Schedule

DATA 101, Spring 2018
Kris Shaffer, Ph.D., University of Mary Washington.

Overview

Beginning in Week 2, every week (except for midterm presentations in Week 7) will follow the same general schedule:

• Due at the beginning of class Tuesday: completion of a single DataCamp "Course" (typically 3-4 hours of work).
• During Tuesday's class: Inspire: looking at a noteworthy data science project in the "real world" that can inspire our own work; go over questions and problems that arose during the DataCamp course of the week and the previous week's lab assignment; begin working on the lab assignment of the week collaboratively (pair coding).
• During Thursday's class: Go over bigger questions that arose during Tuesday's work and continue working collaboratively on the lab assignment of the week; introduce the following week's topic of study.
• Due at the beginning of class the following Tuesday (in Canvas): Completed lab assignment, self-evaluation (if assigned).

Note: the weekly lab assignments are begun (and mostly completed) in pairs in class, where you can receive help from your colleagues and from me. However, any work completed outside of class time must be done individually and must be your own work, in accordance with the UMW honor code.

Also note: if you have a laptop that you can bring to class, please do so every day.

The DataCamp courses will take us through the Data Analyst with Python career track at DataCamp. In addition to UMW credit, you will also receive a certificate from DataCamp upon completion of those materials, which you can highlight on your resume, your website, you LinkedIn profile, etc., should you find it beneficial.

You will find information about all DataCamp courses we'll be working on together on our DataCamp course page.

Weekly schedule

Week 1: Introductions

DataCamp course: Introduction to Python for Data Science (complete Chapters 1 & 2 before Thursday's class)

Inspire: Hamilton68 dashboard - tracking Russian influence operations on Twitter

Lab assignment: Getting up and running with Anaconda

Week 2: Intermediate Python for Data Science

DataCamp course: Intermediate Python for Data Science (complete entire course before Tuesday's class)

Inspire: She Giggles, He Gallops - Analyzing gender tropes in film with screen direction from 2,000 scripts, Julia Silge

Lab assignment: Exploratory Data Analysis with the National Immunization Survey (due in Canvas by Tuesday, Jan 30, 11am)

Week 3: Python Data Science Toolbox (Part 1)

DataCamp course: Python Data Science Toolbox (Part 1)

Inspire: The Follower Factory - Interactive article on purchased followers, likes, and favorites on Twitter
Checkout this Jupyter notebook for example code to reproduce a similar study.

Lab assignment: The Ramen Rater (due in Canvas by Tuesday, Feb 6, 11am)

Self-assessment 1

Week 4: Importing Data in Python (Part 1)

DataCamp course: Importing Data in Python (Part 1)

Inspire: Face2Face - Synthesizing facial expressions in real time.

Lab assignment: The Million-Song Dataset (due in Canvas by Tuesday, Feb 13, 11am)

Week 5: Importing Data in Python (Part 2)

DataCamp course: Importing Data in Python (Part 2)

Inspire: Data.gov - free, public datasets.

Lab assignment: Scraping data from the web (due in Canvas by Tuesday, Feb 20, 11am)

Week 6: Cleaning Data in Python

DataCamp course: Cleaning Data in Python

Lab assignment: Midterm project (to present next week)

Week 7: Midterm Presentations

See project details

Self-assessment 2

SPRING BREAK

Week 8: pandas Foundations, Part 1

DataCamp course: pandas Foundations

Lab assignment: Tweet Mining

Week 9: pandas Foundations, Part 2

DataCamp course: pandas Foundations (continued, though you may want to start working ahead on Week 10's DataCamp course)

Lab assignment: Tweet Mining (continued)

Week 10: Manipulating DataFrames with pandas

DataCamp course: Manipulating DataFrames with pandas (due March 27)

Lab assignment: UFO Sightings (due April 3)

Week 11: Merging DataFrames with pandas

DataCamp course: Merging DataFrames with pandas (due April 3)

Lab assignment: 2016 US Election Analysis (due April 17)

Self-assessment 3 (due April 10)

Week 12: Introduction to Data Visualization with Python

DataCamp course: Introduction to Data Visualization with Python (CHAPTER 1 ONLY — "Customizing Plots") and
Statistical Thinking in Python, Part 1 (CHAPTER 1 ONLY — "Graphical Exploratory Data Analysis")

Lab assignment: 2016 US Election Analysis, cont. (due April 17)

Week 13: Statistical Thinking in Python (Part 1)

DataCamp course: Statistical Thinking in Python (Part 1, Chapters 2–4) (due April 17)

Lab assignment: begin work on your final project.

Week 14: Statistical Thinking in Python (Part 2)

DataCamp course: Statistical Thinking in Python (Part 2) (due April 24)

Lab assignment: work on your final project.

FINAL PROJECT

Due Thursday, May 3, noon. Details here.

Self-assessment 4, due Thursday, May 3, noon.

There is no in-person final exam.