1.
Syllabus
2.
Install Guides
1.
R and R Studio
2.
git
3.
Github
4.
DB rowser
5.
OpenRefine
6.
R Packages
7.
[WIN ONLY] Windows Subsystem for Linux
3.
Worksheets
9/14.
R/R studio
9/19.
git
9/21.
Exploratory data analyses
9/26.
Tidy Data
9/28.
Aggregation and Merging
10/3.
Advanced Plotting
10/5.
Dynamic Plotting
10/12.
Data Science Ethics
10/17.
Functions
10/19.
Debugging & Conditions
10/24.
Iteration
10/26.
List and Apply
10/31.
Bash
11/2.
Advanced git
11/7.
Data Cleaning
11/14.
Web Scraping
11/16.
APIs
12/2.
Text as Data
12/2.
Networks as Data
12/5.
Geospatial Data
4.
Labs
5.
Projects
Project 1.
Smith College Museum of Art
Project 2.
Open Payments
Project 3.
Finals
6.
Slides
More
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Credits
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Intro to Data Science
> Slides
Slides
Lecture Slides
01_1_intro.html
(12 MB)
01_2_what_is_data.html
(21 MB)
02_2_intro_r.html
(8 MB)
03_1_git.html
(4 MB)
03_2_eda.html
(3 MB)
04_02_agg_merge.html
(6 MB)
04_1_tidy.html
(5 MB)
05_1_adv_plot.html
(23 MB)
05_2_dyn_plot.html
(26 MB)
06_1_ethics.html
(5 MB)
06_2_project_1.html
(4 MB)
07_1_functions.html
(3 MB)
07_2_conditions_debugging.html
(6 MB)
08_1_iteration.html
(3 MB)
08_2_list_apply.html
(5 MB)
09_1_bash.html
(6 MB)
09_2_adv_git.html
(3 MB)
10_1_cleaning.html
(4 MB)
11_1_web_scrape.html
(6 MB)
11_2_apis.html
(5 MB)
13_1_finals.html
(5 MB)
13_2_text.html
(9 MB)
13_3_networks.html
(9 MB)
14_1_geospatial.html
(4 MB)