Analytical Thinking

UVA’s Science Policy Bootcamp is an opportunity for graduate students to learn about science policy, and meet people who have engaged in the process. It is designed to introduce concepts and skills, as well as expose participants to career paths in science policy.

UVA’s Science Policy Bootcamp is an opportunity for graduate students to learn about science policy, and meet people who have engaged in the process. It is designed to introduce concepts and skills, as well as expose participants to career paths in science policy.

Calling UVA graduate students! We’re still thinking about 2020, but it’s never too early to think about next year. Applications are now open for the 2021-2022 Praxis Program cohort. Applications are due March 1st, 2021.

Prior to this session, students will be asked to purchase an online module which walks them through how to convert their excel model into a simple and clear power point presentation. Once again, student volunteers will be asked to present their analysis.

Prior to this session, participants will be asked to purchase an online module that will walk them through the building of an excel model for consulting.

In this session, we will discuss tips and strategies to best frame your experience to tell a compelling story for why you are a good fit for a career in consulting.

In this session we will explore a McKinsey style “interviewer led” case. We will also discuss more about case frameworks and case starts. As part of this session, we will dig even deeper into how case interviews are evaluated by reviewing an actual evaluation form used by a top management consulting firm.  

After we explore, summarize and visualize our data, we likely need to present our findings to an audience. This may be a document, a web page, a slide deck, or even a dashboard. Using R Markdown we can combine R code with narration to quickly create professional reports and presentations, all within RStudio.

Exploring our data with graphs allows us to visualize relationships, spot unusual observations, or find unexpected patterns. In this session we introduce how to effectively use the ggplot2 package to explore and visualize data in R.

Before analyzing data, we spend considerable effort exploring the data – understanding patterns, checking for missingness -- and wrangling the into an analyzable form – creating and recoding variables, merging data sets, reshaping data.