☞ RSVP for the Github workshops.
Open Science with Github and Git
April 16, 12:00-2:00, Library of Science and Medicine, Conference Room (room 107)
Scientists are frequently required to share the products of their funded research. Learn how to use the freely available Github platform for project management and collaboration, and connect your Github project to the open Zenodo repository to share your work, and make it citable with a permanent Digital Object Identifier (DOI). No previous experience is necessary.
Introduction to Github and Git
November 9, 1:30 - 3:30, Library of Science and Medicine, Conference Room (room 107)
November 30, 1:30 - 3:30, Alexander Library, Pane Room (1st floor)
This workshop will introduce participants to the open code sharing platform Github, and explore commonly used functions in Github and Git. The first 45 minutes will be introductory, and will continue with slightly more advanced functions in the second half. No previous experience is necessary, and please bring your own laptop.
☞ RSVP for the Python workshops.
Workshops are offered in either Alexander Library or LSM (with identical content). Participants in LSM-based workshops must bring their own laptops. At Alexander, you can either bring your own laptop, or use the desktops in the lab.
Python Basics and Data Exploration
This workshop will be an accelerated introduction to fundamental concepts such as variable assignment, data types, basic calculations, working with strings and lists, control structures (e.g. for-loops), functions. We will also start working with pandas, a popular data science library in Python, to explore a dataset on foodborne outbreaks reported to the CDC.
Data Manipulation and Analysis with Python
In this workshop, we will dive into the world of arrays and data frames using the NumPy and pandas libraries. We'll cover data cleaning and pre-processing, joining and merging, group operations, and more. If you work with tabular data, this workshop is for you!
Data Visualization and Machine Learning with Python
Interested in finding patterns and predicting unknown attribute values in your data? Join us for an overview of machine learning techniques implemented using the scikit-learn library. We'll also learn how to do data visualization with matplotlib, a popular plotting library in Python.
Data Scraping: Interaction with APIs with Python
This workshop is intended to show how to use Python to interact with third-party APIs for data collection. Different type of APIs with real applications will be introduced. Examples such as Rest API for FRED and Quandl will be discussed. A project regarding interacting with FRED API and merging with historical data will be demonstrated in detail.
Data Mining: Regression and Classification with Python
The traditional Least Square estimation, KNN face severe overfitting issues when the dataset has high-dimensional features. Modern data mining regression techniques such as lasso and classification techniques such as SVM gives a better estimation result in such a situation. The workshop intends to show how lasso and SVM works in Python. Compare the estimation result of Lasso with least square estimation, SVM with KNN in the high-dimensional setting.
Machine Learning: Building a Neural Network from Scratch
This workshop is intended to help you establish a Neural Network mindset, and hone your intuitions about Deep Learning. We start by building a logistic regression as the baseline model to recognize cats. Then we develop a single hidden layer NN and extend to a deep NN by adding as many hidden layers as you want. Hopefully, you will see an improvement in accuracy relative to previous logistic regression.
Introduction to SAS
This workshop provides an introduction to SAS, covering the basics of navigation, loading data, graphics, and elementary descriptive statistics and regression using a sample dataset.
SAS is a powerful and long-standing system that handles large data sets well, and is popular in the pharmaceutical industry and health sciences, among other applications.
Introduction to R
This session provides a three-part orientation to the R programming environment, covering statistical techniques, graphics, and data manipulation.
Data Visualization with R
This workshop discusses principles for effective data visualization, and demonstrates techniques for implementing these using R. Some prior familiarity with R is assumed (packages, structure, syntax), but the presentation can be followed without this background.
Also see the Digital Humanities workshops above for several workshops involving R
☞ RSVP for the NVivo workshops.
Introduction to NVivo
This workshop is intended as a basic introduction to using NVivo, a software that supports qualitative and mixed methods research. The workshop focuses on introducing key mechanisms of the software that may be applied as required by different analytical approaches.
Advanced NVivo: Data Visualizations Using NVivo 12
This workshop focuses on the introduction of a suite of visualizations that help you gain deeper insights from your data by exploring and unearthing patterns, trends and connections.
☞ RSVP for the DH workshops.
Citation Management with Zotero
Zotero is a free application that collects, manages, and formats citations and bibliographies. In this introductory, hands-on workshop, we’ll learn how to organize sources, attach PDFs and notes, create tags for easy searching, and generate citations and bibliographies in Word. Bring your personal laptop, download Zotero 5.0 for your OS, and the Zotero Connector for your favorite browser.
Visualizing Demographic Data in Social Explorer
This workshop will introduce you to Social Explorer, an online mapping tool that allows you to explore and visualize demographic data. We will explore the tool's basic capabilities and make sample maps using data from the American Community Survey (ACS).
Introduction to Quantitative Text Analysis
This hands-on workshop will introduce participants to the basics of quantitative textual analysis using the R programming language. Participants will each first select a text of their choice from Project Gutenberg (literary or otherwise), which we will then explore through the demonstration of a variety of approaches, including word frequency, distribution, and co-appearance. No coding experience required.
Introduction to Mapping
What kind of information should be mapped? Which tool is best for the job? If you’ve ever found yourself asking either of these questions – or any other about getting started with mapping – this workshop is for you. We’ll begin with a primer how to identify what kind of data is best suited by a map and what data is necessary to make a map. Then, we’ll explain how to get started with a few common mapping programs (StoryMap JS, Palladio, Tableau, Carto) and evaluate what kinds of uses each is best suited to.
Accessing and Exploring Twitter Data
This hands-on workshop will step participants through the process of collecting social media data from twitter (by handle, hashtag, and/or search phrase) and some of the concerns involved. Participants will then be introduced to a few simple ways to begin analyzing tweet content and metadata, such as the number of likes and retweets.
Approaches to Web Scraping in R
An ever-growing wealth of information can be accessed online, but often there is no easy way to obtain this information for further analysis. This hands-on workshop will introduce a solution to this problem: web scraping, a technique for extracting data and data structures from public websites. Using our browsers and the R programming language, we'll also explore strategies for handling different kinds of websites. No previous coding experience required.
Rutgers, The State University of New Jersey, an equal access/equal opportunity institution. Individuals with disabilities are encouraged to direct suggestions, comments, or complaints concerning any accessibility issues with Rutgers web sites to: email@example.com or complete the Report Accessibility Barrier / Provide Feedback Form.