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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 …

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.

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  • 1. Business Analytics and Data Science
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. 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
  • 7. 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
  • 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 Intelligence
  • 10. What Is Data Science? Conway's Diagram https://s3.amazonaws.com/aws.drewconway.com/viz/venn_diagram/data_science.html
  • 11. 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
  • 12. 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
  • 13. Course Design • What you will learn? • Data Visualization • Data Mining Techniques • Clustering • Association Analysis • Modeling ( including Regression) • What is Demand Forecasting • Data Manipulation
  • 14. 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)
  • 15. Course Design • What you may NOT learn?
  • 16. Course Methodology
  • 17. 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?
  • 18. 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?
  • 19. Part 1: What Is Business Analytics?
  • 20. • 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
  • 21. Part 1: What Is R? www.r-project.org/
  • 22. 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
  • 23. Part 2: Corporate Usage of R?
  • 24. Part 2: Corporate Usage of R?
  • 25. 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
  • 26. 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
  • 27. 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
  • 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://www.jmp.com/support/help/Working_with_R.shtml
  • 30. 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
  • 31. 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
  • 32. Part 2: Corporate Usage of R? • Telecom • Pharmaceuticals • Financial Services • Life sciences, etc
  • 33. Corporate Clients of R http://www.revolutionanalytics.com/aboutus/our-customers.php Part 2: Corporate Usage of R?
  • 34. Part 3: Comparing R
  • 35. 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
  • 36. 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
  • 37. Comparing R and others http://r4stats.com/articles/popularity/ Part 3: Comparing R
  • 38. http://cran.r-project.org/ http://cran.r-project.org/bin/windows/Rtools/ Part 4: Installation of R and Packages
  • 39. Part 4: Installation of R and Packages http://cran.r-project.org/
  • 40. Part 4: Installation of R and Packages http://cran.r-project.org/ http://cran.r-project.org/bin/windows/Rtools/
  • 41. Part 4: Installation of R and Packages http://cran.r-project.org/ http://cran.r-project.org/bin/windows/Rtools/
  • 42. R and R Packages Part 4: Installation of R and Packages
  • 43. Part 4: Installation of R and Packages R and R 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 5: Basics Of R - Command Line Basics of R - Command Line
  • 48. Part 5: Basics Of R - Command Line Basics of R - Command Line
  • 49. Basics of R - Command Line Part 5: Basics Of R - Command Line
  • 50. Part 6: RStudio IDE and GUI Installation of RStudio
  • 51. Installation of RStudio Part 6: RStudio IDE and GUI
  • 52. RCommander - Graphical User Interface (GUI) http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ Part 6: RStudio IDE and GUI
  • 53. 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
  • 54. 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
  • 55. 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)
  • 56. Deducer - GUI for Data Visualization http://deducer.org/ Part 6: RStudio IDE and GUI
  • 57. 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
  • 58. Rattle - GUI for Data Mining http://rattle.togaware.com/ Part 6: RStudio IDE and GUI
  • 59. Part 7: Help And Documentation In R Use ? or ??
  • 60. Part 7: Help And Documentation In R Use ? or ??
  • 61. Part 7: Help And Documentation In R Use ? or ?? http://stat.ethz.ch/R-manual/R-patched/library/stats/html/lm.html
  • 62. Part 7: Help And Documentation In R Task Views http://cran.r-project.org/web/views/
  • 63. ML Task View Part 7: Help And Documentation In R
  • 64. R Community on Twitter #rstats Part 8: Interacting With Community In R
  • 65. Part 8: Interacting With Community In R R Community on Email http://www.r-project.org/mail.html
  • 66. Part 8: Interacting With Community In R R Community on Stack Overflow [r]
  • 67. Part 8: Interacting With Community In R R Community on Cross Validated [r]
  • 68. Part 8: Interacting With Community In R R Community On Blogs http://www.r-bloggers.com/
  • 69. Github - Social Coding Part 8: Interacting With Community In R
  • 70. Part 8: Interacting With Community In R Github - Social Coding
  • 71. Thank You See You in Class Next Week