Stat 470/670: Exploratory Data Analysis

Logistics

Meetings: Tuesdays and Thursdays, 4-5:15pm, Fine Arts 102

Website: http://jfukuyama.github.io/teaching/stat670

Instructor: Prof. Julia Fukuyama		jfukuyam at iu dot edu
Office hours: Wednesday 1-3pm		Office: Informatics East, Room 201

Associate Instructor: Ms. Fatma Parlak		fparlak at iu dot edu
Office hours: Thursday 9-11am		Office: Informatics East, Room 103

Course Overview

Graphical and modeling techniques for exploring data, with an emphasis on visualization, interpretation, and clear communication of findings. Use of modern software tools for data manipulation and visualization. Connections to traditional statistical methods.

Textbooks

The primary textbook for the course will be Cleveland’s Visualizing Data.

Also useful will be Hadley Wickham’s ggplot2: Elegant Graphics for Data Analysis, available for free through the IU library as an ebook, and R for Data Science by Wickham and Grolemund, available online.

Readings and notes for topics not covered in the textbooks will be posted to the course website and to canvas.

Class Structure

Classes will be a combination of lecture and tool demonstration. It will generally be helpful for you to bring a laptop with R installed so you can follow along. Slides or notes, with R code, will be posted to the class website the day before each lecture.

Assessment

Grades will be assigned based on:

Homeworks, 25% of the grade. These will be short, assigned most weeks, and due Fridays at 5pm. Submitted via canvas.
Mini projects, two, together worth 20% of the grade. These will involve more substantial data analysis and a writeup. Done in groups of up to three.
In-class midterm, November 15, 15% of the grade. Note that this is the Thursday before Thanksgiving break.
Final project and presentation, 40% of the grade. The components are a proposal, due November 9, final presentations in the last week of classes, and a final report due December 7. Done in groups of up to three.

There will be no final exam; the last responsibility for the course will be the report for the final project due December 7.

All the assignments will be graded on how well the material is presented in addition to accuracy. This means there should be no extraneous material, plots should be readable, and text and figures should be formatted nicely.