Learning Modules
We offer online and on-site training for your organization or enterprise. Whether you are a large company or small research group that would like to enhance your data science and research skills, we can custom design a training program for you - delivered virtually or in-person. See our list of learning modules offered below, and if there is a topic you would like training in but don't see on our menu, please let us know!
Zero to Hero in R
99% of people who quit trying to learn data science give up in the first few hours; the rest give up in the first few weeks. This module, designed for participants who have never used R or any other programming language, will jump-start their data science career by guiding them through a series of firsts: their first line of code, first script, first data table, first plot, and first project. Participants leave with the skills and the orientation they need to not just survive in data science, but thrive.
Data Visualization in Practice
This workshop puts into practice the theory covered in the Principles of data visualization workshop. Participants will learn how to write code that produces beautiful, compelling, and impactful visualizations of their data.
This workshop teaches beginner and advanced ggplot techniques.
Priciples of Data Vizualization
Raw data is hard to interpret, and relationships within raw data are difficult to spot directly. These challenges become increasingly acute as the amount of data increases. Substandard and boring graphs are ineffective in knowledge dissemination. This code-free module will teach participants powerful design principles for clearly summarizing and communicating the messages within their data.
Data Management
Data that is stored haphazardly, organized poorly, and/or formatted unpredictably is hard to analyze, undermines its own value, and wastes people’s limited time. This module teaches modern practices for cleaning, storing, formatting, and accessing data so that its full potential can be realized.
One session is devoted to non-coding principles of keeping data organized, clean, and accessible. The second session is devoted to strategies for bringing your data into R and basic data cleaning.
Making Maps
Spatial data can be surprisingly difficult to visualize well. Ready to move away from screenshots of GoogleEarth? In this workshop, participants learn simple, open-source tools for making beautiful maps in R. Session 1 is devoted to basic principles and simple mapping functions in R, then producing elegant and interactive maps using R's leaflet package; Session 2 is devoted to building publication-ready maps using the sf and tmap packages in R.
Statistical Modeling
Justifiable, informed decisions have to be made while facing uncertainty, risk, and changing conditions. This module will teach participants rigorous methods for (a) predicting future outcomes based on previous observations and (b) analyzing how those outcomes depend on interacting variables.
In Session 1, participants will learn to conduct simple linear regressions, build models with multiple predictors, and interpret the results. In Session 2, participants will learn more advanced modeling techniques (glm and gam approaches) and methods for model evaluation (e.g., goodness of fit tests) and model comparison. In Session 3, participants wil learn how to make predictions based on their models and evaluate the quality and uncertainty of their predictions.
Statistics Refresher
Statistics are everywhere, but they can be confusing to interpret. It doesn’t help that statistical methods are often used incorrectly or misinterpreted by the folks relying on them. This back-to-basics workshop guides participants through the fundamental principles of statistics, such as thinking about distributions and interpreting p-values, and how to decide which statistical test to use given your data and your research question. Participants will practice using the most common and widely applicable techniques, including the t-test, ANOVA, linear regression, and chi-square analysis.