CSCI 235 - Data Science with R

5 Credits

This course builds on programming fundamentals to focus on the R statistical language and its common uses for data science and applied statistics.

Pre-requisite(s) CSCI 132 min. 2.0 and either MATH& 141 or MATH& 146 min 2.0
FeesAcademic Technology Fee

Quarters Typically Offered
Summer “""”
Fall “""”
Winter “""”
Spring “""”

Designed to Serve Students in data science, web development or computer science and wanting to advance their skills with common toolsets.
Active Date 20250521T15:17:35

Grading Basis Decimal Grade
Class Limit 24
Contact Hours: Lecture 55
Total Contact Hours 55
Degree Distributions:
AA

“""”

ProfTech Course Yes
Restricted Elective Yes
ProfTech Related Instruction

“""”

BAS

“""”

Course Outline
I. The landscape of statistical analysis and data mining

II. How to analyze and explore data in preparation for analysis

Introduction to tools
Summary statistics and interpretation
Correlation, tests, and significance
Transform of data, log trans, missing data, and outliers
Variable selection and data visualization
Telling a story with data

III. Building predictive model building, evaluation, and strategy

Linear regression
Logistic regression
Neural network
Cluster analysis
Decision tree

IV. Design patterns in R

V. Case study in data science

Student Learning Outcomes
Analyze the modern Data Science landscape, including distributed systems, cloud technologies, and emerging technologies.

Apply data preprocessing techniques to prepare data for analysis and modeling using relevant tools and technologies.

Demonstrate statistical concepts core to Data Science, including probability distributions, hypothesis testing, and statistical inference.

Use machine learning models and time series analysis for applications like prediction and anomaly detection.

Implement object-oriented design patterns for statistical modeling of large datasets.

Apply advanced statistical programming techniques to a case study in data science.