P4A - Fall 2020
Syllabus
Schedule
Lessons
0: Course Prep
Week 1.1: Course Introduction
Week 1.2: Getting Started
Week 1.3: Operations & Data Types
Week 2: Functions & Packages
Week 3: Creating functions
Week 4.1: Conditionals
Week 4.2: Testing & Debugging
Week 5: Loops
Week 6: Vectors
Week 7: Strings
Week 9: Introduction to Python
Week 11-1: Data Analysis Prelude
Week 11-2: Data Frames
Week 12: Data Wrangling
Week 13: Data Visualization
Week 14: Reproducible Reporting
Week 15: Monte Carlo Methods
Assignments
HW 1 - Getting Started
HW 2 - Functions & Packages
HW 3 - Creating Functions
HW 4 - Conditionals & Testing
HW 5 - Loops
HW 6 - Vectors
HW 7 - Strings
HW 8 - Python
HW 9 - Data Frames
HW 10 - Data Wrangling
HW 11 - Data Visualization
HW 12 - Reproducible Reporting
References
Getting Help
Programming in R
Visualizing Data
R Markdown
Other
Tools
Autograder
Slack
RStudio Server
Schedule a meeting w/Prof. Helveston
About
License
Contact
Source files
Programming in R
Programming in R
Last semester’s 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
]
Beginning Computer Science with R
, by Homer White
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
RMarkdown
Andrew Heiss’s Markdown guide
60 second markdown guide
10 minute markdown tutorial
Markdown It
: Quickly demo Markdown code.
Table generator
: Create tables and get export code for multiple formats.
CMU RMarkdown guide (very detailed)
RStudio “Cheatsheets”
All cheatsheets
Data wrangling with the
dplyr
library
Data visualization with the
ggplot2
library
RMarkdown
EMSE 4574: Programming for Analytics (Fall 2020) |
Tuesdays |
12:45 - 3:15 PM |
Dr. John Paul Helveston
|
jph@gwu.edu
Content
2020
John Paul Helveston.
See the
licensing
page for details.