Data Manipulation and Cleaning

Laura Schmahmann

Instructor
City and Regional Planning

I am a PhD Candidate within the Department of City and Regional Planning at UC Berkeley. My dissertation explores the political economy of warehouse development across California, focusing on two case studies - the Inland Empire and North San Joaquin Valley. I am also a Graduate Student Researcher within the Labor Management Partnerships team at the UC Berkeley Labor Center. I hold a Bachelor of Planning (Honours Class 1) and Master of Philosophy (Planning and Urban Development) both from the University of New South Wales.

Chirag Manghani

Consultant
School of Information

Chirag is a 2nd year graduate at the I-School. Proficient in Python, Java, R, and SQL, he navigates software application development, machine learning and data science. His keen interest lies in data analysis and statistical methods, driving him to bridge theory and practice seamlessly. Chirag's dedication to excellence, adaptable mindset, and innate curiosity define him as a dynamic problem solver in the ever-evolving tech landscape.

Python Data Wrangling and Manipulation with Pandas

August 22, 2024, 2:00pm
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.

Deya Chic

Data Science for Social Justice Fellow 2024
Graduate School of Education

Deya is deeply committed to supporting underrepresented students and contributing to policies that address oppression in higher education. She aims to influence fellow professionals and researchers to adopt a comprehensive approach to addressing systemic issues in the higher education system.

TEST: Python Data Wrangling and Manipulation with Pandas

August 22, 2024, 2:00pm
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.

Violet Davis

Data Science for Social Justice Senior Fellow 2024
MIDS

I am a Masters student studying Data Science with the School of Information. My research involves computational social science projects focused on social justice and equity.

R Data Wrangling and Manipulation: Parts 1-2

February 12, 2024, 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.

Python Data Wrangling and Manipulation with Pandas

February 15, 2022, 9:00am
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.

R Data Wrangling and Manipulation: Parts 1-2

November 1, 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

June 26, 2023, 2:00pm
In this workshop, we cover how to scrape data from the web using Python. Web scraping involves downloading a webpage's source code and sifting through the material to extract desired data.