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R Functional Programming

Overview

This workshop helps you to step up your R skills with functional programming. The purrr package provides easy-to-use tools to automate repeated things in your entire R workflow (e.g., wrangling, modeling, and visualization). The end result is cleaner, faster, more readable and extendable code. I highly recommend you to take this workshop (1) if you still write copy-and-paste code, (2) exclusively rely on for loops for automation, and (3) want to know about the joy and power of R functional programming.

Prerequisites

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QGIS Fundamentals: Parts 1-2

This workshop will introduce methods for working with geospatial data in QGIS, a popular open-source desktop GIS program that runs on both PCs and Macs as well as linux computers. Participants will learn how to load, query and visualize point, line and polygon data. We will also introduce basic methods for processing spatial data, which are the building blocks of spatial analysis workflows. Coordinate reference systems and map projections will also be introduced.

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Python Introduction to Artificial Neural Networks

Overview

  1.  A brief history of ANNs (Artificial Neural Networks) and an explanation of the intuition behind them. This part aims to give the audience a conceptual understanding with few mathematical barriers, and no programming requirements.

  2. Step-by-step construction of a very basic ANN. Although the code will be written in Python, it will be intuitive enough for programmers of other languages to follow along. 

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R Data Wrangling and Manipulation

Overview

It is often said that 80% of data analysis is spent on the process of cleaning and preparing the data. This R workshop will introduce tools (notably dplyr and tidyr) that makes data wrangling and manipulation much 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 stuff.

Click here for install instructions and workshop materials.

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Qualtrics Fundamentals

Overview

This workshop will introduce students to the basics of designing a survey instrument using the Qualtrics platform, such as randomization and survey flow. We will also cover more advanced topics like implementing embedded data and using javascript, as well as tips and tricks on how to use your design to maximize the number of quality responses you get.

The last hour of the workshop will be left open to allow for feedback on any existing designs on which participants are working.

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Python Data Wrangling and Manipulation with Pandas

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:

  • pandas data structures

  • loading data

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Introduction to Bash + Git

Overview

An introduction to programming basics in Bash and GitHub that are often assumed, but that you might have never had good instruction on!

The first half of this workshop will introduce you navigating your computer’s filesystem and basic Bash commands to remove the fear of working with the command line to give you the confidence to use it to increase your productivity.

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Computational Text Analysis: ML, OCR and NLP for extracting information from biological pathway figures

Computational Text Analysis Working Group (CTAWG)

Title: ML, OCR and NLP for extracting information from biological pathway figures

Presenters: Anders Riutta from the Institute of Data Science and Biotechnology, Gladstone Institutes in San Francisco. Anders will present the work done together with Kristina Hanspers, Martina Summer-Kutmon & Alexander R. Picoon - which deals with information extraction from biological pathway figures using NLP, ML and OCR.

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Organized Code Repositories Accelerate Science and Facilitate Reproducibility

Posted: Mar, 02, 2021

By: Pratik Sachdeva

Computational and data-driven research increasingly requires developing complex codebases. At the same time, many scientists don’t receive training in software engineering practices, resulting in, for some, the perception that scientists write terrible software. As scientists, good software should accelerate our work and facilitate its reproducibility.

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Sign up for CALI-DH Online today!

Posted: Mar, 02, 2021

By: Evan Muzzall

Cultural Analytics Learning Institute for Digital Humanities (CALI-DH Online)

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