** Prerequisites
*** { @unit = “Prior to April 1st”, @title = “Why Learn R”, @reading, @foldout }
Data programming languages are different from traditional programming languages. They were designed to make data analytics fast and powerful. R and Python are two of the primary tools of the data science communities.
This course focuses on R and Markdown (not quite a language, but a formatting convention for data-driven documents). Python and Notebooks are similar tools that you will encounter in this space, and they are somewhat interchangeable. If you start out as a computer scientist or engineer and evolve into working in the data science space you will likely use Python. If you start as as a social scientists or a statistician and venture into the data science space chances are you are using R. The differences between the two is more cultural than practical.
*** { @unit = “”, @title = “Installation”, @reading, @foldout }
Go to r-project.org to find instructions for downloading R. R is necessary for your computer to operate R Studio.
R Studio is an integrated development environment (IDE) for R which we’ll be using in the class. Once R is downloaded download R Studio. Both are free.
*** { @unit = “”, @title = “Getting Started with R”, @reading, @foldout }
If this is your first time working with R, we recommend spending 4 hours to work through some of these examples to become familiar with the syntax and basic data structures:
** Section-I Course Overview
*** { @unit = “1st April”, @title = “Intro to Data-Driven Management and Policy”, @reading, @lecture, @assignment, @foldout }
Textbook: Chapter 1
R Markdown: The bigger picture - Garrett Grolemund
** Section-II Data Programming
*** { @unit = “8th April”, @title = “R Basics”, @reading, @lecture, @assignment, @foldout }
Getting Started on Quantified Life
TED - What to Do With Big Data
Abundant Data by Itself Solves Nothing
Big Data: The Management Revolution
Building a Learning Organization
*** { @unit = “15th April”, @title = “Data Structures in R”, @reading, @lecture, @assignment, @foldout }
Textbook: Chapter 2-3
*** { @unit = “22th April”, @title = “Working with Data Frames”, @reading, @lecture, @assignment, @foldout }
Textbook: Chapter 4-8
*** { @unit = “29th April”, @title = “Intro to Visualization”, @reading, @lecture, @assignment, @foldout }
Textbook: Chapter 9-11
*** { @unit = “6th May”, @title = “Spatial and Multidimensional visualization”, @reading, @lecture, @assignment, @foldout }
** Section-III Dashboards
*** { @unit = “13th May”, @title = “Dynamic visualization”, @reading, @lecture, @assignment, @foldout }
*** { @unit = “20th May”, @title = “Analyzing Managerial Experiments”, @reading, @lecture, @assignment, @foldout }
*** { @unit = “27th May”, @title = “MEMORIAL DAY” }
Instructions will be given on what to do for this week.
*** { @unit = “3rd June”, @title = “Dashboards II”, @reading, @lecture, @assignment, @foldout }
[Building Shiny Apps Guide](https://github.com/DS4PS/ddmp-uw-class-spring-2019/blob/master/Reading/Building%20Shiny%20apps%20-%20an%20interactive%20tutorial.pdf
Those notes, by virtue of being printed to add to the website, will not be dynamic. If you want to experiment with the sliders and other features, copy the code into R and run with the shiny package.
The lecture was uploaded to YouTube. This is my first time doing that, so please let me know if there are any technical issues.
*** { @unit = “11th June”, @title = “FINAL PROJECTS DUE”, @assignment, @foldout }
There are two components of the final project, both of which must be completed seperately: the data repository and the dashboard. Instructions for both are below.
Instructions for Data Repository