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DSCI 1301 Principles of Programming for Data Science I
Credit Hours 4.0
Description

This course covers fundamentals of programming and data analysis using Python, including its data-oriented library ecosystem and tools. Topics include Python data types and variables, built-in data structures such as Strings, Lists, Sets, and Dictionaries, Program flow primitives such as conditional statements, for-loops, while-loops, Recursion, introduction to object-oriented design, and Python-enabled libraries and tools for manipulating, processing, cleaning, and crunching data.

DSCI 1302 Principles of Programming for Data Science II
Credit Hours 4.0
Prerequisites DSCI 1301 with a C or higher.
Description

This course covers advanced programming and problem solving for data science in Python. The students will learn how to perform data wrangling by manipulating real world data using a range of data science tools (including the command line, python, jupyter, git, and github). This course will also introduce students to data science Python libraries like SciPy, NumPy and Pandas.

DSCI 2720 Data Structures in Python for Data Science
Credit Hours 3.0
Prerequisites DSCI 1302, CSC 2510, and MATH 2211 with C or higher.
Description

This course covers Data Structures in Python. Topics include Array-Based Sequences, Stacks, Queues and Deques, Linked Lists, Search Trees, Priority Queues, Maps, Hash Tables, and Skip Lists, Sets and Dictionaries, Tries, and Graphs. Time and Space Complexities of operations will be emphasized. Data Science related packages such as NumPy, Pandas, and Matplotlib will also be covered.

DSCI 3000 Ethics for Data Science
Credit Hours 1.0
Prerequisites Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

This course is intended to provide a general introduction to ethics in data science through readings, weekly discussions and case studies. It will provide some context and skills for ethically collecting, storing, sharing, and analyzing data. The topics include awareness of preserving privacy, avoiding bias while 1) collecting, 2) sampling data, 3) discovering/reporting new knowledge, as well as 4) deploying machine learning models.

DSCI 4311 Cloud Computing
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

This course covers topics related to cloud computing including cloud computing infrastructure such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Popular cloud services such as AWS, Microsoft Azure and Google Cloud will be introduced. Container technologies such as Docker, Kubernetes etc. will be introduced.

DSCI 4350 Data Science Capstone
Credit Hours 3.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

The purpose of the Capstone Project is for students to apply theoretical knowledge acquired during the Data Science program to a project involving actual data in a realistic setting. During the project, students 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.

DSCI 4710 Database Systems
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

An introduction to the fundamental concepts and principles that underlie the relational model of data. Topics include formal query languages; SQL; query optimization; relational database design theory; physical database design, integrity, security, and concurrency control.

DSCI 4730 Data Visualization
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

Students must meet the Data Science major eligibility requirement to be able to register for the class. Data visualization or displaying data in visual forms and is closely related to data analytics. In this class, students will study the theories of data visualization, design principles, and data visualization techniques. Students will learn the various tools for creating interactive data visualizations, such as charts, maps, graphs and specialized data visualizations.

DSCI 4740 Data Mining
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

Introduction to basic data mining techniques (such as association rules mining, cluster analysis, and classification methods) and their applications (such as Web data mining, biomedical data mining and security).

DSCI 4760 Big Data Programming
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

This course will cover the technologies, tools, frameworks and languages that are most commonly used in Big Data Programming. Focus will be on algorithms for analyzing and mining massive datasets, graphs and social network data. Topics include the storage, management, processing and analysis of massive datasets, as well as Big Data governance, security, and privacy issues.

DSCI 4780 Fundamentals of Data Science
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

Introduction to the fundamental concepts of predictive data science for tabular data with qualitative and quantitative scales. Topics include: data exploration, pre-processing and visualization; analytics base table (ABT) generation; basic supervised learning algorithms (i.e. information-based learning, similarity-based learning, and error-based learning), and comparative evaluation of these algorithms.

DSCI 4850 Machine Learning
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

This course is intended to provide a general introduction to machine learning. This course will cover the fundamental concepts and principles of supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. Students will understand the basic knowledge of machine learning, be familiar with classic machine learning algorithms, and gain experience of designing and implementing methods in real scenario.

DSCI 4851 Deep Learning
Credit Hours 4.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

This course introduces the basic concepts and algorithms of deep neural networks and its applications to computer vision and natural language processing. Depending on the course progress, selected topics such as unsupervised learning and model compression will be covered. The class emphasizes the understanding of the state-of-the-art DL architectures as well as practical implementations of deep neural networks with Python.

DSCI 4940 Data Science Internship
Credit Hours 1.0–2.0
Prerequisites DSCI 2720 with C or higher. Students must meet the Data Science major eligibility requirement to be able to register for the class.
Description

This course will enable the student to gain signature experience in a Data Science role in the industry. The student will complete a Data Science project during their internship.