Data Manipulation and Cleaning

CANCELED: Python Data Wrangling and Manipulation with Pandas

November 29, 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.

Exploring Population Data with IPUMS

November 8, 2022
Exploring Population Data with IPUMS

IPUMS logo with feature icons

Last month, demographer and historian Steve Ruggles was awarded a prestigious MacArthur Foundation Fellowship for his work developing IPUMS—a harmonized database of individual and family...

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 & APIs

November 2, 2022, 3:00pm
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.

Python Data Wrangling and Manipulation with Pandas

October 24, 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 Machine Learning with tidymodels: Parts 1-2

November 29, 2022, 10:00am
Machine learning often evokes images of Skynet, self-driving cars, and computerized homes. However, these ideas are less science fiction as they are tangible phenomena that are predicated on description, classification, prediction, and pattern recognition in data. During this two part workshop, we will discuss basic features of supervised machine learning algorithms including k-nearest neighbor, linear regression, decision tree, random forest, boosting, and ensembling using the tidymodels framework. To social scientists, such methods might be critical for investigating evolutionary relationships, global health patterns, voter turnout in local elections, or individual psychological diagnoses.

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.

Aaron Culich

Consulting Drop-In Hours: By appointment only

Consulting Areas: Python, R, SQL, APIs, Cloud & HPC Computing, Databases & SQL, Bash or Command Line, Git or Github

Quick-tip: the fastest way to speak to a consultant is to first submit a request and then ...

Wadzanai Makomva

Discovery Graduate Fellow
School of Information

Wadzanai is a graduate student at the School of Information and she is a part of the MIMS program. She has a vested interest in the integration between data science, technology and developmental surveillance techniques. She has prior experience working as a quantitative analyst in project management consulting within a professional services firm, public health, and most recently in sustainable construction materials. Wadzanai is particularly interested in increasing access of STEM subjects and fields to under-privileged women of color in the African continent, particularly her home...