This is a 6-week course for professionals who aspire to learn 'R' language for Analytics. Practical approach of learning has been followed in order to provide a real time experience and make you think like an analyst. Our course will cover not only the basic concepts but also the advanced concepts like Data Visualization, Data Mining, Model Building in R, Web Analytics and so on.

2. Business Analytics and Data Science

3. Meet your Instructor
• Author "R for Business Analytics"
• Founder "Decisionstats.com"
• University of Tennessee, Knoxville
MS (courses in statistics and
computer science)
• MBA (IIM Lucknow,India-2003)
• B.Engineering (DCE 2001)
http://linkedin.com/in/ajayohri

4. How it works?
• 6 weeks duration
• 2 hour live online sessions every Saturday and Sunday
• Total 8 hours per week = 4 hours (in class) + 4 hours (assignments and case studies)
• Project Work (~15 hours)
• 2 hour proctored Final Online Exam*
• All classes are recorded and recordings will be shared even in downloadable format
• All course material (ppt,pdfs,assignments,etc) will be shared as soft copy
• Life time access to the Learning Management System (LMS)**
*problem based exam **24 X 7 Online Support

5. Course Topics
• Week 1
- Introduction to Business Analytics
- Introduction to Data Science
- Introduction to R
- Data Import in R
- Introduction to Business Analytics
-
• Week 2
- Data Quality in R
- Data Manipulation in R
• Week 3
- Exploratory Data Analysis in R
- Data Visualization in R
• Week 4
- Data Mining in R (P1)
- Data Mining in R (P2)
• Week 5
- Understanding Model Building in R (P1)
- Understanding Model Building in R (P2)
• Week 6
- Advanced Topics in R
- Revision and Final Exam

6. Learning Objectives
By the end of this chapter,
• Know more on business analytics , data science and R
• Know more on the R language, community and ecosystem
• Understand how 'R' is being used in the industry
• Compare R with other software in analytics
• Install R and packages which are going to be used for the course
• Do basic operations in R using command line
• Learn how to use the IDE R Studio and Various GUI
• Use the R help
• Be introduced to how the worldwide R community collaborates
Introduction

7. Learning Objectives
By the end of this chapter,
• Know more on the R language, community and ecosystem
• Understand how 'R' is being used in the industry
• Compare R with other software in analytics
• Install R and packages which are going to be used for the course
• Do basic operations in R using command line
• Learn how to use the IDE R Studio and Various GUI
• Use the R help
• Be introduced to how the worldwide R community collaborates
Introduction To R

8. Business Analytics
• Definition
“Study of business data using statistical techniques and programming for creating decision support
and insights for achieving business goals”
• Various Types
• Who uses it? How?
• Across Domain
- Dashboards
- Models
• Across A Company
• Who creates it? How?
• Skills Needed
• Business Perception

9. Business Analytics
• Definition
“Study of business data using statistical techniques and programming for creating decision support
and insights for achieving business goals”
• Various Types
• Who uses it? How?
• Across Domain
- Dashboards
- Models
• Across A Company
• Who creates it? How?
• Skills Needed
• Business Perception

10. Business Intelligence

11. What Is Data Science?
Conway's Diagram
https://s3.amazonaws.com/aws.drewconway.com/viz/venn_diagram/data_science.html

12. Studying Data Science?
• Coursera Courses ( 26 Courses at
https://www.coursera.org/courses?orderby=upcoming&cats=stats
o Computational Methods for Data Analysis
https://www.coursera.org/course/compmethods
o Computing for Data Analysis
https://www.coursera.org/course/compdata
o Introduction to Data Science
https://www.coursera.org/course/datasci
o Machine Learning https://www.coursera.org/course/ml

13. Course Design
• Business Analytics - Understanding what
solution business needs
• Data Science
•- Primarily R programming skills
•- Some Applied Statistical Methods
• - Exposure to new domains and techniques

14. Course Design
• What you will learn?
• Data Visualization
• Data Mining Techniques
• Clustering
• Association Analysis
• Modeling ( including Regression)
• What is Demand Forecasting
• Data Manipulation

15. Course Design
• What you may learn a bit?
• Infographics
• Business Strategy Models
• Data Mining Techniques
• SVM and Decision Trees
• Neutral Nets and Ensemble Models
• Web Analytics and Social Media Analytics
• Social Network Analysis (SNA)

16. Course Design
• What you may NOT learn?

17. Course Methodology

18. Part 1: What Is Business Analytics?
• What are the problems in a business ?
• What are the tools that can be used?
• How are businesses solving these problems?
• What problems can be solved by MS Excel?
• What can’t be solved by MS Excel?

19. Part 1: What Is Business Analytics?
• What is Business Analytics
• What problems does it solve?
• History of Business Analytics.
• What are the different kinds of Business Analytics?
• What is R? What is SAS?
• How are they different?

20. Part 1: What Is Business Analytics?

21. • Part 1
- What is R?
• Part 2
- Corporate usage for R
• Part 3
- Comparing R
• Part 4
- Installation of R and Packages
• Part 5
- Basics of R - Command Line
• Part 6
- RStudio IDE and GUI
• Part 7
- Help and Documentation in R
• Part 8
- Interacting with community in R
Topics: Introduction To R

22. Part 1: What Is R?
www.r-project.org/

23. Part 1: What Is R?
www.r-project.org/about.html
History Open Source Official Website R Journal
Evolution Free R Core
Current State Widely Recognized Creators

24. Part 2: Corporate Usage of R?

25. Part 2: Corporate Usage of R?

26. Part 2: Corporate Usage of R?
Did you know Oracle creates a version of R?
http://www.oracle.com/technetwork/topics/bigdata/r-offerings-1566363.html

27. Part 2: Corporate Usage of R?
Did you know SAP uses R for analyzing it’s HANA database?
http://help.sap.com/hana/hana_dev_r_emb_en.pdf

28. Part 2: Corporate Usage of R?
Did you know SAS Institute views R as an Opportunity?
http://support.sas.com/rnd/app/studio/Rinterface2.html

29. Part 2: Corporate Usage of R?
Did you know SAS Institute views R as an Opportunity?
http://support.sas.com/rnd/app/studio/Rinterface2.html

30. Part 2: Corporate Usage of R?
Did you know SAS Institute views R as an Opportunity?
http://www.jmp.com/support/help/Working_with_R.shtml

31. Part 2: Corporate Usage of R?
Did you know Teradata uses R for in-database analytics?
http://developer.teradata.com/applications/articles/in-database-analytics-with-teradata-r
http://downloads.teradata.com/download/applications/teradata-r

32. Part 2: Corporate Usage of R?
Did you know IBM uses and even teaches R for High-end Analytics?
http://www-304.ibm.com/jct03001c/services/learning/ites.wss/us/en?pageType=course_description&courseCode=DW540

33. Part 2: Corporate Usage of R?
• Telecom
• Pharmaceuticals
• Financial Services
• Life sciences, etc

34. Corporate Clients of R
http://www.revolutionanalytics.com/aboutus/our-customers.php
Part 2: Corporate Usage of R?

35. Part 3: Comparing R

36. but
R is open source and free R has a steep learning curve
Part 3: Comparing R
R has lots of packages
R can be customized
R has the most advanced graphics
R has GUI to help make learning easier
R can connect to many database and
data types
multiple packages and ways to do the
same thing
by default stores memory in RAM
you need much better programming skills
customization needs command line
you need to know which package to use

37. Comparing R and Base SAS* /SAS Stat*
R is open source and free Base SAS* , SAS/Stat*, SAS/ET*,
SAS/OR*, SAS/Graph* are expensive
relatively because of annual licenses
Part 3: Comparing R
Open source R has support from
email lists, twitter, stack overflow
R is slower on the desktop than base
SAS for datasets ~4-5 gb
R has much much better graphics
You can create custom functions in
R easily
R has multiple GUI that are free
*Copyright © 2012 SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513, USA. All rights reserved.
SAS Institute* products have dedicated
support and extensive documentation
by default R stores memory in
RAM,so we can use the cloud
you need much better programming
skills
Customization needs command line
SAS GUI are more expensive
but

38. Comparing R and others
http://r4stats.com/articles/popularity/
Part 3: Comparing R

39. http://cran.r-project.org/
http://cran.r-project.org/bin/windows/Rtools/
Part 4: Installation of R and Packages

40. Part 4: Installation of R and Packages
http://cran.r-project.org/

41. Part 4: Installation of R and Packages
http://cran.r-project.org/
http://cran.r-project.org/bin/windows/Rtools/

42. Part 4: Installation of R and Packages
http://cran.r-project.org/
http://cran.r-project.org/bin/windows/Rtools/

43. R and R Packages
Part 4: Installation of R and Packages

44. Part 4: Installation of R and Packages
R and R Packages

45. Part 4: Installation of R and Packages
R and R Packages

46. Part 4: Installation of R and Packages
R and R Packages

47. Part 4: Installation of R and Packages
R and R Packages

48. Part 5: Basics Of R - Command Line
Basics of R - Command Line

49. Part 5: Basics Of R - Command Line
Basics of R - Command Line

50. Basics of R - Command Line
Part 5: Basics Of R - Command Line

51. Part 6: RStudio IDE and GUI
Installation of RStudio

52. Installation of RStudio
Part 6: RStudio IDE and GUI

53. RCommander - Graphical User Interface (GUI)
http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/
Part 6: RStudio IDE and GUI

54. Part 6: RStudio IDE and GUI
RCommander - Graphical User Interface (GUI)
RcmdrPlugin.IPSUR An IPSUR Plugin for the R Commander
RcmdrPlugin.KMggplot2 Rcmdr Plug-In for Kaplan-Meier Plots and Other Plots Using the ggplot2 Package
RcmdrPlugin.mosaic Adds menu items to produce mosaic plots and assoc plots to Rcmdr
RcmdrPlugin.MPAStats R Commander Plug-in for MPA Statistics
RcmdrPlugin.orloca orloca Rcmdr Plug-in
RcmdrPlugin.plotByGroup Rcmdr plots by group using lattice
RcmdrPlugin.qcc Rcmdr qcc Plug-In
RcmdrPlugin.qual Rcmdr plugin for quality control course
RcmdrPlugin.SCDA Rcmdr plugin for designing and analyzing single-case experiments

55. Part 6: RStudio IDE and GUI
RCommander - Graphical User Interface (GUI)
RcmdrPlugin.seeg Rcmdr Plugin for seeg
RcmdrPlugin.SLC SLC Rcmdr Plug-in
RcmdrPlugin.SM Rcmdr Sport Management Plug-In
RcmdrPlugin.StatisticalURV Statistical URV Rcmdr Plug-In
RcmdrPlugin.steepness Steepness Rcmdr Plug-in
RcmdrPlugin.survival R Commander Plug-in for the survival Package
RcmdrPlugin.TeachingDemos Rcmdr Teaching Demos Plug-In
RcmdrPlugin.temis Graphical user interface providing an integrated text mining solution
RcmdrPlugin.UCA UCA Rcmdr Plug-in

56. RcmdrPlugin.BCA Rcmdr Plug-In for Business and Customer Analytics
RcmdrPlugin.coin Rcmdr Coin Plug-In
RcmdrPlugin.depthTools R commander Depth Tools Plug-In
RcmdrPlugin.doBy Rcmdr doBy Plug-In
RcmdrPlugin.DoE R Commander Plugin for (industrial) Design of Experiments
RcmdrPlugin.doex Rcmdr plugin for Stat 4309 course
RcmdrPlugin.EACSPIR Plugin de R-Commander para el manual EACSPIR
RcmdrPlugin.EBM Rcmdr Evidence Based Medicine Plug-In package
RcmdrPlugin.epack Rcmdr plugin for time series
RcmdrPlugin.EZR R Commander Plug-in for the EZR (Easy R) Package
RcmdrPlugin.HH Rcmdr support for the HH package
Part 6: RStudio IDE and GUI
RCommander - Graphical User Interface (GUI)

57. Deducer - GUI for Data Visualization
http://deducer.org/
Part 6: RStudio IDE and GUI

58. Part 6: RStudio IDE and GUI
Deducer - GUI for Data Visualization
http://www.deducer.org/pmwiki/index.php?n=Main.Development
DeducerExtras : An add-on package containing a variety of additional analysis dialogs. These include:
Distribution quantiles, single/multiple sample proportion tests, paired t-test, Wilcoxon signed rank test,
Levene's test, Bartlett's test, k-means clustering, Hierarchical clustering, factor analysis, and multi-
dimensional scaling
DeducerPlugInScaling : Reliability and factor analysis
DeducerMMR : Moderated multiple regression and simple slopes analysis
DeducerSpatial : A GUI for Spatial Data Analysis and Visualization
gMCP : (Experimental) A graphical approach to sequentially rejective multiple test procedures
RGG : (Experimental) A GUI Generator
DeducerText : (Experimental) Text Mining
Extension Packages

59. Rattle - GUI for Data Mining
http://rattle.togaware.com/
Part 6: RStudio IDE and GUI

60. Part 7: Help And Documentation In R
Use ? or ??

61. Part 7: Help And Documentation In R
Use ? or ??

62. Part 7: Help And Documentation In R
Use ? or ??
http://stat.ethz.ch/R-manual/R-patched/library/stats/html/lm.html

63. Part 7: Help And Documentation In R
Task Views
http://cran.r-project.org/web/views/

64. ML Task View
Part 7: Help And Documentation In R

65. R Community on Twitter #rstats
Part 8: Interacting With Community In R

66. Part 8: Interacting With Community In R
R Community on Email
http://www.r-project.org/mail.html

67. Part 8: Interacting With Community In R
R Community on Stack Overflow [r]

68. Part 8: Interacting With Community In R
R Community on Cross Validated [r]

69. Part 8: Interacting With Community In R
R Community On Blogs
http://www.r-bloggers.com/

70. Github - Social Coding
Part 8: Interacting With Community In R

71. Part 8: Interacting With Community In R
Github - Social Coding

72. Thank You
See You in Class Next Week