DASC 1001 - Programming for Data Science

Description
This course offers an introduction to the R programming language and its role in statistical computing. This course provides a semester-long introduction to basic concepts, tools, and computer programming R. This class will introduce students to computer programming and provide them with the basic skills and tools necessary to efficiently collect, process, analyze, and visualize datasets. Students will gain hands-on experience using real-world data, finding and utilizing packages, and working in both the command-line and GUI environments.

Pre-Requisite
None.

Co-Requisite
PROG 1003 - Programming Logic I

1 Credit Hour(s)

Contact Hours
15 Lecture/Lab

1 Faculty Load Hour(s)

Semesters Offered
Fall, Spring

ACTS Equivalent
None

Grade Mode
A-F

Learning Outcomes
Students completing DASC 1001 should be able to
• apply basic programming skills to analyze data
• interpret and employ R as a language using correct syntax

• use R across multiple environments
• apply R in data analysis
• find and utilize third-party packages and tools

General Education Outcomes Supported
• Students will demonstrate technological fluency.
• Students demonstrate information literacy.

Standard Practices
Topics list
1. Directory structure, permissions, and introduction to the command line
2. Version control with Git and GitHub; Markdown
3. Data formats (csv, json, xml) and data acquisition (SQL, wget, curl, checksum)
4. Project management with Jupyter locally and on the cloud
5. Variables and variable types (integers, floating-point numbers, strings, boolean)
6. Lists and dictionaries
7. Functions and Packages (numpy, pandas)
8. Use of pseudocode and flow charts
9. Introduction to R and R syntax; RStudio locally and on the cloud
10. R: file operations and I/O
11. R: Data types (Variables, Vectors, Matrices, Data frames, Lists) & Tidy Data (dplyr)
12. R: Basic data exploration, summarization, and analysis
13. R: Data exploration and visualization with ggplot2
14. R: Basic statistics (glm)
15. R: Shiny

Learning activities
Assignments and Projects.
This course requires some in class, hands-on work and also additional hands-on work in the
virtual or on-campus computer lab.

Assessments
Homework
Projects
Quizzes
Exams

Grading guidelines
A = 90 - 100%
B = 80 - 89%
C = 70 - 79%
D = 60 - 69%
F = 0 - 59%