In Person – Eval+Learn

Our in-person classes use engaging data, interactive activities, and a step-by-step approach that makes complex analytic concepts accessible to everyone.

We use an inquiry-based teaching style that increases engagement + motivation and decreases frustration + over saturation.

Our in-person workshops are typically 7 hours of content delivered by two instructors that train at a location of your choosing. Content length and material can be customized to meet your needs.

See information about some of our more popular workshops below.

Foundations of Analytics

Developing the Data-Driven Mindset

This course introduces participants to the concept of data-driven decision-making. Participants will learn how they can better use data for setting goals and defining objectives while identifying the proper metrics for those objectives. Participants will also learn how to assess the right analytical tools to manage projects and processes. The class involves a hands-on exercise with open data and is intended to build data literacy and familiarity with the analytics process while also addressing the challenges and opportunities of doing more and better analysis.

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Excel for Data Analysis

Excel for Data Analysis I

This one-day class will address one of the greatest challenges those who work with data face today: how to make sense of the data they already have. Using Microsoft Excel, participants will be introduced to the foundational skills necessary to clean, process, and visualize data with Microsoft Excel including PivotTables, PivotCharts, functions, and formulas. This class reinforces the problem ideation and process mapping skills taught in “Developing the Data-Driven Mindset” and “Data Analytics for Managers”, as well as introduces best practices for documenting and visualizing work. Working collaboratively in small groups, participants develop an analytical question they explore throughout the class, presenting their data story at the end of class for constructive feedback.

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Excel for Data Analysis II

A one-day class reinforcing the skills necessary to clean, process, and visualize data, as well as introducing advanced skills with PivotTables, data visualization, functions, and formulas including joins, and logic functions in Microsoft Excel. This class reinforces the problem ideation and process mapping skills taught in “Developing the Data-Driven Mindset” and “Data Analytics for Managers”, as well as best practices for documenting and visualizing work from “Excel For Data Analysis I”. Working collaboratively in small groups, participants develop an analytical question they explore throughout the class, presenting their data story at the end of class for constructive feedback.

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Statistical Analysis with Excel I

A practical, no nonsense. one-day course covering the basics of descriptive statistics with open data, including basic statistical measures such as mean, median, standard deviation, and variance. The course also covers correlation, linear regression, and introduces decision modeling using open data. This course introduces participants to the use of statistics for understanding and communicating city data. Using Excel, participants will learn how to use measures like mean, median, mode, standard deviation, and variance interval to understand the content of city data for making operational decisions. Participants will also learn how to display statistical information in meaningful ways.

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Data Visualization

Data Visualization with Excel

A full-day course covering the key concepts of how to tell the story of your data with truthful and compelling visualizations that are customized to the intended audience. The course will cover how to design and create impactful charts, graphs, and other information visualizations in Microsoft Excel in order to effectively communicate critical analysis to decision-makers, policymakers, and other key stakeholders.

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Creating Interactive Dashboards in Google Data Studio

If you want to create better reports while taking less time to do it, look no further. This course will have you quickly and confidently automating reports, creating interactive visualizations, and delivering engaging dashboards your audience will love.

This course is available for in-person, live online, and self-paced online instruction.

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Advanced Tools for Data Analysis

R for Data Analysis I

A full-day course covering the key concepts of how to leverage the R programming language for data analysis. The course will cover the basic syntax of R as it relates to performing basic exploratory data analysis, as well as how to create impactful charts, graphs, and other information visualizations using NYC Open Data for operational decision making.

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Python for Data Analysis I

A full-day course covering the key concepts of how to leverage the Python programming language for data analysis using open data. The course will cover the basic syntax of Python as it relates to performing basic exploratory data analysis, as well as how to create impactful charts, graphs, and other information visualizations using NYC Open Data for operational decision making.

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GIS for Data Analysis I

A one-day class teaching the basics needed to leverage a geographic information system (GIS) to clean, process, and visualize data. The class will introduce key concepts and skills necessary to use a GIS for data analysis, reinforcing the problem ideation and process mapping skills taught in “Developing the Data-Driven Mindset” and “Data Analytics for Managers”. Working collaboratively in small groups, participants will develop an analytical question they explore throughout the class, presenting their data story at the end of class for constructive feedback.

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SQL for Data Analysis I

A one-day class reinforcing the skills necessary to leverage a relational database to clean, process, and visualize NYC Parks data. The class will introduce key concepts and skills necessary to use the Structured Query Language (SQL) for data analysis, reinforcing the problem ideation and process mapping skills taught in Introduction to Data Analysis. Working collaboratively in small groups, participants will develop an analytical question they explore throughout the class, presenting their data story at the end of class for constructive feedback.