QF104 Data Management in R
Course Catalog Description
Introduction
The course provides an introduction to R, with a focus on how to use R to manage data through the data life cycle.
Campus | Fall | Spring | Summer |
---|---|---|---|
On Campus | X | X | |
Web Campus | X | X |
Instructors
Professor | Office | |
---|---|---|
Mohamad Afkhami | mafkhami@stevens.edu | |
Olorundamilola `Dami' Kazeem
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okazeem@stevens.edu | Babbio 109 |
More Information
Course Outcomes
After successful completion of this course, students will have solid foundation in R and on how to use R to:
- import data
- tidy data
- transform data
- visualize data
- model data
- communicate data
Additionally, students will be able to apply R, which is one of many tools, to solve data-related problems they may encounter beyond this course.
Course Resources
Textbook
G. Grolemund and H. Wickham,\R for Data Science", https://r4ds.had.co.nz/
Additional Resources
W. N. Venables, D. M. Smith, and the R Core Team,\An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics, Version 3.5.2, 2018-12-20", https://cran.r-project.org/
The R Project for Statistical Computing, https://www.r-project.org/
R-Bloggers,", https://www.r-bloggers.com/
Grading
Course Requirements
Homework Assignments & Final Project: Students are required to submit their respective homework assignments via Canvas. No other form of submission will be accepted.
Attendance & Participation: Students are required to attend all lectures to complete the 12 In-Class Drills and 2 Pop-Up Quizzes. Students are required to submit their respective homework assignments via Canvas. No other form of submission will be accepted.
Grading Policies
Grading will be based upon your attendance, in-class drills, homework assignments, pop-up quizzes, and final project. The percentage breakout of these components is indicated below. An average score of 60% or above will indicate a passing grade, and below 60% will be a failing grade.
5 Homework Assignments - 50%
1 Final Project - 20%
12 In-Class Drills - 20%
2 Pop-Up Quizzes - 10%
Late Policy: All late submissions will be punished unless prior notice is given before the due date and it is approved. If your submission passes the due date for less than 24 hours, your highest score will be 67 %; between 24 and 48 hours, your highest score will be 33 %; and after 48 hours your submission will not be accepted. If outside circumstances are aecting your ability to perform in the course, you must contact the instructor before you fall behind in the course.
Lecture Outline
Week | Topic | Reading |
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1 |
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2 |
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3 | Data Visualization - r4ds | Ch. 3 Sec. 1-4 and 7 |
4 | Data Transformation - r4ds | Ch. 5 1-4 |
5 | Data Transformation - r4ds | Ch. 5 5-7 |
6 | Data Import - Part I | |
7 | Data Import - Part II | |
8 | Tidying Data | |
9 | See You After the Break! | |
10 |
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11 | Date and times | |
12 | Writing Functions - Part I | |
13 | No Class | |
14 | Writing Functions - Part II | |
15 |
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16 | Review | |
17 | No Class | |
18 | No Class | |
19 | Correlation and Regression | |
20 | Lab Tutorial | |
21 | Lab Assignment 2 | |
22 | Class Exam 2 | |
23 | Project Data Analysis | |
24 | Project Data Analysis | |
25 | Final Presentations | |
26 | Final Presentations |