CSCI 180 - Introduction to Data Science

5 Credits

Introduction to Data Science is a survey course designed to equip students with fundamental skills in data science. The curriculum encompasses key components such as data integrity, suitability, and cleaning, along with techniques for summarizing and visualizing data. Additionally, the course introduces basic concepts of statistical inference and machine learning. Students gain hands-on experience through labs and applications.

Pre-requisite(s) MATH 81 with a min 2.0
Placement Eligibility Math 91 or higher
FeesAcademic Technology Fee

Quarters Typically Offered
Designed to Serve Students wanting to gain knowledge of data science relevant to the program of study, including students in data-adjacent fields.
Active Date 20240401T16:35:25

Grading Basis Decimal Grade
Class Limit 24
Contact Hours: Lecture 55
Total Contact Hours 55
Degree Distributions:
Transferable Elective Yes
Course Outline

Introduction to data
Data integrity and credibility
Introduction to descriptive statistics and exploratory data analysis
Data visualization techniques
Trend lines and confidence intervals
Hypothesis testing, regression, and simulations
Introduction to machine learning
Machine learning methods
Data science ethics

Student Learning Outcomes
Assess the integrity and suitability of a given dataset to address specific research questions.

Apply descriptive statistics and various visualization techniques in exploratory data analysis to draw conclusions from data or to address research questions, utilizing appropriate tools for analysis.

Apply inferential statistics, encompassing confidence intervals, hypothesis testing, linear regression, and simulations, to draw conclusions from data or to address research questions, utilizing appropriate tools for analysis.

Apply machine learning techniques encompassing decision trees, neural networks, and classifiers to predict numerical or categorical values within datasets, utilizing appropriate tools for analysis.

Advocate for responsible practices in the application of artificial intelligence and data science, considering ethical implications and potential societal impact.