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Master of Science in Data Science and Analytics Course Descriptions

All courses are 3 credits each

DSI-505: PROGRAMMING 1: PYTHON  (3 credits)

Python Programming enables students to implement fundamental principles of modern programming using the Python programming language and problem-solving techniques related to computing.
DSI-506: PROGRAMMING 1: R  (3 credits)

This course introduces essential concepts and techniques of programming in the R computer programming language. It covers R variables, data types, arithmetic and logical operations, environments, functions, flow control and loops. The course also discusses using R to get, clean and transform data, which is a critical step in any data analysis project. Upon completion of this course, students should be able to set up an R programming environment and perform common R programming tasks.
DSI-507 PROGRAMMING 2: PYTHON  (3 credits)

This course builds upon the fundamental principles of Python and prepares students to utilize Python for data analysis. It covers Python skills and data structures, how to load data from different sources, rearrange and aggregate it, and how to analyze and visualize it to create high-quality products. Python is a powerful programming language and has a mature and growing ecosystem of open-source tools for mathematics and data analysis. This course covers working with strings, lists and dictionaries (in addition to variables), reading and writing data, use of Pandas for data analysis, group, aggregage, merge and join, time series and data frames, matplotlib for visualization, and creating format, and output figures. This course prepares students for further study of predictive analytics using Python.
DSI-508: PROGRAMMING 2: R  (3 credits)

This course is for students who have an introductory background in R programming. Students will learn how R works with numeric vectors and special values, and how to deal with special values. Students will start working with R to handle text data, and learn about regular expressions, dates, classes and generic functions, as well as matrices, data frames and lists.
DSI-530 SQL - INTRODUCTION TO DATABASE QUERIES  (3 credits)

In this course students will learn to extract data from a relational database using SQL, so statistical operations can be performed to solve problems. The focus is on structuring queries to extract structured data (not on building databases or methods of handling big data). Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-601 PREDICTIVE ANALYTICS 1 - MACHINE LEARNING TOOLS - WITH PYTHON  (3 credits)

In this course, students will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model). The course includes hands-on work with Python, a free software environment with statistical computing capabilities. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-604 PREDICTIVE ANALYTICS 1 - MACHINE LEARNING TOOLS - WITH R  (3 credits)

In this course, students will be introduced to the basic concepts in predictive analytics, also called predictive modeling, the most prevalent form of data mining. This course covers the two core paradigms that account for most business applications of predictive modeling: classification and prediction. Four machine learning techniques will be used: k-nearest neighbors, classification and regression trees (CART), and Bayesian classifiers. The course will also cover the use of partitioning to divide the data into training data (data used to build a model), validation data (data used to assess the performance of different models, or, in some cases, to fine tune the model) and test data (data used to predict the performance of the final model). The course includes hands-on work with R, a free software environment for statistical computing.
DSI-602 PREDICTIVE ANALYTICS 2 - NEURAL NETS AND REGRESSION WITH PYTHON (3 credits)

In this course, students will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis and neural networks. The course includes hands-on work with Python, a free software environment with capabilities for statistical computing. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-605 PREDICTIVE ANALYTICS 2 - NEURAL NETS AND REGRESSION WITH R  (3 credits)

In this course, students will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis and neural networks. The course includes hands-on work with R, a free software environment with capabilities for statistical computing. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-603 PREDICTIVE ANALYTICS 3 - DIMENSION REDUCTION, CLUSTERING, AND ASSOCIATION RULES WITH PYTHON  (3 credits)

In this course, students will cover key unsupervised learning techniques: association rules, principal components analysis, and clustering. Predictive Analytics 3 will include an integration of supervised and unsupervised learning techniques. The course includes hands-on work with Python, a free software environment with capabilities for statistical computing. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-606 PREDICTIVE ANALYTICS 3 - DIMENSION REDUCTION, CLUSTERING, AND ASSOCIATION RULES WITH R  (3 credits)

In this course, students will cover key unsupervised learning techniques: association rules, principal components analysis, and clustering. Predictive Analytics 3 will include an integration of supervised and unsupervised learning techniques. The course includes hands-on work with R, a free software environment with capabilities for statistical computing. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-622 INTERACTIVE DATA VISUALIZATION  (3 credits)

Students will learn about the interactive exploration of data, and how it is achieved using state-of-the-art data visualization software. Participants will learn to explore a range of different data types and structures (Time Series, scatterplots, parallel coordinate plots, trellising, etc.). They will learn about various interactive techniques for manipulating and examining the data and producing effective visualizations. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-700 APPLIED PREDICTIVE ANALYTICS  (3 credits)

In this course students will apply data mining techniques in a real world case study. The case study concerns micro-targeting in political campaigns, but the principles apply equally to any marketing campaign involving individual-level messaging. This course is really a "lab" for practically testing your skills in a real world context. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-509 TEXT MINING  (3 credits)

In this course students will be introduced to the essential techniques of text mining, understood here as the extension of data mining's standard predictive methods to unstructured text. This course will discuss these standard techniques, and will devote considerable attention to the data preparation and handling methods that are required to transform unstructured text into a form in which it can be mined. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-611 NATURAL LANGUAGE PROCESSING  (3 credits)

This course acquaints students to the algorithms, techniques and software used in natural language processing (NLP). Students will examine existing applications, particularly speech understanding, information retrieval, machine translation and information extraction, with regard to work in computational linguistics and artificial intelligence. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-612 DEEP LEARNING (3 credits)

In this online course, students will learn about the rapidly evolving field of Deep Learning. The surge in deployed applications based on concepts and methods in this field is an indication of its potential to help fully realize the promise of Artificial Intelligence. At the end of this course students will understand the basic concepts underlying the representations and methods in deep learning and analyze some applications where deep learning is most effective. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-613 ANOMALY DETECTION  (3 credits)

In this online course, students will learn how to examine data with the goal of detecting anomalies or abnormal instances. This task is critical in a wide range of applications ranging from fraud detection to surveillance. At the end of this course students will have understood the different aspects that affect how this problem can be formulated, the techniques applicable for each formulation and knowledge of some real-world applications in which they are most effective. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-510 FORECASTING ANALYTICS (3 credits)

In this course students will learn how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: Regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-614 CUSTOMER ANALYTICS IN R  (3 credits)

In this course students will work through a customer analytics project from beginning to end, using R. Students will start by gaining an understanding of the problem and the context, and continue to clean, prepare and explore the relevant data. Work will focus on feature engineering, handling dates, summarization, and working with the customer lifecycle concept in data analysis. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-640 SPATIAL STATISTICS WITH GEOGRAPHIC INFORMATION SYSTEMS  (3 credits)

Spatial analysis often uses methods adapted from conventional analysis to address problems in which spatial location is the most important explanatory variable. This course is directed particularly to students with backgrounds in either computing or statistics but who lack a background in the necessary geospatial concepts. Spatial Statistics with Geographic Information Systems will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-511 INTRODUCTION TO NETWORK ANALYSIS  (3 credits)

In this course students will learn a mix of quantitative and qualitative methods for describing, measuring and analyzing social networks. Students will also learn how to identify influential individuals, track the spread of information through networks, and see how to use these techniques on real problems. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-608 R PROGRAMMING INTERMEDIATE (3 credits)

This course will help to prepare students to become experienced data analysts looking to unlock the power of R. It provides a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code. After completing the course, students should be able to manipulate data programmatically using R functions of their own design. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-610 OPTIMIZATION - LINEAR PROGRAMMING (3 credits)

In this course, students will learn how to apply linear programming to complex systems to make better decisions - decisions that increase revenue, decrease costs, or improve efficiency of operations. The course introduces the role of mathematical models in decision-making, then covers how to formulate basic linear programming models for decision problems where multiple decisions need to be made in the best possible way, while simultaneously satisfying a number of logical conditions (or constraints). Students will use spreadsheet software to implement and solve these linear programming problems. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-625 RISK SIMULATION AND QUEUING (3 credits)

This course covers three important modeling techniques. Students will learn how to construct and implement simulation models to model (1) the uncertainty in decision input variables so that the overall estimate of interest from a model can be supplemented by a risk interval of possible other outcomes (risk simulation), and (2) the variability in arrivals over time (customers, cars at a toll plaza, data packets, etc.) and ensuing queues (queuing theory). Students will also learn how to employ decision trees to incorporate information derived from models to actually make optimal decisions. Students will use spreadsheet-based software to specify and implement models. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.
DSI-621 INTEGER AND NONLINEAR PROGRAMMING AND NETWORK FLOW  (3 credits)

In this course students will learn to specify and implement optimization models that solve network problems. Students will also learn how to solve Integer Programming (IP) problems and Nonlinear Programming (NLP) problems. Students will use spreadsheet-based software to specify and implement models. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.

Toni M. Terry, BA

"I am 67 years old, soon to be 68, and to be able to say I did this at this day in my life is just gratification for my own self."

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