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Data Science is a new "sexy" buzzword without specific meaning but often used to substitute Statistics, Scientific Computing, Text and Data Mining and Visualization, Machine Learning, Data Processing, and Warehousing as well as Retrieval Algorithms of any kind.

The general consensus when it comes to programming languages for data science is to utilize either Python, R or Julia. This, however, is not necessarily the case if you are a Rubyist. Here is a short list of some awesome data science tools for Rubyists:

1. Ruby vs. Python

Ruby Python
Daru Pandas
NArray NumPy

2. Data Manipulation

a. Kiba (https://github.com/thbar/kiba/) — lightweight Ruby ETL (Extract-Transform-Load) framework.
b. jongleur (https://gitlab.com/RedFred7/Jongleur) — Workflow manager using DAG definitions to execute ETL tasks.

3. Distributed Computing

a. ruby-spark (https://github.com/ondra-m/ruby-spark) — Ruby Interface to Apache Spark 1.x.x.
b. JRuby-spark (https://github.com/chyh1990/jruby-spark) — JRuby based bindings for Apache Spark.

4. Data sets

a. rdatasets (https://github.com/kojix2/rdatasets) — Data sets available in R via Rdatasets.
b. red-datasets (https://github.com/red-data-tools/red-datasets) — Growing collection of publicly available data sets such as CIFAR-10, Iris,
MNIST etc.
5. Statistics

a. rb-gsl (https://github.com/blackwinter/rb-gsl) — Ruby interface to the GNU Scientific Library.
b. simple_stats (https://github.com/brianhempel/simple_stats) — Enumerable patches for descriptive statistics.
c. enumerable-statistics (https://github.com/mrkn/enumerable-statistics) — fast implementation of descriptive statistics for the Enumerable
module.
d. statsample (https://github.com/sciruby/statsample) — basic and advanced statistics for Ruby
e. statsample-glm (https://github.com/sciruby/statsample-glm) — extension of statsample by Generalized Linear Models
g. statsample-bivariate-extension (https://github.com/sciruby/statsample-bivariate-extension) — extension of statsample by Bivariate
Correlations.
h. distribution (https://github.com/sciruby/distribution) — probabilistic distributions and descriptive measures for them.
i. statistics2 (https://github.com/abscondment/statistics2) — Normal, Chi-square, t- and F- probability distributions for Ruby.

6. Visualization

a. matplotlib (https://github.com/mrkn/matplotlib.rb) — Ruby based wrapper around matplotlib.
b. mathematical (https://github.com/gjtorikian/mathematical) — PNG and MathML renderings for your equations.
c. daru-view (https://github.com/sciruby/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.
d. daru-plotly (https://github.com/genya0407/daru-plotly) — Plotly based visualization for Daru.

7. Interactive Computing

a. iruby (https://github.com/sciruby/iruby) — Ruby kernel for Jupyter.
b. iruby-rails (https://github.com/SciRuby/iruby-rails) — Integration library for IRuby and Rails.

8. Machine Learning

https://github.com/arbox/machine-learning-with-ruby

Happy Coding!