Course Search Results

• DATA 5620: Advanced Regression for Causal Inference

  3.00 Credits

  Utah State University

  
  This course focuses on the application of regression to inform decision-making, particularly using interpretable models to understand the effect of interventions on business outcomes. Students learn to model experimental and observational data and infer causality instead of correlation only. Additional coursework is required for those enrolled in the graduate-level course
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• DATA 5630: Deep Forecasting

  3.00 Credits

  Utah State University

  
  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.
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• DATA 5690: Computational Methods for FinTech

  3.00 Credits

  Utah State University

  
  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.
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• DATA 5695: Predictive Methods for FinTech

  3.00 Credits

  Utah State University

  
  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.
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• DATA 5910: Data Competitions and Problem Solving

  3.00 Credits

  Utah State University

  
  Prepare professionally for job interviews and future opportunities through hands-on experience with real-world, open-ended problems. Students in this class will collaborate to solve industry-grade data challenges from start to finish through data cleaning, exploratory analysis, machine learning, and data presentation. Additional coursework is required for those enrolled in the graduate-level course.
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• DATA 6060: Analytics in the Enterprise

  3.00 Credits

  Utah State University

  
  This course provides foundational knowledge and important skills for business professionals regarding the role and use of analytics in today's enterprises.
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• DATA 6330: Data Pipeline Engineering

  3.00 Credits

  Utah State University

  
  This course explores the design and engineering of effectively structured data ecosystems. Using programming tools, students develop efficient and effective dataflows to ensure quality data are available to meet critical data analysis requirements. Additional coursework is required for those enrolled in the graduate-level course.
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• DATA 6360: Data Warehousing

  3.00 Credits

  Utah State University

  
  This course introduces the process of creating and populating data warehouses for enterprise-level decision making. Topics include data consolidation from operational SQL databases via ETL process, database schema design and implementation, and various report generation. Additional coursework is required for those enrolled in the graduate-level course.
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• DATA 6400: Visual Data Analytics

  3.00 Credits

  Utah State University

  
  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.
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• DATA 6420: Introduction to Text Mining

  3.00 Credits

  Utah State University

  
  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.
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