There are two ways to progress through this material, depending upon how much time you have to devote to it each week.
- If you are teaching graduate students or are self-paced with enough time to devote, I’d recommend adopting the schedule below, which is designed for an intense but doable semester-long course. It is intended to take the average graduate student roughly 10 hours per week to complete all required tasks. However, some number of students will find programming to be more challenging and may take up to 15 hours per week. Some will breeze through the material in 5.
- If you are teaching undergraduate students or are self-paced with less free time, you can take a more relaxed pace by alternating weeks: in the first week in each pair, complete the DataCamp materials, and in the second week, complete the project. If you are teaching undergraduates for a single semester, I suggest taking this approach but skipping modules 6 and 10-13.
You can download a sample graduate-level syllabus here, which I used when I taught this course in Fall 2017.
If you are designing your own data science course using these materials, it is recommended that for each week of this course, you strive for students to accomplish four objectives:
- Complete the assigned DataCamp modules.
- Listen to the lecture materials tying DataCamp modules to social scientific applications and highlighting critical new skills.
- Complete the practical project.
- Participate in a debriefing explaining how DataCamp and the lecture materials link together to write the code to address project requirements. A sample debriefing is provided for self-paced or asynchronous online courses, but this is best done live if possible.
Weeks 1 – 6 are fundamental R programming. Weeks 7 – 9 are traditional social scientific statistical analyses and visualization. Week 10 – 14 are new data science skills.
Additional videos will be released 4-at-a-time through Spring 2018 until the complete course is available.
You can view video content by either watching the entire set of videos currently available or by clicking on individual video links below. Be sure to watch in high definition (HD; 720p or 1080p) to see text clearly.
|Module||Topic||DataCamp and Readings||Lecture||Project||Debriefing|
|1||Introduction and Software||Reading: You Say Data, I Say System||None||None|
|2||Data Types and Basic Variable Manipulation|
|3||Conditionals, Loops, and Apply||Course: Intermediate R
Course: Intermediate R – PracticeReading: Using apply, sapply, lapply in R
|4||Data Import and Formatting||Course: Importing Data in R, Part 1 modules:
|5||Data Manipulation||Course: Data Manipulation in R with dplyr
Course: Joining Data in R with dplyr modules:
|6||String Manipulation||Course: String Manipulation with stringr
Course: RegexOne (not DataCamp)Reading: Demystifying RegEx
|7||Data Visualization||Courses: Data Visualization with ggplot2:|
|8||Analysis of Variance|
|9||General and Generalized Linear Model||Coming Mar 15|
|10||Generating Reports and Web Apps||Coming Mar 15|
|11||Web Scraping and APIs||Coming Mar 15|
|12||Machine Learning|| See
|See Project 13|
|13||Natural Language Processing||
Course: Sentiment Analysis in R: The Tidy Way module:
|Coming Mar 15|