Syllabus
Schedule
Class
Jan 13: Getting Started
Jan 20: Functions & Packages
Jan 27: Creating Functions
Feb 03: Conditionals & Testing
Feb 10: Iteration
Feb 17: Vectors
Feb 24: Strings
Feb 28: Midterm Review
Mar 03: Python in R
Mar 24: Data Frames
Mar 31: Data Wrangling
Apr 07: Data Visualization
Apr 14: Reproducible Reporting
Apr 21: Monte Carlo Methods
Assignments
HW 1: Getting Started (Due 01/19)
HW 2: Functions & Packages (Due 01/26)
HW 3: Creating Functions (Due 02/02)
HW 4: Conditionals & Testing (Due 02/09)
HW 5: Iteration (Due 02/16)
HW 6: Vectors (Due 02/23)
HW 7: Strings (Due 03/02)
HW 8: Python (Due 03/09)
HW 9: Data Frames (Due 03/30)
HW 10: Data Wrangling (Due 04/06)
HW 11: Data Visualization (Due 04/13)
HW 12: Reproducible Reporting (Due 04/20)
Readings
1.1: Getting Started
1.2: Operations & Data Types
2: Functions & Packages
3: Creating functions
4.1: Conditionals
4.2: Testing & Debugging
5: Iteration
6: Vectors
7: Strings
8: Introduction to Python
9.1: Data Analysis Prelude
9.2: Data Frames
10: Data Wrangling
11: Data Visualization
12: Reproducible Reporting
13: Monte Carlo Methods
References
Course Software
Visualizing Data
Programming in R
R Markdown
Other
Help
Slack
Autograder
Schedule a meeting w/Prof. Helveston
About
License
Contact
Source files
Programming in R
Programming in R
Programming for Analytics course website
Grolemund, Garrett. “Hands-On Programming with R” [
free online
], [
buy on amazon
]
Peng, Roger D. “R Programming for Data Science” [
online - pay what you want
]
Data Analysis in R
Grolemund, Garrett and Wickham, Hadley. “R for Data Science” [
free online
], [
buy on amazon
]
Peng, Roger D. “Exploratory Data Analysis with R” [
online - pay what you want
]
16 HOWTO’s
, by Lingyun Zhang
RStudio “Cheatsheets”
All cheatsheets
Data wrangling with the
dplyr
library
Data visualization with the
ggplot2
library
RMarkdown
EMSE 4571: Intro to Programming for Analytics (Spring 2022)
Thursdays |
12:45 - 3:15 PM EST |
Tompkins 208
|
Dr. John Paul Helveston
|
jph@gwu.edu
LICENSE: CC-BY-SA