Data Manipulation and Cleaning

Python Data Wrangling and Manipulation with Pandas

September 28, 2022, 3:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

R Data Wrangling and Manipulation: Parts 1-2

October 4, 2022, 9:00am
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.

Ella Belfer

Consultant
Energy and Resources Group

Ella is a PhD student in the Energy and Resources Group. Her research examines water governance in a changing climate, drawing on geo-spatial techniques. Her past work includes applications of topic modelling in climate change adaptation research, and inductive coding of semi-structured interviews.

R Data Wrangling and Manipulation: Parts 1-2

September 12, 2022, 2:00pm
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.

Python Web Scraping & APIs

June 29, 2022, 1:00pm
In this workshop, we cover how to extract data from the web using Python. We focus on two approaches to extracting data from the web: leveraging application programming interfaces (APIs) and web scraping.
See event details for participation information.

R Data Wrangling and Manipulation: Parts 1-2

May 24, 2022, 1:00pm
It is said that 80% of data analysis is spent on the process of cleaning and preparing the data for exploration, visualization, and analysis. This R workshop will introduce the dplyr and tidyr packages to make data wrangling and manipulation easier. Participants will learn how to use these packages to subset and reshape data sets, do calculations across groups of data, clean data, and other useful tasks.
See event details for participation information.

Python Data Wrangling and Manipulation with Pandas

May 31, 2022, 1:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.
See event details for participation information.

Python Data Wrangling and Manipulation with Pandas

April 18, 2022, 10:00am
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

Python Data Wrangling and Manipulation with Pandas (5pm-8pm)

March 29, 2022, 5:00pm
Pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with 'relational' or 'labeled' data both easy and intuitive. It enables doing practical, real world data analysis in Python. In this workshop, we'll work with example data and go through the various steps you might need to prepare data for analysis.

Python Web Scraping

April 5, 2022, 1:00pm
In this workshop, we cover how to extract data from the web using Python. We focus on two approaches to extracting data from the web: leveraging application programming interfaces (APIs) and web scraping.