1. edureka! BusinessAnalytics With R

2. Business Analytics and Data Science

3. How it works?
• 6 weeks duration
• 2 hour live online sessions every Saturday and Sunday
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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
Class 1
 Introduction to Business Analytics
 Introduction to Data Science
 Introduction to R
Class 2


Class 3

Class 4

Class 5

Class 6

Data Import in R
Introduction to Business Analytics
Data Quality in R
Data Manipulation in R
Exploratory Data Analysis in R
Data Visualization in R
Class 7
 Data Mining in R (P1)
Class 8
 Data Mining in R (P2)
Class 9
 Understanding Model Building in R (P1)
Class 10
 Understanding Model Building in R (P2)
Class 11
 Advanced Topics in R
Class 12
 Revision and Final Exam

5. Introduction
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

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

7. Business Intelligence

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

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

10. 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

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

12. 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)

13. Course Design
• What you may NOT learn?

14. Course Methodology

15. 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?

16. 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?

17. Part 1: What Is Business Analytics?

18. • 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

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

20. 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

21. Part 2: Corporate Usage of R?

22. Part 2: Corporate Usage of R?

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

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

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

32. Part 3: Comparing R

33. 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

34. 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
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
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.
Customization needs command line
SAS GUI are more expensive
but

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

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

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

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

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

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

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

42. Part 4: Installation of R and Packages
R and R 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 5: Basics Of R - Command Line
Basics of R - Command Line

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

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

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

49. Thank You
See You in Class Next Week