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Data Services Class Descriptions

Information, materials, and schedules for all currently offered Data Services classes
This session is an intermediate-to-advanced level class that offers some ideas for how to approach common data wrangling needs in research. This courses focuses on obtaining data and loading it into a suitable data "container" for analysis, often via a web interface, especially an API; parsing data retrieved via an API and turning it into a useful object for manipulation and analysis; performing some basic summary counts of records in a dataset and work up quick visualizations.
Software: Python, Jupyter Notebooks
Duration: 120 min

Room description:

During the Fall 2021 semester, some tutorials are held remotely and require NYU sign on to access, while others are held in person, without a remote component. Please note the correct modality and location of the tutorial when registering

Prerequisites:
  • Familiarity with Pandas, Numpy, and core Python objects types (lists, dictionaries, strings, numbers)
  • Familiarity with common data storage file types such as JSON and CSV
  • Comfort with using Jupyter Notebooks for writing code
  • Understanding of (or comfort with learning about) principles of writing code to make http (web) requests
Skills Taught / Learning Outcomes:
  • Pulling data from website front ends, unrestricted web APIs, and web APIs requiring token authorization
  • Parsing API responses in JSON or HTML format to transform them into usable Python data objects
  • Performing quick summary statistics on data
  • Producing quick in-line visualizations in Jupyter Notebooks
Class Materials: https://nyu-dataservices.github.io/DataHarvesting-Python/​
Related Classes:

Introduction to Python

Data Cleaning Using OpenRefine

Data Visualization with Tableau

Introduction to Jupyter Notebooks

Introduction to Research Data Management

 

Additional Training Materials:

Data analysis with Python and Pandas [video]

Python API tutorial - An Introduction to using APIs

Feedback: bit.ly/feedbackds

 

Upcoming sessions for this tutorial