My presentation on openSUSE Asia Summit 2019

1. Data Science Environment with R
on openSUSE Leap 15.1
Sabar Suwarsono
2019-10-05
CC BY-SA 4.0
soewarsono@klim.or.id
@soewarsono

2. Who am I?
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Data Scientist with R enthusiast
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Member of Komunitas GNU/Linux Malang (KLiM)
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Member of openSUSE Indonesia
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Member of Komunitas R Indonesia
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Sysadmin on University of Islam Malang

3. Introduction to Data Science

4. Data Science is the intersection of
Hacking Skills, Math & Statistics
Knowledge and Substantive Expertise
Those are the pillars of data science: computing,
statistics, mathematics and quantitative
disciplines combined to analyze data for better
decision making

5. Hacking Skills
Ability to build things and find clever solutions to
problems.
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Programming/Coding: Python and R (and others)
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Databases: MySQL, PostgreSQL, Cassandra,
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MongoDB and CouchDB.
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Visualization: D3, Tableau, Qlikview and Markdown.
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Big Data: Hadoop, MapReduce and Spark.

6. Hacking Skills

7. Hacking Skills
https://www.kdnuggets.com/2019/05/poll-top-data-science-machine-learning-platforms.html

8. Math & Statistics
Being able understand the right solution to each
problem
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Linear algebra: Matrix manipulation
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Machine Learning: Random Forests, SVM,
Boosting
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Descriptive statistics: Describe, Cluster
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Statistical inference: Generate new knowledge .

9. Math & Statistics

10. Substantive Expertise
Ability to ask good questions requires domain understanding,
that’s why a data scientist can’t create data based solutions
without a good industry knowledge
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Is this A or B or C? (classification)
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Is this weird? (anomaly detection).
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How much/how many? (regression).
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How is it organized? (clustering).
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What should I do next? (reinforcement learning)

11. Why Data Science?
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Data is the oil for today's world. With the right tools, technologies, algorithms, we
can use data and convert it into a distinctive business advantage
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Data Science can help you to detect fraud using advanced machine learning
algorithms
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It helps you to prevent any significant monetary losses
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Allows to build intelligence ability in machines
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You can perform sentiment analysis to gauge customer brand loyalty
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It enables you to take better and faster decisions
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Helps you to recommend the right product to the right customer to enhance your
business

13. What is R Programming

14. Why Learn R

15. Data Science on R

16. Know your neighbour!
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Who are you?
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What you do with data?
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How would you describe
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your experience with R?

17. Compiling R from source and
why you shouldn't do it
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I want a faster R.
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I can compile against other linear algebra
libraries and get a speedup that way.
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I need special customization.
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I just want to.

18. Dependency of R installation From Source
on openSUSE
pattern devel_basis lapack-devel
gcc-fortran java-11-openjdk
readline-devel java-11-openjdk-accessibility
xorg-x11-devel java-11-openjdk-devel
xz-devel java-11-openjdk-headless
libcurl-devel java-11-openjdk-jmods
texlive libpng-devel

19. Installing dependency

20. Download & Extract R Source

21. Compiling R From Source

30. R + RStudio