Courses

DASC 1001. Introduction to Data Science. 1 Hour.

Introduction to Data Science is a course providing an overview of Data Science and preparation of Data Science First Year students for the Data Science program and for choosing one of the Data Science program concentrations: Bioinformatics, Biomedical and Healthcare Analytics, Business Data Analytics, Computational Analytics, Data Science Statistics, Geospatial Data Analytics, Operations Analytics, Social Data Analytics, or Supply Chain Analytics. Corequisite: Lab component, drill component and MATH 2554. (Typically offered: Fall, Spring and Summer)

DASC 1104. Programming Languages for Data Science. 4 Hours.

Programming Languages for Data Science provides a semester-long introduction to basic concepts, tools, and languages for computer programming using Python and R, two powerful programming languages used by data scientists. This class will introduce students to computer programming and provide them with the basic skills and tools necessary to efficiently collect, process, analyze, and visualize datasets. Students will gain hands-on experience with de novo programming in R and Python, finding and utilizing packages, and working in both interactive (Jupyter and RStudio) and non-interactive (Unix) environments. Corequisite: Lab component. (Typically offered: Fall, Spring and Summer)

DASC 1204. Introduction to Object Oriented Programming for Data Science. 4 Hours.

Introduction to Object Oriented Programming for Data Science, introduces object-oriented programming in JAVA. It covers object-oriented programming elements and techniques in JAVA, such as primitive types and expressions, basic I/O, basic programming structures, abstract data type, object class and instance, Methods, Java File I/O, object inheritance, collections and composite objects, advanced input /output: streams and files, and exception handling. Students will gain hands-on programming experience using JAVA. Corequisite: Lab component. Prerequisite: DASC 1104. (Typically offered: Fall, Spring and Summer)

DASC 1222. Role of Data Science in Today's World. 2 Hours.

Role of Data Science in Today's World is a survey course providing an overview of the Data Science Curriculum and an introduction to the essential elements of data science: data collection and management; summarizing and visualizing data; basic ideas of statistical inference; predictive analytics and machine learning. Students will gain hands-on experience using the Python programming language and Jupyter notebooks. Prerequisite: DASC 1104. (Typically offered: Fall, Spring and Summer)

DASC 188V. Special Topics in Data Science. 1-6 Hour.

Special Topics in Data Science is a course for data science topics not covered in other courses. (Typically offered: Fall, Spring and Summer) May be repeated for up to 9 hours of degree credit.

DASC 2103. Data Structures & Algorithms. 3 Hours.

Data Structures & Algorithms focuses on fundamental data structures and associated algorithms for computing and data analytics. Topics include the study of data structures such as linked lists, stacks, queues, hash tables, trees, and graphs, recursion, their applications to algorithms such as searching, sorting, tree and graph traversals, divide-and-conquer, greedy algorithms, and dynamic programming, and the theory of NP-completeness. Students will gain hands-on experience using Python or Java. Corequisite: Lab component. Prerequisite: DASC 1204. (Typically offered: Fall)

DASC 2113. Principles and Techniques of Data Science. 3 Hours.

Principles and Techniques in Data Science is an intermediate semester-long data science course that follows an overview of data science in today's world. This class bridges between introduction to data science and upper division data science courses as well as methods courses in other concentrations. This class equips students with essential basic elements of data science, ranging from database systems, data acquisition, storage and query, data cleansing, data wrangling, basic data summarization and visualization, and data estimation and modeling. Students will gain hands-on experience using Python and various packages in Python. Corequisite: Lab component. Prerequisite: MATH 2564. (Typically offered: Fall)

DASC 2203. Data Management and Data Base. 3 Hours.

Data Management and Data Base focuses on the investigation and application of data science database concepts including DBMS fundamentals, database technology and administration, data modeling, SQL, data warehousing, and current topics in modern database management. Corequisite: Lab component. Prerequisite: DASC 1204. (Typically offered: Spring)

DASC 2213. Data Visualization and Communication. 3 Hours.

Data Visualization and Communication is a seminar providing an essential element of data science: the ability to effectively communicate data analytics findings using visual, written, and oral forms. Students will gain hands-on experience using data visualization software and preparing multiple formats of written reports (technical, social media, policy) that build a data literacy and communication toolkit for interdisciplinary work. In essence, this is a course emphasizing finding and telling stories from data, including the fundamental principles of data analysis and visual presentation conjoined with traditional written formats. Corequisite: Lab component. Prerequisite: DASC 1104 and DASC 1222. (Typically offered: Spring)

DASC 2594. Multivariable Math for Data Scientists. 4 Hours.

Multivariable Mathematics for Data Scientists provides an in depth look at the multivariate calculus and linear algebra necessary for a successful understanding of modeling for data science. Students will gain an understanding of the mathematical and geometric concepts used in optimization and scientific computation using mathematical and computational techniques. At the end of the course, students will be equipped with the calculus and linear algebra skills and knowledge to be successful in courses in optimization and advanced data science methods.Prerequisite: MATH 2564 and DASC 1104. (Typically offered: Fall)

DASC 3103. Cloud Computing and Big Data. 3 Hours.

Cloud Computing and Big Data covers: introduction to distributed data computing and management, MapReduce, Hadoop, cloud computing, NoSQL and NewSQL systems, Big data analytics and scalable machine learning, real-time streaming data analysis. Students will gain hands-on experience using Amazon AWS, MongoDB, Hive, and Spark. Corequisite: Lab component. Prerequisite: DASC 2594 and DASC 2203. (Typically offered: Fall)

DASC 3203. Optimization Methods in Data Science. 3 Hours.

Optimization Methods in Data Science is an advanced mathematical course providing the foundations and concepts of optimization that are essential elements of machine learning algorithms in data science, ranging from mathematical optimization to convex optimization to unconstrained and constrained optimization to nonlinear optimization to stochastic optimization. Students will gain hands-on experience using Python and various optimization packages in Python. Corequisite: Lab component. Prerequisite: DASC 2113 and DASC 2594. (Typically offered: Spring)

DASC 3213. Statistical Learning. 3 Hours.

Statistical Learning is a course providing an in depth look at the theory and practice of applied linear modeling for data science: including model building, selection, regularization, classification and prediction. Students will gain hands-on experience using statistical software to learn from data using applied linear models. Corequisite: Lab component. Prerequisite: DASC 1104 and ((MATH 3013 and STAT 3003) or (INEG 2313 and INEG 2333)). (Typically offered: Spring)

DASC 4113. Machine Learning. 3 Hours.

Machine learning covers: logistic regression, ensemble methods, support vector machines, kernel methods, neural networks, Bayesian inference, reinforcement learning, learning theory, and their applications in text, image, and web data processing. Students will gain hands-on experience of developing machine learning algorithms using Python and scikit-learn. Corequisite: Lab component. Prerequisite: DASC 2103 and DASC 3203. (Typically offered: Fall)

DASC 4123. Social Problems in Data Science and Analytics. 3 Hours.

This course explores the ways data analytics and data science are impacted by or intersect with issues of social justice, poverty and economic inequality, racial and ethnic relations, gender, crime, education, health and healthcare, and other contemporary social problems. Corequisite: Lab component. Prerequisite: DASC 1222. (Typically offered: Fall)

DASC 4533. Information Retrieval. 3 Hours.

Information Retrieval is a course providing expertise in processing unstructured data as a key component of data science. It covers text processing, file structures, ranking algorithms, query processing, and web search. Students will gain hands-on experience developing their own search engine from scratch, using Python, C, C++, or Java on a Linux server and making their search engine web accessible. Note: Prior user-level knowledge of Linux for file and directory management and remote login is required for this course. Corequisite: Lab component. Prerequisite: DASC 2103. (Typically offered: Fall and Spring)

DASC 4892. Data Science Practicum I. 2 Hours.

Application of data science, analytics, business intelligence, data mining, machine learning, and data visualization to existing problems. Data Science techniques using current and relevant software and problem-solving methods are applied to current problems for presentation to management. This is the first semester of the required full-year multi-college interdisciplinary practicum using real-world data to solve real-world problems. Corequisite: Lab component, DASC 3213, DASC 4113 and DASC 4123. Prerequisite: DASC 2113, DASC 2213 and DASC 3203. (Typically offered: Fall)

DASC 4993. Data Science Practicum II. 3 Hours.

Application of data science, analytics, business intelligence, data mining, machine learning, and data visualization to existing problems. Data Science techniques using current and relevant software and problem-solving methods are applied to current problems for presentation to management. This is the second semester of the required full-year multi-college interdisciplinary practicum using real-world data to solve real-world problems. Corequisite: Lab component. Prerequisite: DASC 4892 with a grade of C or better. (Typically offered: Spring)