2023-2024 Course Catalog
Welcome to Virginia Tech! We are excited that you are here planning your time as a Hokie.
Welcome to Virginia Tech! We are excited that you are here planning your time as a Hokie.
Career opportunities and job search strategies in the business information technology and operations fields with reference to the BIT courses that best help the student identify a career in his/her selected field. Includes career skills development and resume writing. Pass/Fail only.
Introduction to multiple analytical perspectives on contemporary security environments, including political, legal, ethical, technical, environmental and historical and cultural perspectives relative to the conception, design and implementation of security solutions, practices, and policies. Emphasizes applying and analyzing the effectiveness of diverse procedures, tools and policies used in security and privacy solutions, decision-making, risk management and operational policy to mitigate local, national, international and global threats.
Study of how people and organizations in multiple cultures analyze, model and solve problems from a business perspective. Addresses ethical considerations in developing and solving problems. No statistics background is required. Sophomore standing.
Introduction to basic statistical (inference) tools, analytics techniques, and modeling necessary in managerial decision-making. The decision-making aspect of the course, while utilizing quantitative/computational thinking, will emphasize ethical reasoning. Topics include, but are not limited to, descriptive statistics, elementary probability theory, sampling and sampling distributions, portfolio management, hypothesis testing, regression analysis, analysis of variance, big data, and data analytics.
Overview of analytic models and solution techniques in decision science. Discussion of descriptive and predictive analytics goals and methods. In addition to overview of mathematical modeling and solution techniques, discussions will include considerations of adapting analytics methods to various global and ethical business applications. Students should develop skills and appreciation of the use of data and analytics for problem solving.
Build foundational knowledge of the Linux operating system and file system concepts necessary for cybersecurity business professionals. Discuss the purpose, use, architecture, and navigation of the Linux file system. Build skill using a command line operating system for system administration and security management tasks. Employ file and directory management, process management, text manipulation, and permissions in the Linux operating system. Create shell scripts for process automation, configuration and process management, and security applications. Hands-on experience securing and hardening the Linux operating system.
This course provides students with an international business experience. It is only offered as part of a program outside of the United States. Students will learn from the structured educational experience developed by the faculty leader. This course is intended for students who want to develop information technology or operations management related free electives. Pre: Instructors consent and the completion of 24 semester hours with a minimum GPA of 3.0 or departmental consent.
Study of the process directly related to the creation and distribution of goods and services. Increasingly, these operations are taking place outside the boundaries of a traditional enterprise. This course teaches students how to analyze processes, ensure quality, create value, and manage the flow of information, products and services across a network of customers, enterprises and supply chain partners.
Introduction of modeling of problems encountered in business analytics. Statistical and optimization modeling, computer solution, and analysis of business problems. Uses spreadsheet and database software to facilitate the modeling and solution of these problems.
Study of selected, advanced topics in decision modeling and business analytics. Emphasis on model formulation, solution techniques, interpretation of results and comprehensive approaches to problem-solving. Integer, multi-criteria, and non-linear programming as well as network analysis and heuristics. Includes case studies and use of Excel as the primary analytical tool.
Study of selected advanced topics in business computing. Construction of business applications using an advanced application development environment such as Visual Studio.net. Coverage of computer terminology, HTML, and Internet applications. The course builds computer literacy and strong programming skills. Junior standing required.
Examines the technical aspects of business process improvement focusing on improvement strategies, quality control, data analysis and mining, and maturity models. Emphasizes analytical techniques for business process design, control, and improvement.
The study of the design, analysis and implementation of enterprise-wide resource planning and control systems. The course examines decision support models for production planning, master scheduling, inventory control, shop floor control and related topics in planning and control. The course emphasizes the application of information technologies such as ERP, MRPII, CIM to operations planning and control.
Python for data wrangling and data analysis to support business decisions. Illustrate proficiency in Python, basic skills such as variables, functions, conditionals, loops, libraries, and data structures. Leverage Python skills to learn how to manage data. Learn how to clean, transform, and augment data, as well as how to use Python to obtain data, in particular to work with application programming interfaces (APIs) and do web scraping. Learn how to use Python to conduct data analysis and for plotting and visualization of data. Apply Python for time series data analysis and data modeling using libraries.
Python and R for advanced data analysis, including predictive analytics and machine learning, to support business decisions. Use Python and R to conduct exploratory data analysis. Learn how to handle data, sampling distributions, statistical experiments, and significance testing in Python and R. Apply simple and multiple linear regression and related concepts such as confidence intervals, dummy variables, correlation, multicollinearity, confounding variables, interactions and main effects, and outliers. Learn how to apply classification, specifically NaΪve Bayes, discriminant analysis, and logistic regression. Learn how to apply model evaluation techniques such as ROC curves, AUC, and lift. Introduction to and application of machine learning, including k-nearest neighbors, tree models, principal component analysis, and hierarchical clustering.
Study of the current technologies for designing and developing computer-based business systems. Topics will include process, structural, behavioral, and conceptual data modeling methodologies such as Uniform Modeling Language (UML) and important design-related issues such as data flows and system capabilities. Design issues will be explored through class projects. This course duplicates BIT 4524.
Study of the design of databases and data structures for supporting business applications. Basic database structure and design, structured query language, database management systems, integration of backend database servers, data warehousing and mining, on-line analytical processing, and database application, security, and management. This course duplicates BIT 4514.
Provides an introduction to computer networks and data communications in business. Topics include mechanisms for reliable data transfer, network topologies and technologies, and a comprehensive treatment of inter-networking. Additional topics include packet switching, and cloud, edge, and advanced networking. Security issues related to using computer networks are discussed, along with network design issues, and methodologies for network applications. Duplicates BIT 4554.
Election system and historical voting in the U.S., their inherent vulnerabilities, and how to locate and prepare against poor election security practices. Trust and cultural significance of voting in a democratic America. The election system security standards and regulations including the Virginia Department of Elections Voting Systems Security Policies, Standards, Guidelines, and Locality Election Security Standards (LESS). Threats, vulnerabilities, and attacks on election infrastructures and how they may be evaluated and mitigated. Security controls for voting systems, voter registration databases, and associated IT infrastructure and systems used to manage elections while exploring cybersecurity careers in the public sector. Prepares selected students for a summer internship supporting the Cyber Navigator Program with a specific Virginia Locality. Primarily designed for students majoring or minoring in cybersecurity, the course is open to all students of any major.
Identification and analysis of complex, real-world security problems and threats to people, organizations, and nations across multiple domains, roles and future scenarios. Crisis communication, decision making tools, ethical principles and problem-solving methods to respond, assess options, plan, scope, and communicate before, during and after conflicts, disasters and attacks. Use of an experiential learning facility, and participation in a reality-based team simulation of cascading security and disaster events.
Basic perception and design principles and techniques for information visualization, with an emphasis on the application of visualization software for data exploration and the development of analytical skills for business. Includes hands-on exposure to information visualization and statistical software.
In-depth study of the application of computer simulation techniques to business decision making and process improvement. The theory of computer simulation and statistical analysis of results are included. Attention is focused on using simulation software stressing application to specific problems.
Study of current technologies for designing and constructing interactive, Internet-based systems for supporting business decisions. Topics may include the operation of the Internet, server-side programming, client-side programming, server-side scripting, XML, XHTML, database integration, COM, CGI, and others. Design issues will be explored through a class project.
Comprehensive treatment of Decision Support Systems (DSS) as managerial tools, particularly in an e-commerce environment. Emphasis is at the builder and user level. A primary emphasis is on problem solving through the integration of various quantitative techniques as well as on IT concepts. The course includes a comprehensive project using state-of-the-art software.
Advanced study of efficient methods for streamlining the production and delivery of products and services across functions, enterprises and global boundaries. Topics include the facilities, functions, technologies, and activities involved in creating and delivering products and services, especially in a digital marketplace. Designing and managing a network of suppliers across enterprises is discussed, along with the information systems, risk management and planning issues involved.
This course includes concepts and issues critical in the globalization of business operations and information technology. Topics covered include the organization of global operations, cultural and national comparisons, planning global operations, facilities location, product development, technology transfer, global communication links, transborder data flow, international information systems, and other emerging operations and information technology issues.
Study of efficient methods for planning and controlling projects. Topics include project management and scheduling tools, project quality assurance, risk and cost control, resource constrained scheduling, definition and requirements analysis, task integration, and managing alliances. The application of information technology to project management and control is emphasized throughout the course.
Learn concepts and techniques related to artificial intelligence (AI), machine learning (ML), and deep learning (DL). Learn the fundamentals of AI, ML, and DL algorithms and how to apply them to problems of interest to majors in the business information technology (BIT) department. Understand and apply supervised (classification, regression) and unsupervised (clustering) machine learning, and applications of these techniques in business. Apply deep learning, for example, recurrent neural networks (RNN), generative adversarial networks (GAN), and convolutional neural networks (CNN).
Examination of data analytics and automated decision making issues, across multiple technology contexts, through the lens of the humanities and ethics. Privacy, autonomy, data ownership, equality, and accountability. Decision making and exploration of questions of data ethics and data fairness throughout the data life cycle.
Study of policies, procedures, and technologies for enhancing the security of information. Topics include physical security, communications security, emissions security, computer security, and network security. The core security goals of confidentiality, integrity, and availability are emphasized throughout the course.
Application of advanced analytics to cybersecurity in a business setting. Categorization of cyber threats and solutions. Data mining, visualization and machine learning applied to large data sets for anomaly detection, threat prediction, and incident response analysis. Investigation of adversarial machine learning. Selection of appropriate analytics techniques and security platforms. Consideration of business and ethical issues.
Problem-solving framework and analytic techniques for solving messy, unstructured, high-impact, real-world organizational/societal problems within an interdisciplinary, intercultural, experiential learning context. Definition of problem scope, objectives, need for change, ethical concerns, and diversity and inclusion issues; identification of stakeholders and their values; evaluation of decision tradeoffs; problem decomposition and hypothesis formulation; project planning and administration; data versus user requirements, ethical and inclusive decision making, data collection, preparation, and analysis; team roles and management; professional communication of insights, policy and action recommendations.
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