This document discusses the rise of open source analytics tools and languages. It notes that SAS and SPSS previously dominated the market but were very expensive. R, Python, and Hadoop have provided lower-cost open source alternatives for data storage, querying, visualization, and statistical analysis. The document reviews popular open source tools like R, Python, RapidMiner, and Hadoop ecosystems. It also discusses commercial offerings that build on open source like Revolution Analytics. Overall, open source has helped reduce the costs of analytics software and enabled more organizations to benefit from data-driven insights.

1. Open Source in Analytics

2. Introduction
IIML ,DCE
Founder Decisionstats.com
Author R for Business Analytics

3. Brief History of Analytics
SAS and SPSS led from 1970-s to early 2000s
SAS leads market but very expensive
IBM bought SPSS but still not open source
R, Python and Hadoop Challenged this

4. Analytics Sub Components
Data Storage
Data Querying
Data Summarization
Data Visualization
Statistical Routines

5. Analytics Sub Components
Data Storage
Data Querying
Data Summarization
Data Visualization
Statistical Routines
Proprietary Open Source
OracleDBMS
SQL Server
Business Objects
SAP
SQL, SAS,Crystal
Reports
Tableau
SAS,SPSS

6. Analytics Sub Components
Data Storage
Data Querying
Data Summarization
Data Visualization
Statistical Routines
Proprietary Open Source
OracleDBMS
SQL Server
MySQL, NoSQL,
Hadoop
Business Objects
SAP
Pentaho, Jaspersoft
SQL, SAS,Crystal
Reports
Still SQL,Pig, Hive
Tableau R,Python,Javascript
SAS,SPSS R,Python,RapidMiner

7. Analytics using Python
● pandas http://pandas.pydata.org/ High-performance, easy-to-use data structures and data analysis tools
● scikit-learn http://scikit-learn.org/stable/ Simple and efficient tools for data mining and data
analysis and built on NumPy, SciPy, and matplotlib
● NumPy http://www.numpy.org/
● SciPy http://www.scipy.org/scipylib/index.html
● matplotlib http://matplotlib.org/
● statsmodels http://statsmodels.sourceforge.net/# Statsmodels is a Python module that allows users to
explore data, estimate statistical models, and perform statistical tests. An extensive list of descriptive statistics, statistical tests, plotting
functions, and result statistics are available
● iPython http://ipython.org/ interactive computing

8. Analytics using R
http://www.r-project.org/
● RStudio and Revolution Analytics
● sqldf https://code.google.com/p/sqldf/ and RODBC http://cran.r-project.org/web/packages/RODBC/index.html
● ggplot2 http://ggplot2.org/ and ggmap and shiny
● RHadoop et al https://github.com/RevolutionAnalytics/RHadoop
● car, stats, forecast, sna,tm
● rattle and Rcommander (with plugins)
More at http://rforanalytics.wordpress.com/

9. Analytics using R
http://www.revolutionanalytics.com/

10. Analytics using R
http://www.revolutionanalytics.com/

11. Analytics using R
<blatant self promotion>
http://www.amazon.com/R-Business-Analytics-A-Ohri/dp/1461443423
R for Business Analytics looks at some of the most common tasks performed
by business analysts and helps the user navigate the wealth of information in R
and its packages. With this information the reader can select the packages that
can help process the analytical tasks with minimum effort and maximum usefulness
. The use of Graphical User Interfaces (GUI) is emphasized in this book to
further cut down and bend the famous learning curve in learning R.
</blatant self promotion>

12. Analytics using Rapid Miner
Early adopter of open source analytics
Recently moved from Germany to USA
following PE infusion
One of the first marketplace for analytics
extensions http://marketplace.rapid-i.com/UpdateServer/
One of the best GUI - Drag and Drop using flow

13. Analytics using Rapid Miner

14. Analytics using Rapid Miner

15. Analytics using other languages
Julia- faster than R http://julialang.org/
Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to
users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical
accuracy, and an extensive mathematical function library. The library, largely written in Julia itself, also integrates mature, best-of-
breed C and Fortran libraries for linear algebra, random number generation, signal processing, and string processing.

16. IJulia !!

17. IJulia !!

18. Analytics using other languages
Clojure- for JVM http://clojure.org/
Clojure is a dynamic programming language that targets the Java Virtual Machine . It is designed to be a general-purpose
language, combining the approachability and interactive development of a scripting language with an efficient and robust
infrastructure for multithreaded programming. Clojure is a compiled language - it compiles directly to JVM bytecode, yet remains
completely dynamic. Every feature supported by Clojure is supported at runtime. Clojure is a dialect of Lisp
https://bigml.com/gallery/models

19. Analytics using other languages
bigml.com (using clojure)
https://bigml.com/gallery/models

20. Analytics using other languages
Scala- for big data analytics http://www.scala-lang.org/
● A Scalable language
● Object-Oriented
● Functional
● Seamless Java Interop
● Functions are Objects
● Future-Proof
● Fun

21. Analytics using Jaspersoft
OLAP
BIG DATA
(offered through cloud, mobile)

22. Analytics using Pentaho
Basically Weka
Reporting as well
Complete BI and Analytics Stack

23. Weka

24. Hadoop
http://hadoop.apache.org/

25. Hadoop- evolving ecosystem

26. Hadoop- evolving ecosystem

27. Hadoop- evolving ecosystem

28. R
http://www.r-project.org/
Open Source
Free
5000+ Packages
Growing Faster
>2 million users
RAM constraints??

29. R
http://www.r-project.org/
Object Oriented
has GUI and IDE
has Commercial offerings

30. R
http://www.r-project.org/
Object Oriented
has GUI and IDE
has Commercial offerings

31. R - Rattle- Data Mining GUI
http://www.r-project.org/
Object Oriented
has GUI and IDE
has Commercial offerings

32. R - R Commander
http://www.r-project.org/
Object Oriented
has GUI and IDE
has Commercial offerings

33. R -R Studio

34. R -Revolution Analytics
Free for Academics
World Wide !!
RevoScaleR package
for Big Data
Recommended Install -
http://info.revolutionanalytics.com/free-academic.html

35. R -Revolution Analytics
Free for Academics
World Wide !!
RevoScaleR package
for Big Data

36. R -Big Data Packages
http://cran.r-project.org/web/views/HighPerformanceComputing.html
● The RHIPE package, started by Saptarshi Guha and now developed by a core team via GitHub, provides an interface
between R and Hadoop for analysis of large complex data wholly from within R using the Divide and Recombine approach
to big data. ( link )
● The rmr package by Revolution Analytics also provides an interface between R and Hadoop for a Map/Reduce
programming framework. ( link )
● A related package, segue package by Long, permits easy execution of embarassingly parallel task on Elastic Map Reduce
(EMR) at Amazon. ( link )
● The RProtoBuf package provides an interface to Google's language-neutral, platform-neutral, extensible mechanism for
serializing structured data. This package can be used in R code to read data streams from other systems in a distributed
MapReduce setting where data is serialized and passed back and forth between tasks.
● The HistogramTools package provides a number of routines useful for the construction, aggregation, manipulation, and
plotting of large numbers of Histograms such as those created by Mappers in a MapReduce application.

37. Terrific Data Mining using R GUI

38. Great Data Visualization using R GUI

39. So many packages- CRAN Views to
the rescue
http://cran.r-project.org/web/views/
Bayesian Bayesian Inference
ChemPhys Chemometrics and Computational Physics
ClinicalTrials Clinical Trial Design, Monitoring, and Analysis
Cluster Cluster Analysis & Finite Mixture Models
DifferentialEquations Differential Equations
Distributions Probability Distributions
Econometrics Computational Econometrics
Environmetrics Analysis of Ecological and Environmental Data
ExperimentalDesign Design of Experiments (DoE) & Analysis of Experimental Data
Finance Empirical Finance
Genetics Statistical Genetics
Graphics Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
HighPerformanceComputing High-Performance and Parallel Computing with R
MachineLearning Machine Learning & Statistical Learning
MedicalImaging Medical Image Analysis
MetaAnalysis Meta-Analysis
Multivariate Multivariate Statistics
NaturalLanguageProcessing Natural Language Processing

40. So many packages- CRAN Views to
the rescue
http://cran.r-project.org/web/views/
NumericalMathematics Numerical Mathematics
OfficialStatistics Official Statistics & Survey Methodology
Optimization Optimization and Mathematical Programming
Pharmacokinetics Analysis of Pharmacokinetic Data
Phylogenetics Phylogenetics, Especially Comparative Methods
Psychometrics Psychometric Models and Methods
ReproducibleResearch Reproducible Research
Robust Robust Statistical Methods
SocialSciences Statistics for the Social Sciences
Spatial Analysis of Spatial Data
SpatioTemporal Handling and Analyzing Spatio-Temporal Data
Survival Survival Analysis
TimeSeries Time Series Analysis
WebTechnologies Web Technologies and Services
gR gRaphical Models in R

41. R in the Browser
http://www.r-fiddle.org/#/
http://statace.com/
http://www.rstudio.com/ide/server/

42. R -Hadoop Packages
https://github.com/RevolutionAnalytics/RHadoop/wiki
● plyrmr - higher level plyr-like data processing for structured data, powered by rmr
● rmr - functions providing Hadoop MapReduce functionality in R
● rhdfs - functions providing file management of the HDFS from within R
● rhbase - functions providing database management for the HBase distributed database from within R
http://amplab-extras.github.io/SparkR-pkg/
SparkR is an R package that provides a light-weight frontend to use Apache Spark from R.
https://github.com/nexr/RHive
RHive is an R extension facilitating distributed computing via HIVE query. RHive allows easy usage of HQL(Hive SQL) in R, and
allows easy usage of R objects and R functions in Hive.

43. R - Cloud Computing
http://cran.r-project.org/web/views/WebTechnologies.html

44. R -Big Data Packages
http://cran.r-project.org/web/views/HighPerformanceComputing.html
Large memory and out-of-memory data
● The biglm package by Lumley uses incremental computations to offer lm() and glm() functionality to data sets stored
outside of R's main memory.
● The ff package by Adler et al. offers file-based access to data sets that are too large to be loaded into memory, along with
a number of higher-level functions.
● The bigmemory package by Kane and Emerson permits storing large objects such as matrices in memory (as well as via
files) and uses external pointer objects to refer to them. .
● A large number of database packages, and database-alike packages (such as sqldf by Grothendieck and data.table
● The HadoopStreaming package provides a framework for writing map/reduce scripts for use in Hadoop Streaming; it also
facilitates operating on data in a streaming fashion which does not require Hadoop.
● The speedglm package permits to fit (generalised) linear models to large data.
● The biglars package by Seligman et al can use the ff to support large-than-memory datasets for least-angle regression,
lasso and stepwise regression.
● The bigrf package provides a Random Forests implementation with support for parellel execution and large memory.
● The MonetDB.R package allows R to access the MonetDB column-oriented, open source database system as a backend.

45. Data Scientist Tool Kit
● web scraping
● visualization
● machine learning
● data mining
● modeling
● sna
● social media analytics
● web analytics
● reproducible research
● TS forecasting
● spatial analysis
● data storage
● data querying

46. Data Scientist Programming Skills
Java http://www.learnjavaonline.org/
Python http://www.codecademy.com/tracks/python
SQL http://www.w3schools.com/sql/
R http://bigdatauniversity.com/bdu-wp/bdu-course/introduction-to-data-analysis-using-r/
http://www.statmethods.net/
Hadoop http://hortonworks.com/hadoop-training/
Linuxhttps://github.com/WilliamHackmore/linuxgems/blob/master/cheat_sheet.org.sh

47. Other place to learn
MOOCs 1 https://www.edx.org/ 2 https://www.coursera.org/ 3 https://www.udacity.com/ 4 https://www.udemy.com/
Books
Courses
Workshops

48. Summary
Open source has greatly helped cut down cost
of software in analytics
The benefits of analytics continue to be many
Added with Big Data and Cloud and MOOCs
-----total cost to geeks is much lower !!

49. Thanks
Contact and Feedback-
ohri2007@gmail.com via http://linkedin.com/in/ajayohri