CSC 4801 - Data Science Practicum

2 lecture hours 2 lab hours 3 credits

Course Description
This course provides students with the experience of working on a team in a professional setting to tackle large-scale data analysis projects using real-world data sets provided by industry, academic researchers, and the government. Students are given access to data sets and directed questions and must apply the theory and practices from previous coursework to propose and evaluate hypotheses. Students will hone skills required for visualizing, summarizing, and presenting results to technical and lay audiences. Projects end with teams presenting their results to their clients both verbally and in a structured technical document.
Prereq: CSC 2621, MTH 2480 (quarter system prereq: CS 3300, MA 262)
Note: None
This course meets the following Raider Core CLO Requirement: Integrate Learning

Course Learning Outcomes
Upon successful completion of this course, the student will be able to:

Collaborate with a team to apply data science practices and techniques to analyze real-world data sets
Work with stakeholders in a professional setting to identify project scope, requirements, and deliverables
Perform exploratory data analysis by selecting, applying, and interpreting appropriate visual, statistical, and unsupervised learning methods to generate hypotheses and gain insights into data sets
Communicate findings through data visualizations and technical analysis in both written and oral form
Work with subject matter experts to translate a statement of work into a data science problem by identifying hypotheses, dependent variable(s), appropriate experimental setup, type of learning, and appropriate evaluation metrics
Transform data, engineer features, and perform feature selection for data science tasks
Apply ethical standards to study design and data collection, storage, and sharing policies
Identify potential ethical, data security, and privacy issues that may arise for a given project and approaches for mitigating them

Prerequisites by Topic

Experience with data preparation, data analysis, factor analysis, statistical inference, predictive modeling, and data visualization
Data manipulation and analytics using scripting languages and interactive methods
Basic probability and statistics, including statistical testing

Coordinator
Dr. John Bukowy

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