Practical World.
4. Identify A Structured View Of Managerial Decision Making.
5. Identify Knowledge Necessary For Success In Business Administration And To Cope With The Challenges Inherent In The Implementation Of Rapidly Advanced It Domain.
6. Illustrate How To Put Theoretical Concepts Into Practical Applications.

E-Business Strategy, Architecture & Design (MIST280)

e-Business has changed the way emerging and current businesses operate and compete. This course focuses on the fundamentals of e-business, its architecture, business models, challenges, and promises. It illustrates how business process re-engineering (BPR) can achieve effective e-Business strategies. This course emphasizes the innovative nature of e-business models, which includes B2B, B2C, B2E, B2G and others. It provides an overview of e-Commerce from a managerial perspective. The course introduces students to e-marketplaces, e-procurement, e-business infrastructure, online payment systems, e-Business strategic issues, and the role of ethical and social issues.

Credit Hours : 3

Prerequisites

• MIST200 with a minimum grade D

Course Learning Outcomes

1. Describe The Nature Of E-Business And Related Concepts
2. Discuss The It Infrastructure Used In E-Business
3. Explain The Different Security And Ethical Issues Related To E-Business
4. Identify The Different Managerial Issues Of Conducting E-Business
5. Identify The Various E-Business Models

Information Systems in Business (MIST610)

The ultimate goal of this course is to provide students a holistic and in-depth understanding of information systems (IS) role in supporting, shaping, and enabling business strategies and achieving corporate objectives. Information systems are one of the major tools available to business managers for achieving operational excellence, developing new products and services, improving decision making, and achieving competitive advantage. A fundamental question that is answered by this course is “how information systems and technologies are efficiently and effectively utilized in managing the information as a business resource?”. The divide that currently exist between IT and business can be bridged by increasing the IS and IT-knowledge of decision makers. This course provides an understanding of the different types of information systems in business organizations, the role of IT in business decision-making, E-business, IT infrastructure and emerging technologies, business intelligence, MIS ethical and social issues, and enterprise information systems.

Credit Hours : 3

Course Learning Outcomes

1. Assess The Common Pitfalls That Damage Many Is Projects And Make Well-Grounded Recommendations On Actual Or Planned Is Applications
2. Describe Ethical Practices And Legislation Concerning Information Use, Protection And Privacy
3. Discuss The Various Types Of Information Systems And Their Applications In Business Organizations.
4. Evaluate Current Is Provision In An Organization In The Light Of Emerging Technical Possibilities.
5. Explain How Is Can Add Value To A Business Process And Enable Radical Re-Engineering Of Existing Processes.
6. Outline How Is Could Support An Organization’S Strategy, Including Developing New Products, Services And Markets.

Information Technology Strategy & Management (MIST614)

Information technology and digital platforms are critical enablers of organizational competence and resilience. This course focuses on identifying and designing effective strategic IT. Special emphasis is placed on information as a critical resource and on its role in strategic planning. The course takes a broad perspective by planning by examining the internal, external, and strategic planning involved in information technology and digital platforms. Course content will draw concepts from Enterprise architecture, platform architecture, digital business models, two-sided pricing, monetization design, network effects, information asymmetry, open innovation, and game theory. Upon completion of the course, students understand how IT enables growth, and how digital platforms start, grow, and compete in modern digital economies.

Credit Hours : 3

Prerequisites

• MGMT500 or MGMT201

Course Learning Outcomes

1. Analyze evidence of the impact of IT and digital platforms on both global and UAE markets.
2. Apply concepts from Information systems, economics, strategic management, and ethics that inform understanding of how IT and digital platforms grow and decline.
3. Design effective IT outsourcing plans to optimize scarce resources.
4. Formulate digital strategies to grow, maintain, manage, and capture value from IT and platforms

Management of Technology (MIST625)

The focus of this course is management of technology and innovation (MoT+I) which is a powerful tool organizations use to compete in an increasingly challenging global economy. Technology Management is at the intersection of science, engineering, management and behavioral science. Participants will: (1) Understand the dynamics of technological innovation, (2) be familiar with how to formulate technology strategies, (3) know how to implement technology strategies, and (4) understand how to manage ideas in a technological based organization.

Credit Hours : 3

Course Learning Outcomes

1. Analyze The Organization’S Core Capabilities As Competitive Weapons
2. Apply The Strategic Management Theories To Utilize Innovative Technologies
3. Demonstrate The Concept Of Innovation And Management Of Technology
4. Discover The Importance Of Adopting Disruptive Technologies
5. Formulate The Diffusion And Management Of Technologies
6. Illustrate The Change Management Requirements Orally And In Written Formats

Strategic IS Management (MIST630)

This course is about information systems strategy and management from a top management perspective. Information technology (IT) is an integral part of most products and services of the post-industrial society of the 21st century and has changed the top management job. Topics include business models and organization forms in the information age, IT as a business enabler, IT and competitive strategy, information for management control, analysis and redesign of business structure and processes, knowledge management and information networks, interorganizational networks, sourcing strategies, interfacing with the IT function, reliability and security, and ethical and policy issues. The course relies extensively on the case method and the students will supplement their analyses with current information obtained from the Web, or directly from the firms under study in the cases.

Credit Hours : 3

Course Learning Outcomes

1. Critically Assess The Value Adding Characteristics Of Using The Internet For E-Business.
2. Describe The Concept Of Business Innovation Through The Adoption Of Modern Information Technologies And Information Systems.
3. Develop A Practical Approach To Supporting Organizations To Achieve Symmetry And Alignment In Their Business And Information Systems Strategies.
4. Develop An Understanding Of Strategies For Competitive Advantage Using Information Systems.
5. Develop An Understanding Of The Key Developments And Issues In The Strategic Management Of Information Systems.
6. Discuss The Differences And Similarities Faced By Organizations Regarding The Strategic Use Of Information Systems.

Business Intelligence & BPM (MIST640)

This course develops an understanding about the essentials of Business Intelligence, Data Warehousing, Business Analytics, Data Visualization, Data, Text and Web Mining. Focus will be on use of above technologies in decision support systems and business performance management. The course also covers decision support systems concepts, methodologies, and technologies. Through lectures, case studies and class discussions this course aims to develop participants? ability to identify key performance indicators (KPIs) that are affecting business performance and subsequently monitor the same using decision support and business intelligence systems using online analytical process (OLAP) and other performance management (BPM) techniques.

Credit Hours : 3

Course Learning Outcomes

1. Be Member Of A Team Which Designs Organizational Structures, Processes, And Technology Infrastructure For A Business Intelligence/Performance Management Solution By Assessing Business, Social, And Ethical Aspects Of Solution Alternatives.
2. Identify Specific Opportunities To Improve Business Performance With Business Intelligence And Performance Management Technologies And Practices
3. Make A Business Case For A Business Intelligence/Performance Management And Present It In Order To Persuade Decision Makers In An Organization.
4. Take Into Account Information Privacy Issues And Ethical Use Of Personal Information In Business Intelligence.
5. Take Part In Implementation Of A Business Intelligence/Performance Management Solution With An Understanding Of Practical Problems, Available Tools, And Project Structure.
6. Understand Of The Strategic Importance Of Data By Developing A Fact-Based Decision-Making Approach Built With Hands-On Experience.

E-Business: Technology, Strategies & Applications (MIST650)

eBusiness (abbreviation of Electronic Business), online business, or digital business is the use of electronic means to conduct business internally and/or externally . Internal e-business activities may include production, development, maintenance of IT infrastructure, and product management. External eBusiness activities also include supporting after-sales service activities and collaborating with business partners. From this definition it is apparent that eBusiness is a broad scope of subjects. Accordingly, this course takes an integrative approach drawing on new and existing approaches and models from many disciplines, including information systems, strategy, marketing, supply chain management, operations and human resources management. The course is intended to equip current and future managers with some of the knowledge and practical skills to help them navigate their organization towards eBusiness. A key aim of this course is to identify and review the key management decisions required by organizations moving to or managing eBusiness and consider the process by which these decisions can be taken. The overall structure of the course follows a logical sequence: introducing the foundations of eBusiness concepts; reviewing alternative strategic approaches and applications of eBusiness how strategy can be implemented using digital transformation.

Credit Hours : 3

Course Learning Outcomes

1. Discuss the fundamental concepts, strategies, technologies, challenges, and evolvement of eBusiness and its digital transformation.
2. Classify the different eBusiness models and strategies.
3. Outline the range of digital technologies used to build an eBusiness infrastructure within an organization and its partners.
4. Apply tools to generate and select eBusiness strategies.
5. Identify the main elements of electronic supply chain management (eSCM), eMarketing, and eProcurement.

Enterprise IS (MIST660)

Traditionally information systems have been introduced into organizations as functionally specialized applications serving the specific needs of individual departments. Enterprise Information Systems, more commonly referred to as ERP systems, provide a more holistic view of the organization, helping eliminate narrower departmental perspectives. Introducing ERP applications has the potential of adding enormously to organizational value, if undertaken properly. This course discusses how these applications can best be applied to realize those organizational benefits and will discuss the associated topics of supply chain management (SCM), human resource management (HRM), customer relationship management (CRM) and knowledge management (KM).

Credit Hours : 3

Course Learning Outcomes

1. Describe The Impact Of Disruptive Technologies Such As Cloud Computing And Service Oriented Architecture (Soa) On Implementing Enterprise Systems In Businesses.
2. Evaluate The Experiences Of Real Businesses With Enterprise Systems Implementations.
3. Explain How “Best Practices” Are Incorporated In Enterprise Systems.
4. Explain How Integrated Information Sharing Increases Organizational Efficiencies.
5. Understand Current Trends Related To Enterprise Systems.
6. Understand The Fundamentals Of Enterprise Systems And Issues Associated With Their Implementation.

Statistics in the Modern World (STAT101)

The course helps students explore and learn about popular real-world topics using statistics as a tool. It discusses statistical application in population growth, economic developments, income distribution and environmental changes. Key statistical tools will be introduced through their applications in real world issues.

Credit Hours : 3

Course Learning Outcomes

1. Analyze Distributional Properties As They Apply To A Real Life Issue.
2. Apply Statistical Techniques To Compute Common Statistical Measures Used In The Modern World.
3. Define And Formulate Real-World Problems In Scientific/Probabilistic Terms Using Statistical Techniques.
4. Identify Statistical Indicators Used To Describe World Social And Economic Developments.
5. Interpret Statistical Results And Draw Conclusions Concerning The Use Of Statistics In Solving Real-World Problems.

Business Statistics I (STAT102)

This course introduces students to decision making based on data in a business context. It covers basic concepts, sources and methods of data collection, tabular and graphic presentation of data, descriptive statistics (measures of location, dispersion, skewness and kurtosis), measures of association between variables, introduction to probability, random variables and probability distributions, sampling distributions, and statistical estimation including interval estimation.

Credit Hours : 3

Course Learning Outcomes

1. Describe the importance of statistics and data-driven business decisions.
2. Apply the appropriate methods to summarize business data numerically and graphically using statistical software.
3. Choose the appropriate probability distributions to model uncertainty in business decision making.
4. Apply basic sampling methods of statistics.
5. Utilize the appropriate point and interval estimation methods for inferring about unknown parameters and drawing and communicating the relevant conclusions.
6. Execute statistical analysis and projects effectively in teams.
7. Identify ethical issues associated with data collection and analysis.

Statistics for Business (STAT130)

This course introduces students to the fundamental concepts of statistics and trains them to apply the basic methods and techniques of statistical analysis in business and economics problems. It covers basic concepts, sources and methods of data collection, tabular and graphical presentation of data, descriptive statistics, introduction to probability and probability distributions, sampling distributions, statistical estimation, hypotheses testing, analysis of variance, chi-square test of independence, and correlation and regression analysis.

Credit Hours : 3

Prerequisites

• MATH115 with a minimum grade D or MATH105

Course Learning Outcomes

1. Apply Descriptive Statistics Techniques To Describe Business Data Using Computer Package.
2. Apply Statistical Methods And Models In Business Situations Using Computer Package.
3. Communicate Statistical Information In Oral And Written Form.
4. Define And Apply The Basic Concepts Of Probability Theory And Statistics To Business Problems.
5. Define Random Variables And Their Distributions.
6. Identify Statistical Methods Used In Estimation, Testing And Regression Models.

Psychological Statistics I (STAT180)

This course introduces the basic concepts and elementary applications of statistics that are widely utilized by psychologists. It covers data description, central tendency measures, variability indicators, and degrees of peakedness and asymmetry of data distributions. In addition, the normal distribution, standard scores, correlation and their applications in psychology and as well as hypothesis testing will be studied in this course. Statistical packages will be used throughout the course to work out psychological applications.

Credit Hours : 3

Course Learning Outcomes

1. Describe the functional statistical concepts in Psychology.
2. Apply descriptive statistical techniques to summarize and describe data arising in psychology using a computer package.
3. Perform point and interval estimation of the population mean and proportion.
4. Describe the elements of statistical test.
5. Analyze and interpret relations between qualitative or quantitative variables using correlation and contingency tables.

Business Statistics II (STAT202)

This course builds on the knowledge acquired in the Business Statistics I course. It introduces students to the basic methods and techniques of statistical inference and their applications in business and economics. Topics include inference involving one and two populations, analysis of variance, Chi-square tests, nonparametric tests, regression analysis, and time series analysis.

Credit Hours : 3

Prerequisites

• STAT102 with a minimum grade D

Course Learning Outcomes

1. Apply the basic parametric and nonparametric methods to analyze data collected in business and economics environments and interpret the results.
2. Assess the relationship between variables for business decision-making, using the concepts of correlation and linear regression.
3. Develop answers to data-driven questions based on statistical analysis, by drawing and communicating relevant conclusions from the analysis.
4. Identify the uses and limitations of statistical analysis.
5. Discuss ethical issues associated with protecting privacy, sharing data ethically, and minimizing both collective and individual harm associated with data collection and analysis.
6. Execute statistical analysis and projects effectively in teams.

Probability and Statistics (STAT210)

This course introduces students to events and sample space, probability, conditional probability, random variables, cumulative distribution function and probability density function, moments of random variables, common distribution functions, elementary introduction to statistics with emphasis on applications and model formulation, descriptive statistics, sampling and sampling distributions, inference, t tests, one and two factors analysis of variance, randomized complete block design, correlation and regression, and chi-square tests.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D or MATH130 with a minimum grade D

Course Learning Outcomes

1. Define and apply the basic concepts of probability theory and statistics to real situations.
2. Define and compute the common probability distributions used in modeling data arising in engineering and IT.
3. Apply descriptive statistical techniques to describe data using statistical software.
4. Select and apply the appropriate statistical methods in analyzing data using statistical software.
5. Plan, analyze, and interpret the results of experiments.
6. Communicate statistical information in oral and written form.

Social Data Analysis I (STAT216)

This course will introduce students to various statistical techniques commonly used in sociological data analysis. Topics include sources and methods of data collection, tabular and graphical presentation of data, measures of central tendency and dispersion, Probability and random variables, statistical inference, and an introduction to categorical data analysis. A hands-on computer using statistical software and analysis of data from various fields of social research will be applied to all statistical concepts.

Credit Hours : 3

Prerequisites

• SOC290 with a minimum grade D

Course Learning Outcomes

1. Define the steps and components of statistical analysis with application to social challenges
2. Identify and manage different types of sociological data with SPSS
3. Apply appropriate statistical methods for describing and presenting data collected in social environments
4. Design and conduct appropriate statistical testing and correlation analysis for problems in social sciences using SPSS.
5. Report the results of quantitative analysis in written and oral forms

Principles of Probability (STAT230)

This course is an introduction to the principles and laws of probability. It gives the student a thorough understanding of the concepts of probability, conditional probability, random variables and probability distributions, moment generating functions, bivariate and marginal distribution functions, conditional distributions and expectations. While the primary focus of the course is on a mathematical development of the subject, it also includes a variety of illustrative examples and exercises that are oriented towards applications in social and physical sciences, and business.

Credit Hours : 3

Prerequisites

• MATH110 with a minimum grade D
• STAT202 with a minimum grade D

Course Learning Outcomes

1. Describe the concepts of probability and its basic properties and laws.
2. Apply a variety of counting techniques to compute probabilities.
3. Define conditional probability and compute posterior probabilities.
4. Model real-world data using statistical distributions and develop solutions through analytical techniques and computation.
5. Generate pseudo-random variables and simulate probability distributions to compute probabilities, percentiles and moments.

Statistics for Biology (STAT235)

This is an introductory course for students in biological sciences who have no formal background in statistics. It covers the basic statistical methods for describing and analyzing data arising in the biological sciences. The emphasis will be on the intuitive understanding of concepts rather than the underlying mathematical developments. Applications and data analysis will be based on the statistic package Minitab.

Credit Hours : 3

Prerequisites

• MATH105 or MATH115

Course Learning Outcomes

1. Apply Appropriate Statistical Methods For Describing And Presenting Data Arising In Biological Sciences.
2. Appropriately Choose Probability Models For Use In Problems Arising In Biological Sciences.
3. Define The Fundamental Statistical Concepts In The Design And Analysis Of Biological Studies.
4. Identify And Conduct Appropriate Methods Of Hypothesis Testing.
5. Identify And Utilize Appropriate Methods Of Estimation For Application To Data.
6. Identify The Types Of Data Arising In Biology And Health Sciences.
7. Plan, Analyze And Interpret The Results Of Statistical Analyses.

Data Exploration and Analysis (STAT240)

This course provides an introduction to exploratory data analysis with R statistical software. The data analytics process will begin with acquiring data from data sources, cleaning the data, and preparing it, through preprocessing, for statistical & computational analysis. The course will lay the foundation of fluency in handling, processing, and transforming data to a structure that enables its analysis. Topics covered in the course include the above steps of data preparation, as well as managing data frames, working with text data, exploratory graphs, visualizing clusters and distributional shapes, and methods such as sampling distributions and CLT.

Credit Hours : 3

Prerequisites

• STAT202 with a minimum grade D

Course Learning Outcomes

1. Capture and manage data from different sources (manual entry, internet, database) and different formats (csv, txt, html, etc.).
2. Perform data checking and cleaning using reproducible R code and techniques and best accepted practice.
3. Effectively communicate information from data using visualization techniques.
4. Use various exploratory techniques to find trends and structures in the data using reproducible R code and best accepted practice.
5. Estimate and validate linear models.

Statistical Graphics (STAT250)

This course introduces students to statistical graphics. It covers principles of graphical design, perceptual psychology, dimensionality reduction, statistical smoothing, trellis/lattice graphs, mosaic plots, 3D and dynamic graphics. Students will be trained to use appropriate statistical software libraries for graphics, reporting, and user interface.

Credit Hours : 3

Prerequisites

• STAT240 with a minimum grade D or STAT210 with a minimum grade D

Course Learning Outcomes

1. Explain the concepts and elements of statistical graphics and data representation.
2. Critically appraise statistical graphics to suggest appropriate ways of improving them.
3. Use statistical graphics to visualize multivariate data.
4. Apply statistical graphics to explore and analyze data, and to check assumptions of statistical models.
5. Interpret and communicate statistical findings through appropriate graphical techniques.

Psychological Statistics II (STAT280)

This course introduces the basic concepts of statistical inference and their applications in psychology. It covers sampling distributions, point and interval estimation, statistical hypothesis testing, correlation, regression and prediction, analysis of variance and factorial ANOVA. Statistical packages will be used throughout the course to work out psychological applications.

Credit Hours : 3

Prerequisites

• STAT180

Introduction to Statistical Inference (STAT300)

The course starts by reviewing the basics of probability and counting, random variables and distributions, bivariate random variables. The course then covers the basic theories underlying statistical analysis techniques in point estimation, interval estimation, and hypothesis testing. Point estimation methods include methods of moments and maximum likelihood. It also elaborates the concepts of bias, variance, and mean-squared error of estimators. Confidence interval construction methods include likelihood-based intervals, inversion methods, intervals based on pivots, Bayesian credible and highest posterior density regions, and resampling based intervals. Various Markov Chain Monte Carlo (MCMC) computational techniques will be introduced. Hypothesis testing methods include classical and Bayesian approaches.

Credit Hours : 3

Prerequisites

• STAT230 with a minimum grade D
• STAT240 with a minimum grade D

Course Learning Outcomes

1. Describe the basic concepts of probability, estimation theory, and hypothesis testing.
2. Explain the underlying assumptions and the applicability of various statistical inference methods.
3. Choose the appropriate methods for practical inference problems.
4. Apply appropriate methods of statistical inference to real data.
5. Interpret and effectively communicate the results of statistical analysis.

Social Data Analysis II (STAT316)

The course is a continuation of Social Data Analysis I and aims at enriching students’ knowledge and skills in statistical data analysis by introducing more advanced statistical methods for social sciences. This lab-based course will be forming statistical analysis skills which vital for sociologists and social data analysts. The course introduces such statistical methods as logistic regression, multinomial regression, factor analysis and cluster analysis. The students will be further mastering a statistical software(SPSS) usage by having hands-on practical computer-lab sessions.

Credit Hours : 3

Prerequisites

• STAT216 with a minimum grade D

Course Learning Outcomes

1. Apply the correct statistical data analysis method appropriate to the research goal and nature of the dependent variables
2. Analyze social data with the help of multiple regression using variable selection methods.
3. Analyze social data with the help of Logistic and Multinomial regressions
4. Analyze social data with the help of Factor analysis
5. Analyze social data with the help of Cluster analysis
6. Report the results of regression, factor and cluster analysis in written and oral forms

Survey Methods (STAT330)

This course prepares students to plan and implement surveys, and to analyze survey data. Topics include survey planning and formatting, guidelines to develop questionnaires, data collection methods, various sampling methods (simple random, cluster, systematics, and multiple stages), and methods to maximize response rates and minimize survey errors. The course also covers survey weights for unequal probability sampling, non-response and post-stratification, and standard error estimation for complex samples. Appropriate practices for protecting data privacy and sensitivity in survey research will also be discussed.

Credit Hours : 3

Prerequisites

• STAT230 with a minimum grade D
• STAT240 with a minimum grade D

Course Learning Outcomes

1. Define the objectives and basic concepts of survey design.
2. Develop survey questionnaires and select appropriate data collection techniques.
3. Select and apply appropriate sampling and analytical techniques for survey data analysis.
4. Construct and use the survey weights in survey data analysis.
5. Interpret and effectively communicate the findings of survey research to various types of audiences.
6. Discuss ethical concerns associated with survey research, such as data privacy, sensitivity, and sharing.

Applied Regression (STAT360)

This course introduces students to regression analysis, ridge and robust regression, non-parametric regression and Lasso, and General Linear Models (GLIMs). The emphasis of the course is on practical data analysis and interpretation. Real-world examples and data are analyzed throughout the course using the statistical software R.

Credit Hours : 3

Prerequisites

• (MATH140 with a minimum grade D and STAT230 with a minimum grade D and STAT240 with a minimum grade D) or (STAT210 with a minimum grade D and CSBP123 with a minimum grade D)

Course Learning Outcomes

1. Demonstrate in depth knowledge of concepts and methods of regression analysis and general linear models.
2. Select an appropriate general linear model that fits a data set.
3. Apply model diagnostic and remedial methods (outliers, multi-collinearity, heteroschedasticity).
4. Communicate effectively regression analysis results to various audiences.
5. Use the appropriate method to analyze high dimensional data.
6. Fit and interpret ANOVA, Randomized Block Design, and Latin Squares models.

Mathematical Statistics (STAT370)

This course provides a foundation in statistical theory. It covers methods of estimation and properties of estimators with a focus on likelihood-based approaches, interval estimation, tests of hypotheses with a focus on likelihood ratio tests, and theory of linear models. The course illustrates the theoretical concepts and methods through the derivation of some common confidence intervals and tests for means, variances, and proportions.

Credit Hours : 3

Prerequisites

• STAT300 with a minimum grade D

Course Learning Outcomes

1. Derive point estimators of parameters of statistical models using various methods such as maximum likelihood, method of moments, and least squares.
2. Compare estimators based on their properties (sufficiency, unbiasedness, efficiency, consistency).
3. Explain the statistical theory underlying the common statistical tests and confidence intervals, through oral or written communication.
4. Construct confidence intervals for various estimation problems.
5. Derive tests for simple and composite hypotheses using the likelihood ratio test principle.
6. Compute and compare the power of tests.
7. Develop inferences regarding the parameters of a linear model.

Statistical Machine Learning (STAT380)

This course introduces students to the principles and techniques of data mining and statistical machine learning, including artificial neural networks. It covers various statistical machine learning techniques, such as data exploration and visualization, supervised and unsupervised machine learning techniques, e.g., classification, regression, cluster analysis, principal component analysis, and ensemble methods for machine learning e.g., boosting and random forests. The course also includes the cross-validation techniques. The emphasis is on the practical implementations and the discovery of patterns and insights from data.

Credit Hours : 3

Prerequisites

• STAT360 with a minimum grade D

Course Learning Outcomes

1. Explain the concepts and application principles of statistical machine learning/data analytics methods.
2. Apply computational skills and appropriate machine learning/data analytics tools for developing solutions to data-driven problems.
3. Assess the performance of statistical machine learning/data analytics models using state-of-the-art analytics software, for developing comprehensive analytics solutions.
4. Effectively communicate the results of machine learning analyses to various audiences.

Applied Multivariate Analysis (STAT400)

This course introduces students to the methodology and applications of multivariate statistical analysis. It covers multivariate analysis of variance and regression, canonical correlations, principal components, factor analysis, discrimination, classification, and cluster analysis. The emphasis is on practical implementations and applications to the various disciplines and sciences.

Credit Hours : 3

Prerequisites

• STAT360 with a minimum grade D

Course Learning Outcomes

1. Define the properties of the multivariate normal distribution.
2. Compare common multivariate statistical methods, such as principal components, factor analysis, discriminant analysis, cluster analysis, and multivariate analysis of variance.
3. Select an appropriate method and implement it using statistical software to analyze multivariate data.
4. Effectively work in teams to execute projects involving multivariate data.
5. Communicate effectively through different formats the results of multivariate data analysis to various audiences.

Design of Experiments (STAT410)

This course focuses on design of experiments, optimum selection of input for experiments, and the analysis of results. Topics include design and planning experiments, ANOVA, full factorial design, complete randomized designs, blocking principles and RCBD, incomplete block design, confounding in general factorial design, fractional factorial designs, and response surface designs.

Credit Hours : 3

Prerequisites

• STAT360 with a minimum grade D

Course Learning Outcomes

1. Describe (verbally/written) the basic principles of experimental design: randomization, replication, and blocking.
2. Apply a systematic process for designing and conducting an experiment for a given application and objective of experimentation.
3. Implement various statistical analyses involved in a designed experiment in a software package of choice.
4. Describe the general ANOVA process, encompassing the data model, sum of squares decomposition, mean square computation, hypothesis testing, sampling distributions, and verification of model adequacy.

Applied Time Series (STAT420)

This course trains students in selecting and constructing appropriate time series models, estimating their parameters and forecasting with the constructed models. Topics include time series regression, classical decomposition, exponential smoothing, autocorrelation and partial autocorrelation functions, stationary and homogeneous time series; autoregressive, moving average, ARMA and ARIMA models, seasonal models, Box-Jenkins methodology and business applications.

Credit Hours : 3

Prerequisites

• STAT360

Course Learning Outcomes

1. Utilize statistical graphics and summary statistics to identify the underlying patterns of time series data.
2. Classify the techniques used to analyze data on one-dimensional time series.
3. Implement forecasting and estimation with ARMA models and apply diagnostic checks where relevant, using appropriate statistical software packages.
4. Evaluate and interpret the performance of the selected time series models using goodness-of-fit measures.
5. Effectively communicate, orally and in writing, the interpretation of the estimated parameters and forecasting results.

Sampling Techniques (STAT422)

The course develops an understanding of survey research methodologies and data collection methods from scientific and practical perspectives. It emphasizes training students on alternative sample designs used to produce statistical inferences to solve real-life problems. In addition to discussing survey methods and design, it covers: simple, stratified, systematic and cluster sampling, ratio and regression estimates, errors in sample surveys and case studies.

Credit Hours : 3

Prerequisites

• STAT130 with a minimum grade D
• STAT230

Course Learning Outcomes

1. Calculate Point And Interval Estimators Of Relevant Parameters Under Different Sampling Designs.
2. Calculate Sample Sizes For Prescribed Precision And Power.
3. Define The Different Sampling Procedures, Study Designs, And Sources Of Errors In Sample Survey.
4. Determine The Accuracy Of The Estimators Of Relevant Population Parameters.
5. Identify A Suitable Sampling Design Given Available Information And Resources.
6. Interpret Survey Findings Through Written Report And Oral Presentations.
7. Use Appropriate Statistical Software To Select Random Samples And Analyze Survey Data.
8. Use Different Data Collection Techniques.

Categorical Data Analysis (STAT430)

This course is an introduction to topics in categorical data analysis. It is an applied course emphasizing the modeling and analysis of categorical data using mainly the R statistical software. Both descriptive and inferential methods are discussed. The covered topics include measures of association, tests of goodness-of-fit, tests of independence, exact tests, logit and probit models, and discriminant analysis.

Credit Hours : 3

Prerequisites

• MATH140 with a minimum grade D
• STAT230 with a minimum grade D
• STAT240 with a minimum grade D

Course Learning Outcomes

1. Demonstrate an understanding of the various types of dependencies between categorical variables (marginal, conditional, mutual independence), and how to assess them.
2. Apply goodness-of-fit tests, generalized linear models, and loglinear models to categorical data using statistical software and packages.
3. Select and fit appropriate models for analyzing categorical data and inferring relevant results.
4. Effectively work individually and in teams to undertake statistical projects.
5. Demonstrate a thorough understanding of ethical issues arising in the analysis of categorical data.
6. Communicate effectively through different formats the results of categorical data analysis to various audiences.

Bayesian Statistics (STAT460)

The aim of this course is to introduce students to the Bayesian statistical modeling and inference and to the related computational strategies and algorithms. The course starts with the logic behind Bayesian data analysis, including the mathematical formalization of updating beliefs under uncertainty, followed by the treatment of simple models, such as those based on normal and binomial distributions. Concepts of conjugate and non informative priors are illustrated, for single and multi-parameters models. Basic treatment of hierarchical models and linear regression models are also covered. Bayesian computational methods such as the Gibbs sampler and Metropolis-Hastings algorithms are briefly presented, with an emphasis on their implementation and use on simple cases.

Credit Hours : 3

Prerequisites

• STAT300 with a minimum grade D

Course Learning Outcomes

1. Apply concepts underlying Bayesian inference.
2. Demonstrate understanding of the principles and techniques of Bayesian data analysis.
3. Effectively communicate the interpretation of the results of Bayesian data analysis orally and/or in writing.
4. Perform Bayesian analysis using state-of-the-art statistical software libraries and packages.
5. Work in teams to execute Bayesian data analysis projects.

Introduction to Statistical Computing (STAT470)

The course introduces students to common computational techniques needed in statistics. It covers topics such as data manipulation, generation of random variables, simulation, resampling, bootstrapping, and jackknifing. The course also covers probability density estimation and elementary Bayesian analysis using MCMC methods. Furthermore, parallel computing and cloud computing implementations are introduced with basic practical examples. These techniques are demonstrated using a statistical programming language.

Credit Hours : 3

Prerequisites

• STAT230 with a minimum grade D
• (STAT240 with a minimum grade D or STAT360 with a minimum grade D)

Course Learning Outcomes

1. Apply data transformation techniques and tools to organize data, as a step in developing comprehensive analytics solutions.
2. Utilize appropriate techniques to generate random variables.
3. Apply Monte Carlo simulation techniques to simulate complex stochastic models and make inferences, for solving analytics problems.
4. Use bootstrap and jackknife methods to estimate model parameters and their standard errors, for developing analytics solutions.
5. Apply various computational techniques for the estimation of the probability density functions.
6. Implement parallel statistical computing and cloud computing through the use of appropriate software libraries, to enable comprehensive solutions to analytics problems.
7. Use MCMC method for estimating posterior distributions in Bayesian inference.

Selected Topics in Statistics and Data Analytics (STAT475)

This course covers topics in statistics and data analytics that broaden the students understanding of statistical theory and methods, which are not covered in the other courses offered in the Bachelor of Science in Statistics and Data Analytics program.

Credit Hours : 3

Course Learning Outcomes

1. Formulate data-driven real life analytics problems in the selected topics.
2. Integrate knowledge of statistical methods and computational skills covered in the special topics to derive solutions for a given problem.
3. Interpret the results of data analytics methods and models in selected topics.
4. Communicate data analysis results effectively orally, visually and in writing.

Capstone in Statistics and Data Analytics (STAT480)

This capstone course uses the case teaching approach to enable students to synthesize and deepen their knowledge of statistical methods and theories and data analytics techniques learned in earlier courses. Students work individually and in groups to analyze a variety of data-centric cases drawn from the real-world. Covered topics include sampling and survey design, techniques for handling, cleaning, extracting, organizing, and processing real data, as well as data ethics and quality. Students apply their statistical modelling and computational skills to develop comprehensive solutions to data-driven problems. Through a multitude of case studies drawn from the real-world, students advance their skills, exposure, and experience in diverse applications of statistics and analytics.

Credit Hours : 3

Prerequisites

• GBUS300 with a minimum grade D
• STAT330 with a minimum grade D
• STAT380 with a minimum grade D
• STAT400 with a minimum grade D
• STAT460 with a minimum grade D
• STAT470 with a minimum grade D

Course Learning Outcomes

1. Demonstrate knowledge of concepts and methods of statistics and data analytics, as well as statistical computing techniques, as applied in real world case studies.
2. Combine statistical modelling and computational skills to develop comprehensive solutions to data-driven problems.
3. Effectively communicate orally, visually, and in writing the results and interpretations of statistical analyses to various audiences.
4. Execute projects in statistics and data analytics with autonomy and responsibility, while working individually and in teams.
5. Demonstrate awareness of ethical issues in statistics and data analytics.

Capstone in Analytics for Business (STAT482)

This capstone course uses the case teaching approach to enable students to synthesize and deepen their knowledge of statistical methods and theories and data analytics techniques learned in earlier courses and apply this knowledge for business analytics. Students work individually and in groups to analyze a variety of business analytics problems, including issues posed by big data drawn from real-world problems. Covered topics include techniques for handling, cleaning, extracting, organizing, and processing real world data from business and industry, as well as data ethics and quality. Students apply their statistical modelling and computational skills to develop comprehensive solutions to data-driven problems. Through a multitude of case studies drawn from the real-world, students advance their skills, exposure, and experience in diverse applications of analytics for business.

Credit Hours : 3

Prerequisites

• GBUS300 with a minimum grade D
• BANA400 with a minimum grade D
• STAT300 with a minimum grade D
• STAT400 with a minimum grade D

Course Learning Outcomes

1. Demonstrate knowledge of statistical and analytical concepts and methods as applied to real world case studies from business and industry.
2. Combine statistical modelling and computational skills to develop comprehensive solutions to business analytics problems.
3. Effectively communicate orally, visually, and in writing the results and interpretations of analytics applications in business to various audiences.
4. Execute projects in business analytics with autonomy and responsibility, while working individually and in teams.
5. Demonstrate awareness of ethical issues in business analytics.

Bridging Statistics (STAT500)

The bridging course in statistics aims to give students with no statistical background a good knowledge of descriptive statistics and probability and probability distributions. These topics, covered in most introductory statistics courses, are a pre-requisite knowledge for the course STAT 609 (Decision Techniques and Data Analysis).

Credit Hours : 1

Course Learning Outcomes

1. Apply descriptive statistical techniques to describe data using statistical software.
2. Define and apply the basic concepts of probability theory and statistics to real situations.
3. Define and compute the common probability distributions used in modeling data.

Statistics Bridging (online-MBAN) (STAT501)

This online bridging course in statistics aims to provide, for students without a statistical background, a good preliminary knowledge of descriptive statistics, probability, and probability distributions. These topics, covered in most introductory statistics courses, are pre-requisite knowledge for introductory statistics courses at graduate studies.

Credit Hours : 0

Course Learning Outcomes

1. Analyze data through descriptive statistical techniques using relevant statistical software.
2. Apply the basic concepts of probability theory and statistics to real situations.
3. Compute the common probability distributions used in modeling data.

Applied Statistics (STAT503)

This course is dedicated to graduate students from College of Science. It introduces the students to the basic statistical procedures commonly used in the analysis of scientific and environmental problems. These statistical applications complement and reinforce scientific and environmental concepts and methods, particularly in practical, development and assessment models, and interpretation of data and results. It includes numerical and graphical description of data, techniques for significance evaluation and relationships

Credit Hours : 2

Course Learning Outcomes

1. Describe the common data collection and analysis methods.
2. Analyze data arising in science and environmental sciences and interpret the results of the analysis.
3. Select the appropriate statistical method/model and evaluate the model assumptions.
4. Use statistical software to explore and analyze data.
5. Write report on study findings.

Statistics Bridging (Online- MBA) (STAT511)

The bridging course in statistics aims to give students a good knowledge of business research methods, descriptive statistics, probability, and probability distribution. These topics are needed to help student succeed the different courses of the MBA program.

Credit Hours : 0

Course Learning Outcomes

1. Differentiate between the different types of business research.
2. Apply descriptive statistical techniques to describe data using statistical software.
3. Apply basic concepts of probability and statistics in real business situations.

Statistics for Quantitative Research Bridging (Online) (STAT512)

This course prepares students to design and conduct quantitative research to address and solve business challenges. It provides an empirical basis for applying appropriate statistical research tools, identifying the relevant solutions for business problems and achieving business objectives. Students will learn about a range of topics including statistical interval estimation, hypothesis testing and regression analysis. They will use key statistical techniques to analyze business data and interpret and report the outcomes. Excel and SPSS software packages will be used to analyze the data.

Credit Hours : 0

Course Learning Outcomes

1. Apply most common inferential statistical techniques in the analysis of business data.
2. Apply the appropriate statistical models/techniques to help in the decision-making process.
3. Use statistical software appropriately to explore and analyze data.
4. Interpret the results of statistical analyses of business data in writing.

Foundations for Analytics (STAT520)

This course introduces probability and statistics, as the primary foundations of analytics. Topics include data and summary statistics, descriptive statistics -both graphical and numerical-, basic concepts of probability, normal distribution, survey design, sampling, inference: hypothesis testing and analysis of variance, and correlation. The course also presents simple linear regression, significance tests, multiple regression, and time series analysis. Application of the topics and methods is demonstrated with real world data, using spreadsheet software and effective statistical packages.

Credit Hours : 3

Prerequisites

• STAT130 with a minimum grade D or STAT501 with a minimum grade D

Course Learning Outcomes

1. Discuss the foundational concepts and methods of probability and statistics,
2. Perform data exploration, data cleaning, and data processing using descriptive and graphical techniques.
3. Apply basic inferential statistics for drawing valid conclusions from data.
4. Select and fit the appropriate linear regression model based on data visualization, and diagnostics.
5. Use spreadsheet software and statistical packages to conduct statistical analysis and interpret the findings.

Data Analytics & Machine Learning (STAT555)

Data analytics applies visual, statistical, and computational models and methods systematically, for discovering insights from data. In the context of enterprises, such insights are essential for improving performance and achieving success in the markets. In data analytics, data is collected, processed, modeled, and analyzed through descriptive, predictive, and prescriptive methods. Some of the most powerful methods of data analytics come from the field of machine learning, a branch of artificial intelligence (AI), where algorithms automatically learn hidden patterns from data. Besides providing definitions, goals, and processes, this course presents a multitude of technical topics and methods, including supervised (regression, classification) and unsupervised (association mining, clustering) machine learning, predictive modeling, model fitting, overfitting and its avoidance, model evaluation, and visualization of model performance. Methodological foundations are supported with case study discussions and illustrated through experiential learning, where real-world datasets are analyzed with powerful modeling software.

Credit Hours : 3

Prerequisites

• STAT520 with a minimum grade D

Course Learning Outcomes

1. Discuss machine learning and data analytics models, methods, processes, and tools. (CLO1)
2. Apply supervised (regression, classification) and unsupervised (association mining, clustering) machine learning with real-world data from business and industry.
3. Discover actionable insights through text processing, mining, and summarization techniques.
4. Explain the importance and application of privacy, ethics, and governance in business analytics.
5. Conduct applied research projects by analyzing real-world data through data analytics.
6. Communicate effectively and efficiently the models, methods, and analysis results of machine learning, verbally and in writing.

Decision Techniques and Data Analysis (STAT609)

The course provides a structured approach for describing, analyzing, and finalizing decisions involving uncertainty. It introduces various decision analysis techniques and principles of designing decision support systems for carrying out sensitivity analysis. It also presents key probability and statistical techniques used in modeling and analyzing business data and providing empirical evidence for action recommendation. Topics include decision analysis techniques, descriptive and inferential statistics, one-way and two-way analysis of variance, modelling using regression analysis, times series regression, exponential smoothing and forecasting.

Credit Hours : 3

Prerequisites

• STAT210 with a minimum grade D or STAT130 with a minimum grade D or STAT500 with a minimum grade D

Course Learning Outcomes

1. Determine the appropriate statistical and decision analysis technique to help in the decision making process.
2. Carry out the most common statistical analysis, like hypothesis testing, Anova and regression and time series modeling.
3. Carry out decision and sensitivity analysis using decision trees.
4. Apply these techniques in the modeling of business related situation.
5. Carry out a model selection procedure, and relate the result to reality of the situation studied.
6. Evaluate model assumptions and goodness-of-fit.

Experimental Design & Analysis (STAT612)

This courses provides students with an understanding of the required steps in planning experiments; principles of experimental design; application of some designs in product development systems and evaluation factorial design; linear programming, CRD, RCD, LS, regression and correlation: and inspection of mean differences.

Credit Hours : 3

Course Learning Outcomes

1. Describe the principles of experimental design.
2. Select and plan the appropriate statistical design for an experiment.
3. Analyse and interpret the results of an experiment.
4. Use statistical software in the design and analysis of experiments.

Design/Analysis of Experiments (STAT615)

This course focuses on design of experiments, optimum selection of input for experiments, and the analysis of results. Full factorial as well as fractional factorial designs, response surface designs, complete randomized designs, ANOVA, multiple regression, normal probability plot, importance of analyzing interactions, signal to noise ratios, confidence intervals, and variance reduction analysis are covered in this course. Statistical analysis software such as SPSS and Minitab will be used.

Credit Hours : 3

Prerequisites

• STAT210 with a minimum grade D or STAT503 with a minimum grade D

Course Learning Outcomes

1. Apply A Systematic Process For Designing And Conducting An Experiment For A Given Application And Objective Of Experimentation. This Should Involve Selection Of Response Variables, The Selection And Characterization Of Factors, Levels, And Ranges, The Choice Of Experimental Design, Data Collection, And Statistical Analysis.
2. Apply A Systematic Process For Designing And Conducting An Experiment For A Given Application And Objective Of Experimentation. This Should Involve Selection Of Response Variables, The Selection And Characterization Of Factors, Levels, And Ranges, The Choice Of Experimental Design, Data Collection, And Statistical Analysis
3. Describe (Verbally/Written) The Basic Principles Of Experimental Design: Randomization, Replication, And Blocking
4. Describe (Verbally/Written) The General Anova Procedure Including The Model For The Data, The Decomposition Of The Sum Of Squares, The Calculation Of Means Square, Hypothesis Testing And Sampling Distributions, And Model Adequacy Checking
5. Describe (Verbally/Written) The Basic Principles Of Experimental Design: Randomization, Replication, And Blocking.
6. Describe (Verbally/Written) The General Anova Procedure Including The Model For The Data, The Decomposition Of The Sum Of Squares, The Calculation Of Means Square, Hypothesis Testing And Sampling Distributions, And Model Adequacy Checking.
7. Implement Various Statistical Analyses Involved In A Designed Experiment In A Software Package Of Choice
8. Review The Various Types Of Experimental Designs Covered In Class And Describe A Situation Where The Design Is Appropriate
9. Implement Various Statistical Analyses Involved In A Designed Experiment In A Software Package Of Choice.
10. Review The Various Types Of Experimental Designs Covered In Class And Describe A Situation Where The Design Is Appropriate.

Multivariate Systems & Modeling (STAT621)

This course provides students with an understanding of mathematical models for evaluating resource management strategies. It covers stochastic and deterministic simulation for optimization, System control structures and team modeling approach.

Credit Hours : 3

Statistics & Quantitative Analysis (STAT640)

This course prepares MBA students to design and conduct research to address and solve business challenges. It provides an empirical basis for the analysis and action recommendations for the solution of business problems or for the achievement of business objectives. MBA students will learn to frame, plan, and conduct research projects as well as developing and fine-tuning forecasting models. Students will apply key statistical techniques used in modeling and analyzing research findings and business data.

Credit Hours : 3

Course Learning Outcomes

1. Apply Statistical Techniques In The Modeling Of Business Related Situation.
2. Choose And Apply The Appropriate Statistical Model/Technique To Help In The Decision Making Process.
3. Choose The Appropriate Statistical Technique To Help In The Decision Making Process.
4. Describe And Apply The Most Common Statistical Analysis, Like Hypothesis Testing, Anova And Regression Modeling.
5. Describe The Most Common Statistical Techniques In The Modeling Of Business Related Situations.
6. Evaluate The Model Assumptions, And Relate The Result To Reality Of The Situation Studied.
7. Interpret And Communicate The Results Of Statistical Analysis.
8. Interpret And Communicate The Results Of Statistical Analysis.
9. Select Appropriate Model, Evaluate Model Assumptions, And Relate The Result To Reality Of The Situation Studied.
10. Use Appropriate Statistical Software To Explore And Analyze Data.
11. Use Appropriate Statistical Software To Explore And Analyze Data.

Geo-Statistics (STAT661)

This course provides students with an understanding of computer-based methods in geographical analysis. It focuses on bivariate and multivariate regression, discriminant analysis, factor analysis, and analysis of spatial and temporal data.

Credit Hours : 2

Course Learning Outcomes

1. Apply These Techniques In The Modeling Of Gis Related Situation.
2. Carry Out A Model Selection Procedure, And Relate The Result To Reality Of The Situation Studied.
3. Carry Out The Most Common Statistical Analysis, Like Hypothesis Testing, Anova Regression Modeling, Multivariate Analysis And Analysis Of Spatial/ Temporal Data.
4. Determine The Appropriate Statistical Technique To Help In The Study And Decision Making Process.
5. Evaluate Model Assumptions And Goodness-Of-Fit.

Advanced Statistical Models (STAT710)

The course provides an in-depth study of regression and analysis of variance models. Topics include multiple regression and model building, multiple and partial correlation, analysis of residuals, analysis of variance, multivariate analysis of variance, generalized linear model, and various applications of statistical modeling. Computer software packages such as SAS, SPSS, or R will be used to carry out the data analysis. This course is designed for doctoral students to get familiar with statistical modeling for their research projects. The emphasis of the course is on the applications and fine-tuning of statistical modeling techniques.

Credit Hours : 3

Course Learning Outcomes

1. Apply computational methods for statistical learning and data analysis.
2. Employ a spectrum of statistical tools and methods to make decision and inferences.
3. Formulate solutions for academic research problems in IT using problem analysis and critical thinking
4. Identify appropriate models that best fit analyzed research data.
5. Interpret results of model-oriented research.

Advanced Quantitative Research Methods (STAT712)

The course provides an in-depth study of advanced statistical methods used in quantitative research. It covers important modeling and analysis tools which include Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA), multivariate analysis of variance (MANOVA), multiple regression including interactions, logistic regression, discriminant analysis, factor analysis, and structural equation modeling. The course emphasizes applications using a comprehensive statistical packages such as SPSS, MINITAB, SAS or R.

Credit Hours : 3

Prerequisites

• CURR710 with a minimum grade C

Course Learning Outcomes

1. Select the appropriate statistical methods for analyzing various types of data.
2. Formulate research questions as statistical hypotheses and test their significance.
3. Develop parsimonious models for a wide range of data, test their validity and describe their limitations.
4. Implement linear models, latent variable models using statistical software.
5. Interpret statistical analysis results in the context of the research application.

Design and Analysis of Experiments in Applied Sciences (STAT715)

This course focuses on the design and analysis of experiments in applied sciences. It covers ANOVA, full and fractional factorial designs, blocked designs, response surface designs, robust designs, experimentation and modeling strategies, and parameter design optimization. Statistical analysis software such as Minitab and R will be used.

Credit Hours : 3

Prerequisites

• STAT235 with a minimum grade D or STAT503 with a minimum grade D

Course Learning Outcomes

1. Discuss the use of experimental design methods to control bias and reduce errors in various experimental setups (randomization, replications and blocking, etc). (PLO2)
2. Create an appropriate design of experiments for different research problems under various protocols. (PLO2, PLO3, PLO5)
3. Implement various statistical analyses involved in a designed experiment using a software package of choice. (PLO3)
4. Evaluate different techniques in response surface designs including CCD and BBD methods. (PLO2, PLO4)
5. Evaluate the quality of an experimental design and its analysis. (PLO4, PLO5)