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Course Criteria
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3.00 Credits
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.
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3.00 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.
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3.00 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.
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3.00 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.
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3.00 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.
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3.00 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.
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3.00 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.
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3.00 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 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.
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3.00 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.
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3.00 Credits
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.
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