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Course Criteria
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3.00 Credits
This course helps students to make connections between visualization techniques and analytics algorithms, developing a balanced skill set to more clearly communicate insights from complex data sets. Additional coursework is required for those enrolled in the graduate-level course. Dual Listed: DATA 6400
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3.00 Credits
Students learn how to clean and transform unstructured text data, quantify it using several feature engineering methods, and produce data-driven insights using supervised and unsupervised text mining algorithms. This course covers concepts in both natural language processing and statistical/machine learning. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): DATA 3300 or (DATA 3500 or CS 1400) and (DATA 3100 or STAT 1040 or STAT 1045 or STAT 2000 or STAT 2300 or STAT 3000) Dual-listed as: DATA 6420 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
This course introduces data mining and business intelligence technologies. Students utilize a clustering algorithm for customer segmentation, an association rule algorithm for cross-selling/website optimization, single and ensemble classifiers for churn prediction/fraud detection/targeted ads, and visualization tools for data and output representation. Additional coursework is required for those enrolled in the graduate-level course. Dual Listed: DATA 6480
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3.00 Credits
This course covers advanced Python programming principles and analytics applications, including object-oriented programming, sorting, data structures, cloud computing, data mining, and introductory machine learning. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite/Restriction: DATA 3500 with a grade of C- or better Dual Listed: DATA 6500
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3.00 Credits
AI-generated code, and associated tools, are rapidly advancing. This course covers software engineering principles, architecture principles, software development with AI-generated code, AI code testing, and AI code deployment of a larger software applications. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): DATA 3500 or CS 1400 Dual-listed as: DATA 6570 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
Students learn about applied simple and multiple regression, regression models for supervised learning, correlation and collinearity, cost functions, model building, feature importance, logistic regression, and advanced methods for classification. The focus is on applications in business analytics. Prerequisites/Restrictions: DATA 3100 DATA 3500 (may be taken concurrently)
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3.00 Credits
This course covers applications of modern machine learning for business decision-making. Students learn about the modeling process to enable rational data-driven business decisions, using machine learning algorithms and their computational implementation in real-world settings. Additional coursework is required for those enrolled in the graduate-level course. Prerequisites/Restrictions: DATA 5600 Dual listed as: DATA 6610
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3.00 Credits
This course explores time series prediction using Python to build classical, machine learning, and deep learning models. Through coding projects, students develop cutting-edge skills in analyzing complex data, programming predictive models, and applying techniques to real-world problems. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): DATA 3500 or CS 1400, and DATA 5600 or ECN 4330 or STAT 5100 Dual-listed as: DATA 6630 Repeatable for credit: No Grade Mode: Standard
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3.00 Credits
Computational finance is an interdisciplinary pillar of modern FinTech, at the intersection of data science, computer science, and economics. In this course students apply tools from computational science and statistics to identify and exploit arbitrage opportunities for entrepreneurial financial innovation. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): - DATA 3100 or STAT 3000 - DATA 3500 or CS 1400 - FIN 3200 or CEE 4200 - DATA 5500 or CS 2420 (may be taken concurrently) Dual-listed as: DATA 6690
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3.00 Credits
Prediction is a core activity of modern FinTech. This course uses probability, computational statistics, machine learning, and financial econometrics to develop and implement predictive strategies for trading and risk management by identifying and exploiting arbitrage opportunities. Additional coursework is required for those enrolled in the graduate-level course. Prerequisite(s): DATA 5690 Dual-listed as: DATA 6695
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