Data Science Training for Modern Research Organizations

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.

Data Cleaning

Raw datasets are rarely pretty, and few - if any - are ready to be analyzed right out of the box. Missing data, typos, irregular capitalization, and inconsistent date/time and lat/long formatting can make it impossible to analyze or summarize your data. In this workshop, participants learn how to fix, clean, standardize, and reorganize their raw data until it is ready for analysis.

Web Tools & Dashboards

Traditional data-driven reports are static, difficult to update as additional data is acquired, and challenging to distribute. Though we live in the 21st century, reports and papers often adhere to 20th century technology foundations. This module will teach participants how to build and deploy interactive dashboards that can be interrogated directly, updated automatically, and shared easily.

In session 1, participants learn how to build a basic data exploration dashboard using R's shiny package. In session 2, participants learn advanced techniques for making their dashboards as flexible and impressive as possible. In session 3, participants learn how to deploy their dashboards to the web.

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.

GIS Applications

Geographic variability adds significant complexity to data and requires specialized tools and knowledge. GIS software is closed-source, licensed and expensive. This module will teach participants how to work with, analyze, and visualize geographic data using free open source tools.

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.

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.

Writing & Presenting Research

Datasets are useless without effective communication, but many professionals have never been taught how to write an impactful data-driven report or deliver an engaging and persuasive presentation. This module will teach participants how to write about research – organizing a report, conducting a literature review, designing compelling arguments, crafting sentences that are crystal-clear, etc. – and how to present it memorably to professional and public audiences. Session 1 is devoted to writing, and Session 2 is devoted to speaking.

Automated Bibliographies

Bilbiographies are the backbone of reproducible research and scientific progress, but they are tedious to write, prone to errors, and a pain to update. And changing between citation styles is an absolute nightmare! But it no longer has to be this way. In this workshop, participants will learn how to use open-source reference management tools to revolutionize the way they manage their libraries and write their articles.