Classes

1 Data Analytics for Education

1.1 Description

This program is built in stages, starting with developing an efficient workflow, proceeding to analytical techniques, and culminating with principles of data leadership. The goal is to enable you to transform your office into a proactive data leadership role.

1.2 Stages

The Basics
First you need to know how to get out of the grind of endlessly producing reports in Excel. The first courses are designed to allow offices or individuals to efficiently manage data projects via a reproducible, transparent, and automated workflow. The key idea is to convert spreadsheet work to script-writing, unlocking a “do it once” workflow that frees analysts to focus on improvements, automation, and more in-depth analysis instead of rote tasks. The written instructions of the script are created in an intuitive language to precisely describe operations like selecting, filtering, renaming, grouping, and summarizing rows and columns of data—all using free statistical software.
Data Analysis and Presentation
The second stage is to do interesting things with the data and present the results in a way to make them attract attention, while still being easy to maintain. This track includes the creation of graphs and tables, creation of smart documents, and introduction to data modeling with regression.
Data Leadership
With the foundation accomplished, the most powerful tools for changing data culture are unlocked. These are partly technical, like how to build a data warehouse to pull standardized data from, and how to be a good partner to operational units around campus, like financial aid, admissions, and the registrar. Using the automation and reporting tools you learned, it’s now possible to raise the profile of the IR office and become proactive at finding new kinds of data to use, where the reporting pain points are, creating validation streams, creating dashboards from custom data, and how to engage with campus leaders so that they gladly take your calls.

1.3 Course Structure

  • We will not have any physically face-to-face sessions; everything will be synchronous (“live”) online
  • Two live synchronous video sessions — one 90-minute and one 60-minute
  • One session will be dedicated to introducing ideas via demonstration and lecture
  • The other session will exclusively be dedicated to in-class problem-solving and Q&A for material that has already been introduced
  • On your own time after each synchronous session (at least five hours per week), complete lessons and homework
  • The instructors will be available via email and course messages to answer questions related to the material

1.4 Learning objectives for the program

The students will be able to:

  1. Demonstrate how generating reports with R scripting is superior to the use of spreadsheets in many instances
  2. Apply a unifying high-level process needed to analyze data using R in all application areas
  3. Create scripts that can generate professional-looking reports that they can use in their work containing descriptive text, numerical tables, summary statistics, and explanatory graphs
  4. Write a plan for future automated reports in R that would help them work more efficiently and increase their value as an education professional to their institution

1.5 Technology for the program

  • Web browser used to access the class site
  • Software installed on the student’s computer
    • R (open source statistical software from this Web site)
    • RStudio (an integrated development environment from Posit — which already includes Quarto, a technical publishing system)
  • Zoom software for Live sessions (recorded sessions will be available)

1.6 Classes

1.6.1 Data Analysis for Education 101: R Scripting

Dates
  • Beginning 03-Feb-2025 thru 17-Mar-2025
  • Beginning 28-May-2025 thru 2-Jul-2025
  • Fall 2025 (tbd)
Course Information and Registration page
Description
Students, who are assumed to have experience manipulating spreadsheet data but no experience working with R (or databases in general), are introduced to the skills needed both to manipulate large sets of data and to extract information from them. They will be introduced to the software programs that we use in the class: R (the statistics program) and R Studio (the development environment).

Each week students will be introduced to a topic, work through group exercises during class, have work through more advanced exercises during a live problem session, and will work on a course-long project. All of this will be in preparation for defining and completing a personal project with the student’s own data.

General schedule
  • Preparation: Install R, RStudio and some R packages
  • Week 1: Basics of data manipulation (data types, pipe, select, filter, and mutate)
  • Week 2: Pivot tables (pivoting, groups, functions)
  • Week 3: Data joins (joins, IPEDS data)
  • Week 4: Putting it all together (for loop, Excel for output)
  • Week 5: Project presentations
Class website
  • Web site (for the February 2025 offering)

1.6.2 Data Analysis for Education 102: Data Visualization

Dates
  • Beginning 29-May-2025 thru 1-Jul-2025
  • Fall 2025 (tbd)
Description
Students, who are not assumed to have experience manipulating data in R, are introduced to the skills needed to create simple graphs for data exploration and more complicated graphs for reports or presentations.

Each week, students will be introduced to a topic, work through examples during class, work through introductory lessons outside of class, and work through more advanced exercises during a live problem session. All of this will be in preparation for defining and completing a personal project with the student’s data.

General schedule
  • Preparation: Install R, RStudio and some R packages
  • Week 1: Introduction to graphs
  • Week 2: More advanced graphs
  • Week 3: Generating multiple graphs of one type
  • Week 4: Combining multiple plot types into one graph
  • Week 5: Project presentations

1.6.3 Data Analysis for Education 103: Reporting

Dates
  • Beginning in Fall 2025 (tbd)
Description
Students, who are assumed to have experience manipulating data in R (as covered in the 101 course) and creating graphs using the tidyverse in R (as covered in the 102 course), are introduced to the skills needed to create attractive, well-formatted technical reports that easily incorporate tabular reports and graphs. The beauty of this approach to organizing your work will become evident when the student is able to define a monthly report once and then use it over-and-over again with little effort.

Each week students will be introduced to a topic, work through group exercises during class, have work through more advanced exercises during a live problem session, and will work on a course-long project. All of this will be in preparation for defining and completing a personal project with the student’s own data.

2 Other data-focused classes

2.1 Tableau for Greenville Non-profits (Oct-2024)

In this four-week class, Scott introduced a group of data professionals from Greenville-based educational non-profits to Tableau. They learned how to use this software to build dashboards that support decisions that drive business value. See the class Web site.