In this workshop, you will learn foundational principles of graphic design and how to apply these to the reports, memos, posters, images, and other documents you create in your daily work. You will learn a set of four core design principles that apply to all aspects of design; how to use these principles to choose good and accessible combinations of fonts, colors, and other graphical elements; and how to create beautiful well-designed documents with Microsoft Word, Canva, and Gravit Designer.

Power BI is a collection of software services, apps, and connectors that work together to turn unrelated sources of data into coherent, visually immersive, and interactive insights. This course will provide an overview of Power BI and how its services and applications work together to connect a data source to begin visualizing data. During the workshop, you will learn about Power BI and how its services and applications work together to make your business more efficient, understand the building blocks of a Power BI dashboard, connect a data source to a Power BI dashboard and begin visualizing data, and save a report and share within the Power BI service.

Power BI enables everyone at every level of your organization to make confident decisions using analytics. Once you get started with Power BI Desktop, you can go from data to insight to action with hundreds of data visualizations, built-in AI capabilities, a tight Excel integration, and pre-built and custom data connectors to develop rich and interactive reports to share with colleagues. During this workshop, you will understand the building blocks of Power BI desktop, explore the data prep capabilities of Power Query, write formulas and model your data, begin visualizing data and formatting a report, and save and publish a report to the Power BI service.

With the Power BI app for Microsoft Teams, users can easily find data in Power BI using fully-featured experiences without leaving Microsoft Teams and bring data and insights into their chats, meetings, calendar invites, and, of course, teams. By making data more accessible, organizations can have insights-based discussions at the point of collaboration and make better decisions faster. During this workshop, you will collaborate with Power BI in Microsoft Teams, connect to a sample Power BI dataset, and connect to a personal Teams usage report.

With the no-code artificial intelligence (AI) experiences in Microsoft Power BI, users can easily leverage highly-interactive and intelligent visuals to detect patterns in their data, ask questions of their data, and discover new insights that can empower every individual and every team in their decision-making. During this workshop, you will collaborate with Power BI in Microsoft Teams, connect to a sample Power BI dataset, explore how to make confident decisions using AI capabilities, and create a report with the Power BI service.

In this workshop, you will learn the basics of Python, including getting started with Anaconda and JupyterLab; writing, executing, and annotating code; and using Python syntax, objects, and data types. This workshop also focuses on learning more about data analysis using the Pandas module to load and import data, view and subset data, analyze descriptive statistics, and run crosstabs and chi-squared tests.

In this workshop, you will learn more about statistical modeling, including the general logic behind fitting models in Python, how to use the Statsmodels module to generate linear models, and how to use the Scikit-Learn module to generate models. Additionally, you will learn more about data visualization using the Seaborn module, focusing on an overview of plotting syntax using the Matplotlib and Seaborn modules, creating simple univariate and bivariate plots, adjusting plot aesthetics, and layering multiple plots together.

In this workshop, you will learn more about data wrangling, including merging data sets and transforming variables (i.e., recoding, relabeling, and binning). You will also learn new skills like installing new modules and libraries (e.g., Plotly) and displaying code outputs on Github. You will also work with text data and explore string detection and term frequencies.

In this workshop, you will be introduced to the tidyverse, a set of R packages that work together to make loading, cleaning, manipulating, reshaping, visualizing, and analyzing data fun and intuitive. You will learn how to import data from CSV files, Excel, Stata, SPSS, and SAS with the readr, readxl, and haven packages; filter, summarize, reshape, rearrange, and manipulate data with the dplyr and tidyr packages; create beautiful graphs and visualizations with the ggplot2 package; and write reproducible HTML, Word, and PDF reports with R Markdown and the knitr package.

In this workshop, you will learn about the grammar of graphics–a special language for describing data-based graphics–and how to use the ggplot2 package in R to create beautiful and powerful data visualizations. You will learn how to map columns in a data set to specific elements (or aesthetics) in a graphic; learn about the key elements of the grammar of graphics and how to translate the grammar to R code; learn best practices for creating truthful, accessible, and well-designed data visualizations; create interactive HTML graphics with the plotly package; create animated graphics with the gganimate package; and create simple HTML dashboards with the flexdashboard package.

In this workshop, you will learn about exciting new statistical methods that allow you to make causal inferences from observational data: causal diagrams (or directed acyclic graphs) and do-calculus. You will then apply this language of causal inference to program evaluation and learn how to measure the effectiveness and outcomes of social programs with several different statistical methods. You will learn how to create causal models with directed acyclic graphs (DAGs); use basic do-calculus to close and isolate backdoor paths between program treatment and program outcome variables; apply the language of causal models to core econometric techniques, including multiple regression, randomized control trials (RCTs), difference-in-differences (diff-in-diff, or DiD), and regression discontinuity (RD) research designs; analyze results from a randomized control trials; analyze results from an observational study using do-calculus and regression; analyze results from an observational study using difference-in-differences; and analyze results from an observational study using regression discontinuity.

In this workshop, you will learn how to use R to work with geographic data, conduct special GIS calculations, and create beautiful maps. You will learn how to find special geographic datasets (or shapefiles) and import them into R with the sf package; filter, summarize, reshape, rearrange, and manipulate geographic data with the dplyr package; convert geographic data between different map projections; create maps (both simple and complex) with the ggplot2 package; and geocode your own data with the tidygeocoder package.

In this workshop, you will learn how to use R Markdown to combine analysis and text to create powerful documents that showcase and explore your data. You will discover Markdown, a special kind of universal markup language that lets you write in one format and convert to Word, PDF, HTML, and pretty much anything else; explore R Markdown, an extension to Markdown that lets you incorporate R code into text; create interactive HTML plots with the plotly and ggiraph packages; create simple websites using R Markdown; and create data-based dashboards using the flexdashboard package.