Telling Stories with Data - statistics, modeling, and visualization

Homework

Homework assignments will be posted here, in general organized by due date. Unless otherwise specified, parts of homework assignments that need to be handed in should be handed in via your personal Google Drive folder that only you and the instructor have access to. Unless specified below, the deadline for completing homework is before the beginning of class on the due date.

Collaboration on homework is expected and encouraged, although you must write up your own assignment. No copying or cutting and pasting.

Due Thursday 3/31, 5pm

Due Thursday 3/10, 5pm

Due Tues 3/1

  • Project 1: final reports due in class, also, 5-10 minute in-class presentation for each report.

Due Thursday 2/25, 5pm

  • Read OpenIntro Chapter 7.
  • Lab 2. You should hand in an html created with RMarkdown file that shows your code as well as has your written responses to the exercises.

Due Tues 2/23, 5pm

  • draft of the follow-up Project 1 report due at 5pm.

Due Thursday 2/18

  • Read Kaplan Chapter 8.
  • Brief presentations (<5 minutes per team) from Project 1.

Due Thursday 2/11

  • Read Kaplan Chapters 6 and 7.

Due Tuesday 2/9

  • Dataset proposals for Project 1 due.
  • Complete the four modules from the Getting and Cleaning Data course in swirl. You will likely need to install this course by running the commands:
 library(swirl)
 install_from_swirl("Getting and Cleaning Data")

Due Thursday 2/4

  • Use tableplot and the NHANES dataset to create a multivariate data visualization. Post it on Piazza prior to class on Thursday, with a few sentence description of what story you think the graphic is telling.

Due Sunday 1/31 and Tuesday, 2/2

  • Please select a blog entry that you’d like to present (briefly) on Tuesday, February 2nd. The entry should be from 2014 or later and relate to statistics, data science, or R. Please submit your choice on Piazza by midnight on Sunday, January 31st. The presentations will be no more than 90 seconds long and should provide a brief summary of the entry, why you found it interesting, and what question you have after reading it. As a start, check out these blogs for lots of entries that may be of interest:

Due Thursday, 1/28

  • Complete the swirl R Programming Alt course, modules 8, 11 and 12:
    1. Functions
    2. Looking at Data
    3. Simulation
  • Complete the swirl Exploratory Data Analysis course, modules 1-3 (also, modules 5-10 are recommended for extra practice/skill development but not required):
    1. Principles_of_Analytic_Graphs
    2. Exploratory_Graphs
    3. Graphics_Devices_in_R

Due Tuesday, 1/26

  • Read Kaplan Chapter 1.
  • Read articles from Data Visualization reading list.
  • Each group should submit their iPod shuffle lab write-up on Piazza that answers the following questions: What rules did you apply to Mr. Hoffman’s playlists? Did you find evidence to support the claim that Mr. Hoffman’s playlists are not random?
  • Complete the swirl R Programming Alt course, modules 4-7:
    1. Missing_Values
    2. Subsetting_Vectors
    3. Matrices_and_Data_Frames
    4. Logic

Due Thursday, 1/21

  • Read syllabus
  • Install R, RStudio
  • register on Piazza site, complete pre-semester survey
  • Complete the swirl R Programming Alt course, modules 1-3:
    1. Basic_Building_Blocks
    2. Sequences_of_Numbers
    3. Vectors