Graduate Certificates in Analytics & Technology

Python is an easy to learn, widely versatile programming language whose extensive collection of external libraries makes it a popular choice for business analytics and visualization, data science, artificial intelligence, scientific and numeric computing, and many other applications. Its compatibility with leading analytics tools that are widely used in enterprises also places it in high demand. Students in this course will first learn the fundamentals of programming that are common to all programming languages. They will then work with Python libraries to perform common analytics tasks. No prior programming experience is required.

This course teaches foundational data management, retrieval, and manipulation skills with an emphasis on enabling the students to form a strong foundation for analytical processes. It builds a foundation for understanding various domains of practice with conceptual data modeling and demonstrates how the same conceptual needs can be served with different data management technologies. The course covers relational technologies for both operational databases and data warehouses and non-relational data management infrastructures for analytics. The course will help the students develop strong skills in the use of the SQL language for database definition and data manipulation.

The architecture of modern database systems for data analytics with big data are examined. This course provides a hands-on introduction to several architectures and approaches for data for analytics, including data from operational transactions, sensor data, web logs and social media sites. It explores the different types of data that make up the big data space, and applies capture and storage technologies appropriate for relational and non-relational models, such as clickstreams and user navigation of web sites. Data will be explored using Python-based tools for analytics and visualization. Students enrolled in this course are expected to have basic proficiency in the Python programming language and relational databases.

This course introduces students to the foundations of artificial intelligence (AI) and its use in automation. Fundamental concepts and techniques behind software agents, automated reasoning, machine learning and robotics are introduced and illustrated with applications in various domains. Students will learn how these techniques can be integrated into business operations and functions to increase productivity and to support strategic decision making in organizations. Students will have opportunities to explore AI-based software and tools and discuss the ethical issues related to the development and use of AI.

This course teaches foundational data management, retrieval, and manipulation skills with an emphasis on enabling the students to form a strong foundation for analytical processes. It builds a foundation for understanding various domains of practice with conceptual data modeling and demonstrates how the same conceptual needs can be served with different data management technologies. The course covers relational technologies for both operational databases and data warehouses and non-relational data management infrastructures for analytics. The course will help the students develop strong skills in the use of the SQL language for database definition and data manipulation.

The course will provide a business-focused perspective on analytics in organizations, with emphasis on business fundamentals for analytics professionals (including how businesses are structured, functional areas, core business processes and associated performance metrics, and types of business decisions), the value of analytics in organizations (including organizational-level perspectives on value, managing with analytics, and constraints and consequences of analytics processes such as information security, privacy and ethics), and the practice of analytics (understanding and framing ill-defined business problems in various functional business areas, exploring and visualizing problem-related data, identifying actionable insights, and communicating the results at different organizational levels). The course will feature hands-on exercises with real-world data and analytics applications.

This course examines the theory and practice of designing dynamic visualizations that clarify thinking, facilitate problem-solving, and foster creativity. This course helps students to harness their visual and creative potential and to display this potential in the visual medium. In practice, students will learn to make large collections of verbal and numerical data accessible through carefully crafted visual displays. The unique strengths and weaknesses of both words and visuals are analyzed. Advancing from this analysis, the course helps students design a visual-verbal system where the strengths of one medium support the weaknesses of the other. This complementary system more fully integrates visual and verbal information, thereby dramatically improving the reader's understanding and retention of the communication design.

This course extends students' knowledge and skills gained in database management courses and looks further at business intelligence and data science concepts and techniques. The course explores the data management and analytics architecture and technologies required for solving complex problems facing modern enterprises and organizations. Case studies of organizations using these technologies to support business intelligence gathering and decision making are examined. This course also provides hands-on experience with state-of-the-art data warehousing, analysis, mining, and visualization methods and tools. Requires students to have taken CS 605.

This course provides students with an in-depth coverage of simple and multiple linear regression methods and, as time permits, an introduction to the analysis of time series data. Simple and multiple linear regression techniques are covered, including the use of transformations such as squares and logarithms, the modeling of interactions, and how to handle problems resulting from heteroscedasticy and multicollinearity. Issues surrounding outlying and influential observations are also covered. The art and science of model building are demonstrated with the help of cases. Autocorrelation is then considered, and an introduction to the ARIMA modeling of times series is provided. This course makes use of statistical packages such as SAS, JMP, R or SPSS.

Working with and finding value in data has become essential to many enterprises, and individuals with the skills to do so are in great demand in industry. The required skill set includes the technical programming skills to access, process and analyze a large variety of data sets, including very large (big data) data sets, and the ability to interpret and communicate these results to others. Anyone with these abilities will provide benefit to their organization regardless of their position. This course presents the essentials of this skill set.

Optimization and simulation methods are being used as effective tools in many environments that involve decision-making. This course covers classical and modern optimization techniques used today in a business environment. Specifically, the focus will be on linear and nonlinear programming techniques with applications, as well as elective topics selected from game theory, agent-based modeling, and modern simulation and optimization techniques. Examples of application areas of optimization include portfolio selection in finance, airline crew scheduling in the transportation industry, resource allocation in healthcare industry, and minimizing the cost of an advertising campaign in marketing.

This course covers basic statistical techniques in a managerial setting, and features case studies and conceptual exercises. Statistical topics include effective use of numerical and graphical summaries, estimation and confidence intervals, hypothesis testing and regression. More advanced topics such as data mining, the Bayesian paradigm and principles of model building, may be encountered during projects.

This course provides a technical focus on information, computer and network security, which together form the basis for securing enterprise systems and services. It introduces what cybersecurity means, both in the abstract and in the context of business information systems. Students learn relevant cybersecurity issues, technologies and approaches found in the contemporary enterprise. Students recognize and understand threats to privacy, confidentiality, integrity and service availability as well as best practices to defend both digital and physical assets against such threats.

This course presents an overview of information security issues that must be addressed by organizations in today's ubiquitously networked environments. Specific coverage will include information security risks and related protection of data, networks and application software. While the primary focus is on how to protect organizational information assets, other topics will include strategic uses of security in business, the impact of security risk on various industries, as well as the security and privacy rights and responsibilities of end users and home computer operators. The course is designed to help students think critically about the local, national and global information security issues in our highly networked society.8u6

This course explores the design, selection, implementation and management of enterprise IT solutions. The focus is on applications and infrastructure and their fit with the business. Students learn frameworks and strategies for infrastructure management, system administration, content management, distributed computing, middleware, legacy system integration, system consolidation, software selection, total cost of ownership calculation, IT investment analysis, and emerging technologies. These topics are addressed both within and beyond the organization, with attention paid to managing risk and security within audit and compliance standards. Students also read current vendor and analyst publications and hone their ability to communicate technology architecture strategies concisely to a general business audience.

This course examines typical organizational business processes and the information technology that enables those processes. It reviews qualities of information, including those established by authoritative bodies, to assess the ability of information systems to support the business processes and an organization's management. The course focuses on financial and accounting information systems (AIS) and explores several typical AIS application areas. Issues addressed include the effect of emerging technologies on business processes and their related information systems; control issues pertaining to these systems; and the implications of technology-enabled organizational changes on systems design, implementation and management. Students will be introduced to state-of-the-art tools and techniques for examining business processes and information systems and will engage in a project at a company site.