CSC 2621 - Introduction to Data Science

2 lecture hours 2 lab hours 3 credits

Course Description
This course introduces applied data science including data preparation, exploratory data analysis, data visualization, statistical testing, and predictive modeling. Emphasis will be placed on extracting information from data sets that can be turned into actionable insights or interventions. Problems and data sets are selected from a broad range of disciplines of interest to students, faculty, and industry partners.
Prereq: CSC 2611 (quarter system prereq: CS 2300, CS 2852)
Note: None
This course meets the following Raider Core CLO Requirement: None

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

Read scientific literature and interpret results
Communicate computational results and their interpretations in language appropriate for a scientific audience
Generate hypotheses about the relationships between pairs of variables
Use evidence from descriptive statistics, visualizations, and statistical testing to assess correctness of hypotheses
Leverage appropriate libraries and declarative programming techniques to efficiently solve non-trivial data cleaning and transformation challenges such as joining data sets, unifying types and representations of values, and handling missing data
Use exploratory data analysis (e.g., summary statistics and visualizations) to characterize data sets, their variables, and relationships between variables
Convert a variety of variable types into numerical features for use in machine learning models
Determine which variables are informative for use in machine learning models
Create well-reasoned, logical arguments regarding the potential biases of data sets and their impact on the results of a computational model
Identify potential negative implications of applications of machine learning models

Prerequisites by Topic

Proficiency in at least one programming language
Familiarity with common data structures (e.g., lists, maps, and sets)
Familiarity with Python, Pandas, and visualization libraries

Coordinator
Dr. John Bukowy

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