Master of ScienceAnalytics

The first course in analytics covers the core concepts and applications of analytics. The student is introduced to the main concepts and tools of analytics including descriptive, predictive, and prescriptive analytics. During the course, the student uses a variety of statistical and quantitative methods, computational tools, and predictive models to make data-driven decisions. By the end of the course, the student will apply the concepts to real work projects where, by asking some questions about an issue or situation, use analytical tools to respond to it, and present it to technical and layperson audiences.

This course reviews the fundamental mathematics required to be successful in the analytics program. It is designed to strengthen the mathematical abilities while addressing the requirements for coding/scripting. It presents the mathematical topics as coding/scripting problems. This is intended to further strengthen the ability to develop the subroutines/codes/scripts that are also necessary in an analytics career.

This course covers the basic principles of statistical modeling and inference.  The course focuses on developing and fitting several types of regression models, multilevel models, and everything in between.  Topics included in the class cover, prior predictive simulation, sampling from the posterior, interaction terms, covariance, information criteria, and Markov Chain Monte Carlo estimation.

Exploratory data analysis plays a crucial role in the initial stages of analytics. It comprises the preprocessing, cleaning, and preliminary examination of data. This course provides instruction in all aspects of exploratory data analysis. It reviews a wide variety of tools and techniques for pre-processing and cleaning data, including big data. It provides the student with practice in evaluating and plotting/graphing data to evaluate the content and integrity of a data set.

This course takes an applied perspective and provides the statistical tools and analytic thinking techniques needed to: formulate a clear hypothesis, determine the most efficient method to obtain required data, determine and apply the proper statistical techniques to the resulting data, and effectively convey the results to both experts and laypersons. The course begins with a review of the descriptive analytics concepts (i.e., sampling, and statistical inferences) introduced in ANLY 500 as well as general conventions regarding experimentation and research. It then progresses to predictive and prescriptive analytics techniques such as regression and forecasting that can be used to predict future events. Later sessions focus on issues related to lack of experimental control (e.g., quasi-experimental design and analysis). The course culminates with a research project in which the student applies the concepts learned to their own research interests.

The visualization and communication of data is a core competency of analytics. This course takes advantage of the rapidly evolving tools and methods used to visualize and communicate data. Key design principles are used to reinforce skills in visual and graphical representation.

This course focuses on risk management models and tools and the measurement of risk using statistical and stochastic methods, hedging, and diversification. Examples of this are insurance risk, financial risk, and operational risk. Topics covered include estimating rare events, extreme value analysis, time series estimation of external events, axioms of risk measures, hedging using financial options, credit risk modeling, and various insurance risk models.

Web technologies based on text and Natural Language Processing (NLP) are becoming the backbone of analytic solutions for understanding language as text language processing has come to play a central role in the multilingual information society. This course provides a highly accessible introduction to the field of text analytics focusing on processing text, information extraction with named entity recognition, text summarization and generation, text classification, chatbots, and deep learning toolkits. The course is a practical guide on how to create end to end systems for production in business, research, and other applied fields using *R* and Python programming languages.

Decision-making in business today requires the use of all resources, particularly information. Analytics supports decision-making quantitatively by applying information received from multiple sources. This course provides the foundation for quantitative decision-making using a rational, coherent approach and includes decision-making principles and how they are applied to business challenges today.

This course introduces the student to machine learning. It provides the student with the cognitive, mathematical and analytical foundation required for machine learning. It also provides the student with a broad overview of machine learning, including topics from data mining, pattern recognition and supervised and unsupervised learning. This course prepares the student for the complex, higher-level topics which will be provided in deep learning course.

This course considers complex, high-level topics in machine learning. It builds on the foundation provided by Applications of Machine Learning to develop algorithms for supervised and unsupervised machine learning, to study and develop artificial neural networks, to study, develop and evaluate systems for pattern recognition and to consider trade-offs in models, for example, balancing complexity (e.g. volume, variety and velocity of big data) and performance. The focus of this course will be on more advanced techniques such as deep learning and advanced AI.

This course is an introduction to computational language models with a focus on semantic representations. Using methods such as topic models and sentiment analysis, the goal of the course is to show how insights can be derived from text focusing on semantic information. Topics range from basic visualizations of text data to text-based recommendation systems. The topics in this course have real-world applications to gaining insight into human attitudes and behaviors across domains.

This course provides student with exposure to an expanded range of analytical methods. This includes additional functions, e.g. the logit function, additional distributions, e.g. Poisson distribution, and additional analysis techniques, e.g. those included in the study of discrete structures such as combinatorics. Particular attention is paid to analytics relevant to disciplines in the social sciences. Also included are survey design, development and (survey data) analysis.

This course provides the student with the required knowledge and skills to handle and analyze data using a variety of programming languages as well as a variety of programming tools and methods. Depending on current industry standards, the student will be provided with the opportunity to develop knowledge and skills in programming environments such as R, Octave, and Python. In addition, the student is introduced to current industry standard data analysis packages and tools such as those in Matlab, SAS or SPSS.

This course will cover ethical concepts in data analytics and explore ongoing ethical dilemmas in the field. Specifically, this course will cover the legal, moral, and social implications of the analytic process from data collection to data analysis to data storage to communication and decision-making from insights. The objective of the course is to prepare the student to address the ethical challenges in the evolving field of data science.

This course guides the student to develop and finalize a selected research problem and to construct a proposal that effectively establishes the basis for either writing a thesis or launching an experiential capstone project. The course provides an overview of strategies for effective problem investigation and solution proposal. Research methodology is studies and applied as part of suggesting a solution to a problem. Writing and formatting techniques are also explored and applied as a communication tool for cataloging the investigation and recommending the solution.