Go (golang.org) is an open-source programming language that can yield very high performance for large-scale data processing applications. This workshop is an introduction to programming in Go with data. Participants should have programming experience in some language, but prior exposure to Go is not expected. We will cover writing a basic Go program, using the Go tools, Go data structures, and reading files.
Numpy is the powerful and widely-used array and linear algebra library for Python. We will cover the basics of array manipulation using Numpy, and cover selected more advanced topics including broadcasting and type conversion. The workshop assumes an intermediate level of Python programming, but no prior knowledge of numpy is required.
Well known for its statistical capabilities, R can also be used for web-scraping, connecting with websites via APIs, html documents and presentations, interactive visualizations, dashboards, and even building entire, possibly interactive, websites. This talk will provide an overview of web-based use of aàR. Conceptual introductions, package synopses, and small demonstrations will be presented.
R is an extremely powerful tool for data modeling, visualization, and general programming. In many practical applications of statistics, the vast majority of time is spent preparing the data for eventual analysis. However, this also where many practitioners who use R often have relatively little training. In recent years, a variety of packages have become available to make data wrangling, summarizing, generation and other common operations more straightforward, and easier to read for future use (e.g. via piping and clearer syntax). In addition, some newer visualization packages work these approaches, allowing one to go quite seamlessly from raw data to interactive graphics. This workshop will introduce participants to a handful of tools that can make their data exploration and analytical flow more streamlined and reproducible.