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This is an archive of our past training offerings. We are looking to include workshops on topics not yet covered here. Is there something not currently on the list? Send us a proposal.
Pandas is a Python package providing 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.
We plan to cover:
This workshop addresses various topics in Natural Language Processing, primarily through the use of NLTK. We'll work with a corpus of documents and learn how to identify different types of linguistic structure in the text, which can help in classifying the documents or extracting useful information from them.
This workshop will begin with a talk by UC Berkeley History Prof. Robin Einhorn on her research of taxation in the US. She will discuss how mapping these data is useful for revealing historical trends yet presents a technical challenge when dealing with 100 years worth of information. The second half of the talk will be a tutorial on mapping time series data in R.
This workshop is the first part of a two-part series on survey sampling. This first workshop will cover the basic principles and methods of sampling. Topics will include a discussion of the various types of samples, the creation of sampling frames, the use of stratification, and basic methods of selecting samples. Determining an appropriate sample size will also be discussed.
UPDATE: This workshop has been rescheduled for Monday, 11/21 at 2:30-4:00pm in Barrows 356 (D-Lab Convening Room). Please mark the change in your calendar.
Come learn how to turn your data into powerful webmaps in R, by combing the power of Leaflet and Shiny!
This three-part series will cover the following materials:
Part I: Introduction (Thurday, November 3)
In this process-oriented talk, Dr. Sacks will walk us through the ethical, logistical, conceptual, and methodological challenges related to investigating the impact of historical trauma and contemporary trauma on experiences of one research participant connected to the Tuskegee Syphilis Study. Dr.
Sometimes you just want/need to make a map! This workshop will provide a basic overview of the data sources, steps, and considerations necessary to transform an idea or a table of data to a publication-ready map.
No prior knowledge required.
Come learn how to turn your data into beautiful maps using R!
he focus of this workshop is machine learning using the h2o Python module. H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster).
Please note: This workshop is hosted by the Geospatial Innovation Facility. Register here!
Tableau is a data analysis software suite that allows you to create data driven visualizations without programming. The software works with a wide variety of input data sources and formatssources. Your Tableau output graphics can be combined together to create interactive dashboards that you can share online.
This three-part series will cover the following materials:
Part I: Introduction (Thurday, November 3)
Please note: This is a two-part workshop series. The first session will occur Wednesday, November 2 from 12:00pm to 3:00pm. The second session will occur Wednesdsay, November 9 from 12:00pm to 3:00pm. If you are registered for the November 2 session, your registration is valid for both days.
The R for Data Science workshop series is a four-part course, designed to take novices in the R language for statistical computing and produce programmers who are competent in finding, displaying, analyzing, and publishing data in R.
Part 4: Functions and Packages
This three-part series will focus on how to maximize functions in Microsoft Excel, set up database-like structures, and build various kinds of reports. By the end of this series, participants will be able to import text data, perform basic mathematical operations and character-based functions, sort and filter data, and utilize pivot tables.
This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.
The focus of this workshop is machine learning using the h2o and h2oEnsemble R packages. H2O is an open source distributed machine learning platform designed for big data, with the added benefit that it is easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster).
In this workshop, we will explore the process of moving from coded data to conclusions, the logics of analysis, identifying relationships in data, and building arguments with qualitative data.
This workshop will introduce the ArcGIS Online (AGOL) platform. AGOL is a web-based mapping software that allows you to build maps and explore data online. Topics to be covered include how to construct a simple web map from a spreadsheet of data, perform basic spatial analysis and queries, and publish the map to the web.
