These are the slides I used in my presentation about Data Science in Ruby during the first Rubyconf Thailand Really great event! feel free to send questions

1. Data Science in
Ruby? Is it possible?
Is it Fast? Should we
use it?
• Rodrigo Urubatan
• rodrigo@urubatan.dev
• http://urubatan.dev
• http://twitter.com/urubatan

2. Anyone here work
with Data Science?
• Data Scientist?
• Data Engineer?
• Developers of application that uses Data?
• Statisticians?

3. What exactly
is Data
Science?
The process of extracting meaning from and interpret
data
The usage of statistics and machine learning to clean
and manipulate data
The usage of computer software to collect, clean,
manipulate and interpret data
A cool name for the combination of Data Mining and
Business Intelligence (other buzz words that were used
for a long time for exactly what we call Data Science
today, but with more expensive tool sets)

4. Can Ruby do Data
Science?

5. Can Ruby do
Data Science?
(Long Answer)
INTEGRATION WITH
OTHER TOOLS
DATA
MANIPULATION
DISTRIBUTED
COMPUTING
DATA STRUCTURES
DATA SETS STATISTICS VISUALIZATION INTERACTIVE
COMPUTING

6. Interactive
Computing
iruby — Ruby kernel
for Jupyter.
iruby-rails —
Integration library for
IRuby and Rails.

7. Standing on
the shoulders
of giants
(integration)
pycall — Bridge into
the Python world.
rserve-client — Ruby
connector for Rserve,
R's binary server.

8. Data
manipulation
kiba — lightweight Ruby ETL
(Extract-Transform-Load)
framework.
jongleur — Workflow
manager using DAG
definitions to execute ETL
tasks

9. Distributed
Computing
ruby-spark — Ruby
Interface to Apache
Spark 1.x.x.
jruby-spark — JRuby
based bindings
for Apache Spark.

10. Data
Structures
daru — Data Frame and Vector
structures with comprehensive
manipulating and visualization methods.
numo-narray — n-dimensional
Numerical Array for Ruby.
nmatrix — dense and sparse linear
algebra library for Ruby via SciRuby.

11. Data Sets
rdatasets — Data sets
available in R via Rdatasets.
red-datasets — Growing
collection of publicly
available data sets such as
CIFAR-10, Iris, MNIST etc

12. Statistics
rb-gsl — Ruby interface to the GNU
Scientific Library. [dep: GLS]
simple_stats — Enumerable patches
for descriptive statistics.
enumerable-statistics — fast
implementation of descriptive
statistics for the Enumerable module.

13. Visualization
• matplotlib — Ruby based wrapper
around matplotlib. [dep: matplotlib]
• mathematical — PNG and MathML
renderings for your equations.
• daru-view — daru-view is
interactive plotting gem for web
application (any Ruby web
application framework like
Rails/Sinatra/Nanoc/Hanami) &
IRuby notebook. It is a plugin gem
for daru.
• daru-plotly — Plotly based
visualization for Daru.

14. The 3 Major
Ruby Data
Science
Projects
SciRuby project
Nmatrix Centric gems
Nmatrix
Daru
GnuplotRB
Stas_sample
Ruby Numo project
Numo:: NArray centric Gems
Numo:: NArray
Numo:: FFTE
Numo:: FFTW
Numo::Gnuplot
RedDataTools project
Apache Arrow centric gems
RedArrow
RedChainer
RedArrowGSL
RedArrowNMatrix
RedArrowNumoNArray

15. Doing data science in
Ruby is Hard!

16. Ruby
X
Python
Ruby
Daru
NMatrix/NArray
Python
Pandas
Numpy

17. Simple number operation with numpy

18. “Same”number operation with NMatrix

19. Simple DataFrame operation with Pandas

20. “Same” DataFrame operation with Daru

21. Ruby and Ruby on Rails are
way better to write business
web applications!

22. We can even do
really good Machine
Learning with Ruby
(but that is subject
for another
presentation)

23. And my objective is to
help ruby developers to use
the best tools for each job so
they can solve hard
problems, with less bugs and
have more free time.

24. pycall to the
rescue
pycall lets you use Python libraries from
your ruby code very naturally, as if you
were calling a Ruby library
pycall consists of one ruby binding
library for libpython.so and an Object-
oriented protocol for communication
between Ruby and Python

25. Simple pycall
code

26. Ok, so what
are the best
work
patterns?
Python is way better than Ruby for
Data Science
Ruby is better for web business
applications
Best patterns for integration are
(IMHO)
• Pointing both applications to the same
database
• Exchanging data through JSON or some similar
serialization
• Calling Python directly through pycall

27. References
• Ruby Conf 2017 – Using Ruby in Data Science by Kenta Murata (@mrkn)
• Big Data analysis in Ruby
• Lets do some (Data) Science in Ruby by Dan Carpenter (@dan_alyst)
• Progress of Ruby/Numo: Numerical Computing for Ruby
• SciRuby
• Ruby::Numo
• Ruby Machine Learning resources
• Ruby Data Science Resources
• PyCall

28. Any questions? Talk to
me!
• @urubatan
• https://urubatan.dev
• rodrigo@urubatan.dev

29. Other Data
Structure
Libraries
• spreadsheet — manipulation library for MS
Excel spreadsheets
• mdarray — Array structure for Jruby
• cumo — CUDA-aware numerical Array library
with NArray similar interface.

30. Other statistics libraries
statsample — basic and advanced statistics for Ruby. [dep: GLS]
statsample-glm — extension of statsample by Generalized Linear Models.
statsample-bivariate-extension — extension of statsample by Bivariate Correlations.
statsample-timeseries — extension of statsample by Time Series estimators.
pca — Principal Component Analysis (PCA) in Ruby.
descriptive-statistics — descriptive extensions for the Enumerable module or standalone usage.
distribution — probabilistic distributions and descriptive measures for them.
statistics2 — Normal, Chi-square, t- and F- probability distributions for Ruby.

31. General
Format IO
• https://github.com/fiksu/rcsv
• ox — Optimized for speed XML parser and
object marshaller.
• oj — High-speed JSON parser.
• Markdown
• Nokogiri
• CSV

32. Database
Adapters
• pg
• Mongo
• MySQL