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arcgis-online-fundamentals

dleyna-control

dLeyna-control is a Digital Media Player written in python to demonstrate how to use the dleyna DMS APIs. It detects Digital Media Servers on the local area network and allows users to browse, search and play their contents.

excel-fundamentals

D-Lab's two-hour introduction to the basics of Microsoft Excel (with support materials for Google Sheets). Learn Excel functions for handling text, math, dates, logic, and calculations; learn to create charts and pivot tables.

geospatial-data-and-mapping-in-python

Introduction to Working with Geospatial Data in Python with Geopandas

geospatial-fundamentals-in-qgis

jukebox

Democratic Jukebox - your democratic music player

miranda-upnp

Automatically exported from code.google.com/p/miranda-upnp

mytestrepo

python-data-from-web

API and web scraping workshops

python-data-visualization

D-Lab's 3 hour introduction to data visualization with Python. Learn how to create histograms, bar plots, box plots, scatter plots, compound figures, and more, using matplotlib and seaborn.

python-data-wrangling

D-Lab's 3 hour introduction to data wrangling in Python. Learn how to import and manipulate dataframes using pandas in Python.

python-fundamentals

D-Lab's 12 hour introduction to Python. Learn how to create variables and functions, use control flow structures, use libraries, import data, and more, using Python and Jupyter Notebooks.

python-text-analysis

D-Lab's 12 hour introduction to text analysis with Python. Learn how to perform bag-of-words, sentiment analysis, topic modeling, word embeddings, and more, using scikit-learn, NLTK, gensim, and spaCy in Python.

python-text-analysis-fundamentals

D-Lab's 9 hour introduction to text analysis with Python. Learn how to perform bag-of-words, sentiment analysis, topic modeling, word embeddings, and more, using scikit-learn, NLTK, gensim, and spaCy in Python.

python-web-scraping

D-Lab's 3 hour introduction to web scraping in Python. Learn how to use APIs and scrape data from websites using the New York Times API and BeautifulSoup in Python.