2019-2020 Academic Catalog [Archived Catalog]
Courses
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| Undergraduate Prerequisites |
Course Numbering |
| A “C-” or higher is required in all prerequisite courses |
100-299 - Lower division undergraduate |
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300-499 - Upper division undergraduate |
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500-699 - Graduate |
| Search Tip |
700-799 - Doctoral |
| Use the asterisk (*) key as a wild card. |
| Example: Select “Prefix” NURS and enter 6* in the “Code or Number” field to return all Nursing courses at the 600 level. |
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DSCI 200 - Foundations of Data Science
Credits: Three (3)
The course develops the core concepts and skills in statistical inference and computational techniques through working on real-world data. The course is to introduce the foundation of data science to entry-level students who have not previously taken statistics or computer science courses.
Prerequisite: MATH-117
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DSCI 201 - Math Modeling-Excel
Credits: Three (3)
Students receive basic training in Microsoft Excel. A variety of real-life math models will provide the context for developing spreadsheet proficiency, including functions and formulas, pivotal tables, statistical analysis, numerical solutions, optimization and graphical output. Other areas to be covered include database applications and basic application programming techniques.
Prerequisite: MATH-117
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DSCI 297 - Special Studies
Credits: Variable
Prerequisite: Permission of Program Director
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DSCI 301 - Math Modeling-VBA
Credits: Three (3)
The content focuses on business applications including finance, statistics, and mathematical modeling. The applications provide the context for developing programming skills, using the Visual Basic Applications language as the programming vehicle.
Prerequisite: MATH-117
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DSCI 302 - Introduction to R
Credits: Three (3)
This course covers practical issues in data analysis and graphics that includes programming in R, debugging R codes, Jupyter Notebook, cloud computing, data exploration and data visualization. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: MATH-117
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DSCI 303 - Introduction to Python
Credits: Three (3)
This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: MATH-117
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DSCI 304 - Introduction to SQL
Credits: Three (3)
This course is for students who want to enhance their SQL skills through exploring real-world examples. Topics covered include but are not limited to pattern-matching using regular expressions, analytical functions, common table expressions. Students are expected to be able to construct advanced SQL queries to retrieve desired information from the database and solve real-world problems
Cross-listed: DSCI-504
Prerequisite: MATH-117
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DSCI 307 - SAS Programming
Credits: Three (3)
This class is an introduction to the SAS programming language. Topics include reading, exporting, sorting, printing, and summarizing data; modifying and combining data sets; writing flexible code with the SAS macro facility; visualizing data; and performing descriptive and basic statistical analyses such as Chi-square tests, T-Tests, ANOVA, and regression.Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: DSCI-200
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DSCI 314 - Text Mining
Credits: 3
This course covers text analytics extracting the useful information hidden in the unstructured text such as social media, emails and web pages using R/Python. Topics include corpus, transformations, metadata management, term document matrix, world cloud and topic models. Project based learnings is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: DSCI-303
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DSCI 318 - Statistical Design
Credits: Three (3)
This course covers principles of experiments and basic statistics using R/SAS. Topics include analysis of variance, different designs, analysis of covariance, mixed model, categorical data analysis , survey data analysis, sample size and power analysis and model comparison. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: MATH-316
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DSCI 324 - Data Visualization
This course is intended for students with introductory experience in SQL, R, and Excel. In this course students will learn how to connect SQL Server to tools like Excel, R and Tableau, and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating report-quality graphs and presentations. Students will learn to use R Markdown so that their analysis follows the reproducible research paradigm. Finally, students will learn to build reporting and analytics dashboards in Shiny and Tableau. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
Cross-listed: DSCI-624
Prerequisite: DSCI-302 and DSCI-304
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DSCI 393 - Cooperative Education
Credits: Variable
Cooperative Education is a structured method of combining classroom-based education with practical work experience. A cooperative education experience, commonly known as a “co-op”, provides academic credit for structured job experience. Cooperative Education is taking on new importance in helping young people to make the school-to-work transition, service learning, and experiential learning initiatives.
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DSCI 397 - Special Studies
Credits: Variable
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DSCI 408 - Machine Learning
Credits: Three (3)
This is an introductory course intended primarily for students majoring or minoring in Applied Statistics, Data Science or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, k-means clustering, error calculation, optimization training, tree-based algorithms (including random forests), frequent item sets and recommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks).
Prerequisite: DSCI-302 or DSCI-303
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DSCI 409 - Forecasting Principles
Credits: 3
This course is designed to provide a practical overview of forecasting practices and methodologies. The course will follow more of a “cookbook” approach rather than classical academic course. This course can benefit a wide range of audience from actuarial and data science students to “accidental” data analysts who trained in the sciences, business, or engineering and then found themselves confronted with data for which they have no formal analytic training. In this course, different type of sequential information (such as financial or sales data) will be analyzed and several mathematical models that might be used to describe the processes which generate these types of data will be demonstrated. The end goal is to develop a set of skills to build a forecast model that provides intelligence about what might be expected in the future.
Prerequisite: DSCI-302
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DSCI 412 - Predictive Modeling
Credits: Three (3)
This course prepares students with the fundamental statistical learning and real world business problems. Topics include generalized linear model, tree based models, clustering methods and principal components analysis. It provides students key steps and considerations in building predictive model, selecting a best model and effectively communicating the model results. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: DSCI-302 or DSCI-303
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DSCI 417 - Big Data Analytics
Credits: Three (3)
This course targets data scientists and engineers. It covers programming with RDDS, Tuning and debugging Spark, Spark SQL, Spark steaming and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: DSCI-303
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DSCI 419 - Deep Learning
Credits: Three (3)
This course is an introduction to deep learning with the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, Hopfield networks and Boltzmann machines. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Prerequisite: DSCI-408
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DSCI 501 - Math Modeling
Credits: Three (3)
The course covers Microsoft Excel skills and Visual Basic Applications. A variety of real-life problems will provide the context for developing spreadsheet proficiency, including functions and formulas, pivot tables, data tables, and charts. Other areas to be covered related to data include importing and exporting data, data protection, filtering data, examples of data management, and handling the outputs from other valuation software
Note: This course is for graduate students only.
Cross-listed: DSCI-301
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DSCI 502 - R Programming
Credits: Three (3)
This course covers practical issues in data analysis and graphics that includes programming in R, debugging R codes, Jupyter Notebook, cloud computing, data exploration and data visualization. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-302
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DSCI 503 - Python
Credits: Three (3)
This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-303
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DSCI 504 - SQL
Credits: Three (3)
This course is for students who want to enhance their SQL skills through exploring real-world examples. Topics covered include but are not limited to pattern-matching using regular expressions, analytical functions, common table expressions. Students are expected to be able to construct advanced SQL queries to retrieve desired information from the database and solve real-world problems.
Note: This course is for graduate students only.
Cross-listed: DSCI-304
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DSCI 507 - SAS Programming
Credits: Three (3)
This class is an introduction to the SAS programming language. Topics include reading, exporting, sorting, printing, and summarizing data; modifying and combining data sets; writing flexible code with the SAS macro facility; visualizing data; and performing descriptive and basic statistical analyses such as Chi-square tests, T-Tests, ANOVA, and regression. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-307
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DSCI 508 - Machine Learning
Credits: Three (3)
This is an introductory course intended primarily for students majoring or minoring in Applied Statistics, Data Science or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, k-means clustering, error calculation, optimization training, tree-based algorithms (including random forests), frequent item sets and recommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks)
Note: This course is for graduate students only.
Cross-listed: DSCI-408
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DSCI 512 - Predictive Modeling
Credits: Three (3)
This course prepares students with the fundamental statistical learning and real world business problems. Topics include generalized linear model, tree based models, clustering methods and principal components analysis. It provides students key steps and considerations in building predictive model, selecting a best model and effectively communicating the model results. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
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DSCI 598 - Capstone Project
Credits: Three (3)
The Capstone Project course is for the students to apply the knowledge acquired during the Data Science program to a company project involving actual data in a realistic setting. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem.
Note: This course is for graduate students only.
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DSCI 609 - Forecasting Principles
Credits: 3
This course is designed to provide a practical overview of forecasting practices and methodologies. The course will follow more of a “cookbook” approach rather than classical academic course. This course can benefit a wide range of audience from actuarial and data science students to “accidental” data analysts who trained in the sciences, business, or engineering and then found themselves confronted with data for which they have no formal analytic training. In this course, different type of sequential information (such as financial or sales data) will be analyzed and several mathematical models that might be used to describe the processes which generate these types of data will be demonstrated. The end goal is to develop a set of skills to build a forecast model that provides intelligence about what might be expected in the future.
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DSCI 613 - NOSQL Database
Credits: Three (3)
This course covers no-relational database on a large scale. Topics include MongoDB, Cassandra, Redis, HBase and Neo4j. Project based learnings is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
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DSCI 614 - Text Mining
Credits: Three (3)
This course covers text analytics extracting the useful information hidden in the unstructured text such as social media, emails and web pages using R/Python. Topics include corpus, transformations, metadata management, term document matrix, world cloud and topic models. Project based learnings is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
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DSCI 617 - Big Data Analytics
Credits: Three (3)
This course targets data scientists and engineers. It covers programming with RDDS, Tuning and debugging Spark, Spark SQL, Spark steaming and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-417
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DSCI 618 - Statistical Design
Credits: Three (3)
This course covers principles of experiments and basic statistics using R/SAS. Topics include analysis of variance, different designs, analysis of covariance, mixed model, categorical data analysis , survey data analysis, sample size and power analysis and model comparison. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-318
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DSCI 619 - Deep Learning
Credits: Three (3)
This course is an introduction to deep learning with the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, Hopfield networks and Boltzmann machines. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.
Note: This course is for graduate students only.
Cross-listed: DSCI-419
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DSCI 624 - Data Visualization
Credits: Three (3)
This course is intended for students with introductory experience in SQL, R, and Excel. In this course students will learn how to connect SQL Server to tools like Excel and R and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating report-quality graphs and presentations. Students will learn how to use tools like R Notebooks so that their analysis follows the reproducible research paradigm. Finally, students will learn to create simple web applications in R Shiny to build reporting and analytics dashboards. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
Note: This course is for graduate students only.
Cross-listed: DSCI-324
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DSCI 625 - Blockchain
Credits: Three (3)
This course will introduce students to the workings and applications of this potentially disruptive technology. This course explores the fundamentals of the public, transparent, secure, immutable and distributed database called blockchain. Blockchains can be used to record and transfer any digital asset not just currency. Its potential impact on financial services, government, banking, contracting and identity management will be discussed. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
Note: This course is for graduate students only.
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DSCI 697 - Thesis/Research
Credits: Three (3)
A master’s thesis is a piece of original scholarship written under the direction of a data science faculty advisor. Students need to write a data science academic paper in which a research question is developed and analyzed through original empirical and/or theoretical research, supplemented with a literature review. Students will do both a written final report and a presentation.
Note: This course is for graduate students only.
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