This course is an application-driven introduction to data science. Statistical and computational tools are valued throughout the modern workplace from Silicon Valley startups, to marine biology labs, to Wall Street firms. These tools require technical skills such as programming and statistics. They also require professional skills such as communication, teamwork, problem solving, and critical thinking.
Instructor: Robin Cunningham
Instructional Assistant: Adam Waterbury
Graduate Research Consultant: Varun Goel
See the course syllabus for more information.
| Date | Lecture | Slides | Notes & HW |
|---|---|---|---|
| August 23 | Course Overview, Install R | Lecture 1 | R Downloads |
| August 25 | Visualization and ggplot2 | Lecture 2 | Homework 1 |
| August 28 | ggplot Wrap-up and Workflow Basics | Lecture 3 | Reading for 8-28 |
| August 30 | Data Transformations with Dplyr | Lecture 4 | Homework for 8-30 |
| Sept. 1 | Data Transformations with Dplyr | Class Activity | Homework for Sept 1 |
| Sept. 6 | Data Transformations with Dplyr | Still Lecture 4 | Homework for Sept 6 |
| Sept. 8 | Workflow - Scripts, RMarkdown | Lecture 5 | Data Analysis 1 |
| Sept. 11 | Exploratory Data Analysis | Lecture 6 | |
| Sept. 13 | Exploratory Data Analysis | Lecture 7 | Homework |
| Sept. 15 | Tibbles and R Projects | Class Activity | Activity as .rmd |
| Sept. 18 | Importing Data | Lecture 9 | heights.csv |
| Sept. 20 | Tidy Data | Lecture 10 | MOMA_art.csv MOMA_artists.csv |
| Sept. 22 | Tidy Data –> Joins | Lecture 11 | Joins Homework |
| Sept. 25 | Tidy Data - Wrap-up | Lecture 10 Redux | HW Due Weds. Script last class |
| Sept. 27 | Owning Strings | Notes | Slides_______ HW for Friday |
| Sept. 29 | Owning Strings | Slides as Rmd | Data Analysis 2 |
| Oct. 2 | Factors | Slides Lecture 12 | |
| Oct. 4 | Programming and Vectors | Notes L13 | Slides |
| Oct. 6 | Programming Lab | Lab | |
| Oct. 9 | Lists and Loops | Slides | HW |
| Oct. 11 | Lists and Loops | Slides | Loops HW |
| Oct. 13 | Intro to Models | Slides | Reading |
| Oct. 16 | Intro to Models | Slides | HW Due 10-23 |
| Oct. 23 | Intro to Shiny | Shiny Notes Shiny Rmd | Apps for Class |
| Oct. 25 | Data Ethics | Lecture | data privacy _____NYT Uber Oneill Big Data |
| Oct. 27 | Visualizing Models | Lecture | Final Proj Gps Final Project |
| Oct. 30 | Visualizing Models | Lecture | |
| Nov. 01 | Interaction | Commands | Homework |
| Nov. 03 | Interaction (ctd) | Lecture | |
| Nov. 06 | Modeling Real Data | Activity | |
| Nov. 10 | Predictive Modeling | Lecture | Project Exp An Data Analysis 3 |
| Nov. 13 | Get an A on the Project | Lecture | Lecture as ppt |
| Nov. 15 | Classification | Slides | Everything |
| Nov. 20 | Donner Destiny | Slides | Classification Folder Classification Activity |
| Dec. 4 | Project Presentations and Donners | Surveys | Donner_Modified |
| Dec. 4 | Project Presentations and Donners | Surveys for Exam Day |
This course was made possible by a grant from the Data@Carolina initiative and a ton of input from lots of very smart people.
This page was last updated on 2017-12-15 15:19:49 Eastern Time.