Master of Science in Data Science and Analytics Course Descriptions

Required: 24 creditstop of page

DSI-505: Programming 1: Python (3 credits)
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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)
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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)
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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)
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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)
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In this course students will learn to extract data from a relational database using SQL (Structured Query Language), 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)
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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)
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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)
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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)
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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)
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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)
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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)
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In this course students will apply data mining techniques in a real-world case study. The case study concerns microtargeting 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 student's 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.

Electives- 12 credits (Choose 4)top of page

DSI-509: Natural Language Processing I (3 credits)
In this course you will be introduced to the essential techniques of natural language processing (NLP) and text mining with Python. The course will discuss how to apply unsupervised and supervised modeling techniques to text, and devote considerable attention to data preparation and data handling methods required to transform unstructured text into a form in which it can be mined.
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-511: Introduction to Network Analysis (3 credits)
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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 ( credits)
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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)
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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-611: Natural Language Processing II (3 credits)
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In this course you will learn about deep neural networks, and how to use them in processing text with Python. We start with an introduction to neural networks, then extend that knowledge to deep neural networks. We then move on to applications in applying word embeddings, recurrent neural networks, attention, and transformers for information extraction, text classification and other natural-language analytics.
DSI-613: Anolmaly Detection (3 credits)
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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-614: Customer Analytics in R (3 credits)
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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 life cycle 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-621: Integer and Nonlinear Programming and Network Flow (3 credits)
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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.
DSI-625: Risk Simulation and Queuing (3 credits)
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This course covers important modeling techniques. Students will learn how to construct and implement simulation models to model 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 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-640: Spatial Statistics with Geographic Information Systems (3 credits)
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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.