The “Reading” column in the table below contains a number on which it refers to a numbered item in the Books & Online Resources List. For example “[1]” refers to the first item in the list, which is our main text book titled “Modern Data Science with R”.
Week | Day | Topic | Reading |
---|---|---|---|
1 | - | Introduction | - |
1/25 | Why Data Science? | Syllabus & [1] Ch. 1 | |
1/27 | Data Science Principles | [27] | |
2 | - | Programming Basics | - |
2/1 | R Basics Review | [1] B & [12] | |
2/3 | Reproducible Workflow | [1] D & [13] | |
3 | - | Data Wrangling | - |
2/8 | Taming Tables | [1] Ch. 4-5 | |
2/10 | Tidy Data and Data Reshaping | [1] Ch. 6-7 | |
Module 1 Assignment Due 2/11 | |||
4 | - | Advanced Visualizations Part 1/2 | - |
2/15 | Why Data Visualization? | [1] Ch. 2 | |
2/17 | Grammar for Graphics | [1] Ch. 3 | |
5 | - | Advanced Visualizations Part 2/2 | - |
2/22 | Scaling and Color Choices | [1] Ch. 8.1-8.2 | |
2/24 | Animation & Interactivity | [1] Ch. 14.1-14.2, [17], & [18] | |
Module 2 Assignment Due 2/25 | |||
6 | - | Spatial Data | - |
3/1 | What is spatial data? | [1] Ch. 17.1-17.3 | |
3/3 | Visualizing and Analyzing Spatial Data | [1] Ch. 17.4-17.7 | |
7 | - | Text Data | - |
3/8 | What is text data? | [1] Ch. 19.1, [19], [20], & [28] | |
3/10 | Visualizing and Analyzing Text Data | [1] Ch. 19.2.1-4, 19.3 - 19.4 | |
8 | - | Network Data | - |
3/15 | What are networks? | [1] Ch. 20.1 [21], & [22] | |
3/17 | Visualizing and Analyzing Network Data | [1] Ch. 20.2 - 20.4 | |
Module 3 Assignment Due 3/18 | |||
9 | - | Spring Break! | - |
10 | - | Mathematical and Statistical Modeling | - |
3/29 | Class Cancelled | - | |
3/31 | A Review on Data Wrangling & Advanced Visualization | - | |
11 | - | Understanding Spatio-Temporal Data Part 1/2 | - |
4/5 | Basic Population Models | [10] Ch. 1-2.3 | |
4/7 | Modeling Ecosystems & Evolution utilizing Visualization | [10] Ch. 2.4 | |
12 | - | Understanding Spatio-Temporal Data Part 2/2 | - |
4/12 | Basic Susceptible-Infected-Recovered (SIR) Models | [11] Pg. 1-31 | |
4/14 | Class Cancelled | - | |
13 | - | Understanding Categorized Data | - |
4/19 | Non-Regression Classifiers | [1] Ch. 11.1 - 11.2 | |
4/21 | Visualizing & Exploring Data with Categorical Variables | [1] Ch. 11.3 - 11.5 | |
14 | - | Project Period | - |
4/26 | Class Cancelled | - | |
4/28 | Final Project Reports Example | - | |
Module 5 Assignment Due 4/29 | |||
15 | - | Recovery Period | - |
16 | 5/9 | Final Projects and Peer Reviews Posted | - |
[2] Walker, K. (2022). Analyzing US Census Data: Methods, Maps, and Models in R. CRC Press.
[5] Kuhn, M., & Silge, J. (2020). Tidy Modeling with R.
[6] Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. Chapman and Hall/CRC.
[7] Diez, D. M., Barr, C. D., & C etinkaya-Rundel, M. (2019). OpenIntro statistics.
[8] Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach. " O’Reilly Media, Inc.".
[11] Martcheva. (2015). An introduction to mathematical epidemiology (1st ed. 2015.). Springer.
[12] R for beginners
[14] R ggplot2
[15] R ggraph
[16] R gganimate
[18] How to create plots with beautiful animation in R?
[20] Text mining in R with tidytext
[21] Introduction to tidygraph
& ggraph
[22] Announcing ggraph
: A grammar of graphics for relational data
[23] Python for beginners
[24] Python seaborn
[25] Python Anaconda
[26] Jupyter Notebook for beginners
These lists of books can be purchased at a bookstore or can be loaned - some of them - at the Reed College Library for free using your Reed College ID.