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Business Analytics with R

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Business Analytics with 'R' at Edureka will prepare you to perform analytics and build models for real world data science problems. It is the world’s most powerful programming language for statistical computing and graphics making it a must know language for the aspiring Data Scientists. 'R' wins strongly on Statistical Capability, Graphical capability, Cost and rich set of packages. The following blog will help you understand the significance of R Analytics training:
www.edureka.co/blog/r-training-first-step-to-become-a-data-scientist/

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Business Analytics with R

  1. 1. www.edureka.co/r-for-analytics View Business Analytics with R course details at www.edureka.co/r-for-analytics Business Analytics with R
  2. 2. www.edureka.co/r-for-analyticsSlide 2 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Objectives What is data mining What is Business Analytics Stages of Analytics / data mining What is R overview of Machine Learning  What is Clustering What is K-means Clustering Use-case At the end of this session, you will be able to
  3. 3. Slide 3Slide 3 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Data mining ?? Generally, data mining is the process of studying data from maximum possible dimensions and summarizing it into useful information Technically, data mining is the process of finding correlations or patterns among dozens of fields in large data generated from business Or you can say, data mining is the process finding useful information from the data and then devising knowledge out of it for improving future of our business » Data ?? Data are any facts, numbers, or text is getting produced by existing system » Information ?? The patterns, associations, or relationships among all this data can provide information » Knowledge ?? Information can be converted into knowledge about historical patterns and future trends. For example summary of sales in off season may help to start some offers in that period to increase sales
  4. 4. Slide 4Slide 4 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Business Analytics(BA) Refers to the skills, technologies, practices for iterative study and investigation of historical business data to gain insight and drive business planning Study of data through statistical and operations analysis Makes use of past data and statistical methods to understand business performance and hence makes us take necessary steps to improve it Injects intelligence into the business planning Intersection of business and technology
  5. 5. Slide 5Slide 5 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Business Analytics Why Business Analytics is getting popular these days ? Cost of storing data Cost of processing data
  6. 6. Slide 6Slide 6 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cross Industry standard Process for data mining ( CRISP – DM ) Stages of Analytics / Data Mining
  7. 7. Slide 7Slide 7 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Knowledge discovery and data mining ( KDD) Stages of Analytics / Data Mining
  8. 8. Slide 8Slide 8 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is R : Programming Language  You do data analysis in R by writing scripts and functions in the R programming language.  R has also quickly found the following because statisticians, engineers and scientists without computer programming skills find it easy to use. R is Programming Language
  9. 9. Slide 9Slide 9 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is R : Data Analysis Software  Data Scientists, Statisticians, Analysts, Quants, and others who need to make sense of data use R for statistical analysis, data visualization, and predictive modelling.  Rexer Analytics’s Annual Data Miner Survey is the largest survey of data mining, data science, and analytics professionals in the industry.  It has concluded that R's popularity has increased substantially in recent years. R is Data Analysis Software
  10. 10. Slide 10Slide 10 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is R : Environment for Statistical Analysis  R language consists of functions for almost every data manipulation, statistical model, or chart that a data analyst could ever need.  For statisticians, however, R is particularly useful because it contains a number of built-in mechanisms for organizing data, running calculations on the information and creating graphical representations of data sets. R is Environment for Statistical Analysis
  11. 11. Slide 11Slide 11 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions R : Characteristics Effective and fast data handling and storage facility A bunch of operators for calculations on arrays, lists, vectors etc A large integrated collection of tools for data analysis, and visualization Facilities for data analysis using graphs and display either directly at the computer or paper A well implemented and effective programming language called ‘S’ on top of which R is built A complete range of packages to extend and enrich the functionality of R
  12. 12. Slide 12Slide 12 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Data Visualization in R This plot represents the locations of all the traffic signals in the city. It is recognizable as Toronto without any other geographic data being plotted - the structure of the city comes out in the data alone.
  13. 13. Slide 13Slide 13 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Who Uses R : Domains  Telecom  Pharmaceuticals  Financial Services  Life Sciences  Education, etc
  14. 14. Slide 14Slide 14 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Machine Learning We have so many algorithms for data mining which can be used to build systems that can read past data and can generate a system that can accommodate any future data and derive useful insight from it Such set of algorithms comes under machine learning Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data Train data ML model Algorithms
  15. 15. Slide 15Slide 15 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Types of Learning Supervised Learning Unsupervised Learning 1. Uses a known dataset to make predictions. 2. The training dataset includes input data and response values. 3. From it, the supervised learning algorithm builds a model to make predictions of the response values for a new dataset. 1. Draw inferences from datasets consisting of input data without labeled responses. 2. Used for exploratory data analysis to find hidden patterns or grouping in data 3. The most common unsupervised learning method is cluster analysis. Machine Learning
  16. 16. www.edureka.co/r-for-analyticsSlide 16 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Common Machine Learning Algorithms Types of Learning Supervised Learning Unsupervised Learning Algorithms  Naïve Bayes  Support Vector Machines  Random Forests  Decision Trees Algorithms  K-means  Fuzzy Clustering  Hierarchical Clustering Gaussian mixture models Self-organizing maps
  17. 17. www.edureka.co/r-for-analyticsSlide 17 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions What is Clustering? Organizing data into clusters such that there is:  High intra-cluster similarity  Low inter-cluster similarity  Informally, finding natural groupings among objects http://en.wikipedia.org/wiki/Cluster_analysis
  18. 18. www.edureka.co/r-for-analyticsSlide 18 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions K-means Clustering
  19. 19. www.edureka.in/hadoopSlide 19 K-Means Clustering The process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group, but as much similar as possible within each group. The objects in group 1 should be as similar as possible. But there should be much difference between an object in group 1 and group 2. The attributes of the objects are allowed to determine which objects should be grouped together. Total population Group 1 Group 2 Group 3 Group 4
  20. 20. www.edureka.co/r-for-analyticsSlide 20 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions How it works 1. Given n object set, randomly initialize k cluster centers from the existing set 2. Assign the objects from the set to these randomly selected cluster center based on closets Euclidean distance from the center. 3. Set the position of each cluster to the mean of all data points belonging to that cluster 4. Repeat steps 2-3 until cluster center changes no more and cluster size remains constant
  21. 21. www.edureka.co/r-for-analyticsSlide 21 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions We have marks of 17 students in a class. Their ratings are : {1,2,2,4,5,6,6,7,8,10,10,11,11,12,13,13,13} Group the students in three categories i.e. good, average and bad. K-means example with one dimensional data
  22. 22. www.edureka.co/r-for-analyticsSlide 22 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Randomly initialize 3 cluster centers: Iteration 1 Good (centroid=3) Average (centroid=2) Bad (centroid=1) 4,5,6,6,7,8, 10,10,11,11, 12,13,13,13 2,2 1
  23. 23. www.edureka.co/r-for-analyticsSlide 23 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Iteration 1 summary Cluster 1 (Good): No 0f items = 14 Sum of items = 129 mean = 129/14 = 9 Cluster 1 (Average): No 0f items = 2 Sum of items = 4 mean = 4/2 = 2 Cluster 1 (Bad): No 0f items = 1 Sum of items = 1 mean = 1/1 = 1 Change detected Good Average Bad (centroid=9) (centroid=2) (centroid=1) New cluster center after iteration 1
  24. 24. www.edureka.co/r-for-analyticsSlide 24 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Good (centroid=9) Average (centroid=2) Bad (centroid=1) 6,6,7,8, 10,10,11,11, 12,13,13,13 2,2,4,5 1 Iteration 2
  25. 25. www.edureka.co/r-for-analyticsSlide 25 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cluster 1 (Good): No 0f items = 12 Sum of items = 120 mean = 120/12 = 10 Cluster 1 (Average): No 0f items = 4 Sum of items = 13 mean = 13/4= 3 Cluster 1 (Bad): No 0f items = 1 Sum of items = 1 mean = 1/1 = 1 Change detected Good Average Bad (centroid=10) (centroid=3) (centroid=1) New cluster center after iteration 2 Change detected Iteration 2 summary
  26. 26. www.edureka.co/r-for-analyticsSlide 26 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Good (centroid=10) Average (centroid=3) Bad (centroid=1) 7,8, 10,10,11,11, 12,13,13,13 6,6,2,2,4,5 1 Iteration 3
  27. 27. www.edureka.co/r-for-analyticsSlide 27 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cluster 1 (Good): No 0f items = 10 Sum of items = 108 mean = 108/11 = 11 Cluster 1 (Average): No 0f items = 6 Sum of items = 25 mean = 13/4= 4 Cluster 1 (Bad): No 0f items = 1 Sum of items = 1 mean = 1/1 = 1 Change detected Good Average Bad (centroid=11) (centroid=4) (centroid=1) New cluster center after iteration 3 Change detected Iteration 3 summary
  28. 28. www.edureka.co/r-for-analyticsSlide 28 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Good (centroid=11) Average (centroid=4) Bad (centroid=1) 8, 10,10,11,11, 12,13,13,13 7,6,6,4,5 1,2,2 Iteration 4 summary
  29. 29. www.edureka.co/r-for-analyticsSlide 29 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cluster 1 (Good): No 0f items = 9 Sum of items = 101 mean = 108/11 = 11 Cluster 1 (Average): No 0f items = 5 Sum of items = 28 mean = 28/5= 6 Cluster 1 (Bad): No 0f items = 3 Sum of items = 5 mean = 5/3 = 2 No Change detected Good Average Bad (centroid=11) (centroid=6) (centroid=2) New cluster center after iteration 4 Change detected Change detected Iteration 4 summary
  30. 30. www.edureka.co/r-for-analyticsSlide 30 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Good (centroid=11) Average (centroid=6) Bad (centroid=2) 10,10, 11,11, 12,13,13,13 8,7,6,6,4,5 1,2,2 Iteration 5
  31. 31. www.edureka.co/r-for-analyticsSlide 31 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cluster 1 (Good): No 0f items = 8 Sum of items = 93 mean = 93/8 = 12 Cluster 1 (Average): No 0f items = 6 Sum of items = 36 mean = 36/6= 6 Cluster 1 (Bad): No 0f items = 3 Sum of items = 5 mean = 5/3 = 2 Change detected Good Average Bad (centroid=12) (centroid=6) (centroid=2) New cluster center after iteration 5 No Change detected No Change detected Iteration 5 summary
  32. 32. www.edureka.co/r-for-analyticsSlide 32 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Good (centroid=12) Average (centroid=6) Bad (centroid=2) 10,10, 11,11, 12,13,13,13 8,7,6,6,4,5 1,2,2 Iteration 6
  33. 33. www.edureka.co/r-for-analyticsSlide 33 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Cluster 1 (Good): No 0f items = 8 Sum of items = 93 mean = 93/8 = 12 Cluster 1 (Average): No 0f items = 6 Sum of items = 36 mean = 36/6= 6 Cluster 1 (Bad): No 0f items = 3 Sum of items = 5 mean = 5/3 = 2 No Change detected Good Average Bad (centroid=12) (centroid=6) (centroid=2) New cluster center after iteration 6 No Change detected No Change detected Iteration 6 summary
  34. 34. www.edureka.co/r-for-analyticsSlide 34 Twitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions G O O d A v g B a d 10 10 11 11 12 13 13 13 4 5 6 6 7 8 1 2 2
  35. 35. Slide 35Slide 35 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Use Cases
  36. 36. Slide 36 www.edureka.co/r-for-analyticsTwitter @edurekaIN, Facebook /edurekaIN, use #AskEdureka for Questions Demo More Information on R setup and applications at: http://www.edureka.in/blog/category/business-analytics-with-r/
  37. 37. Slide 37 www.edureka.co/r-for-analytics  Module 1 » Introduction to Business Analytics  Module 2 » Introduction to R Programming  Module 3 » Data Manipulation in R  Module 4 » Data Import Techniques in R  Module 5 » Exploratory Data Analysis  Module 6 » Data Visualization in R Course Topics  Module 7 » Data mining: Clustering Techniques  Module 8 » Data Mining: Association rule mining and Sentiment analysis  Module 9 » Linear and Logistic Regression  Module 10 » Annova and Predictive Analysis  Module 11 » Data Mining: Decision Trees and Random forest  Module 12 » Final Project Business Analytics with R class – Census Data
  38. 38. Slide 38 www.edureka.co/r-for-analytics

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