CLASSES
BRING THIS TRAINING TO YOUR ORGANIZATION.
Advanced Data Analysis
Introduction to Statistical Analysis with Excel
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.
Data Analysis with R
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.
Lecture Slides / Class Handout / Slide Source Code
Data Analysis with Python
A full-day course covering the key concepts of how to leverage the Python programming language for data analysis. The course covers 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.
Lecture Slides / Class Handout / Slide Source Code
Advanced Data Analysis with SQL
A one-day class reinforcing the skills necessary to leverage a relational database to clean, process, and visualize government open data. The class introduces 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.
Lecture Slides / Class Handout / Slide Source Code
Machine Learning with Python
A 70-hour course covering the key concepts, applications, and practice of machine learning with Python. The course assumes little prior familiarity with either Python or machine learning, introducing basic descriptive statistics as they apply to predictive analytics, as well as supervised and unsupervised learning. Participants end the course familiar with key concepts and practiced in their application using real-world data.