Top Tweets for #RubyDataScience
Adding Leaflet visualizations to your #Ruby web app.
More on #visualization with #Ruby on our #RubyDataScience list. https://t.co/ul7P3TMcHZ
leaflet-rails (1.3.1): This gem provides the leaflet.js map display library for your Rails 4/5 application. https://t.co/pUOUoOzIv5
Let’s Do Dome (Data) Science in Ruby with @dan_alyst: https://t.co/ixbGXWqMn8
phobos (1.6.0): Phobos is a microframework and library for kafka based applications, it wraps common behaviors… https://t.co/wMp7qLbH7g
Parsing Wikipedia data with Ruby by @zverok 👍
Data Science done with Ruby
#RubyDataScience https://t.co/Cr3LWoNGeM
Small "case study" article about Infoboxer, written for Rubyist Magazine before #RubyKaigi2017 https://t.co/zBS0jKrQoX
Using #Python's scikit-learn for #MachineLearning in Ruby:
https://t.co/c4eQfMqDs3
#RubyML #RubyDataScience
@sbdaugaard 👍👏😀
Parsing Excel documents with Ruby for data science applications.
More on our #RubyDataScience list: https://t.co/Pm8O98DJ6L https://t.co/4qEJToDTAh
saxlsx (1.7.1): Fast xlsx reader on top of Ox SAX parser https://t.co/8mqujxVAQo
ox (2.6.0): A fast XML parser and object serializer that uses only standard C lib.
Optimized XML (Ox),… https://t.co/G7VaMVzVZG
Ruby for Data Science! Tabular Data Structures like NumPy and Pandas for Python 👍
#RubyDataScience #RubyML https://t.co/FUZGH1BjoF
daru (0.1.6): Daru (Data Analysis in RUby) is a library for analysis, manipulation and visualization
of data. Daru… https://t.co/hxncaLqQdx
There are plenty of useful libs on our #RubyML, #RubyNLP and #RubyDataScience lists! https://t.co/zHid14Yesf
[Ruby] GitHub - arbox/machine-learning-with-ruby: Curated list: Resources for machine learning in Ruby. https://t.co/9KiI4GWz2M
@anthonyikeda You can advertise your position through our #RubyML and #RubyDataScience lists (sure for free).
Parallel processing of data sets for #DataScience with #Ruby.
Now on our #RubyDataScience list too 👍 https://t.co/F13X9dZYId
pwrake (2.2.3): Parallel Workflow engine based on Rake, runs on multicores, clusters, clouds https://t.co/Dh44C9JPmG
Nice comparison of #Ruby and #Python for simple data tasks: https://t.co/9gbtKsdATz
#RubyML #RubyDataScience
(Japanese, read the code)
@forever_frey спасибо за интерес к нашей работе!
Пользуйтесь ресурсами #RubyNLP, #RubyML и #RubyDataScience!
Отзывам всегда рады!
#Ruby vs. #Python for #MachineLearning tasks by @niwasawa :
https://t.co/5hwvLLHH95
#RubyDataScience and #RubyML
From our #RubyDataScience list:
statsapmle by @clbustos
https://t.co/o3MU9vyAot
#Statistics and #DataScience with #Ruby 👏
Finally! #Ruby is self descriptive and should be used for descriptive statistics in #DataScience 😋
#RubyDataScience https://t.co/v1yZZ1oq71
Combining the best from two worlds: #Ruby and #Python for #DataScience and #Datavisualization :
https://t.co/rmuwNWLVt9
#RubyDataScience
From our #RubyDataScience list: #R libraries in #Ruby via RServe: https://t.co/oWwc0RGIb6
#DataScience with #Ruby is affordable 👍
These guys from @TruffleRuby make #Ruby more suitable for #Data intensive tasks 👍#RubyNLP #RubyDataScience #RubyML
Looking for #rubykaigi! https://t.co/WTAOjlb0gn
I'll be talking about how we've massively improved @TruffleRuby's startup time with SubstrateVM at this year's RubyKaigi. https://t.co/JUbq8YyIDZ
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