Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – industry knowledge, contextual understanding, skepticism of existing assumptions – to uncover hidden solutions to business challenges. <**What is this course about?**

This Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.**Who will benefit from this course?**

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'

2. Analytics Managers who are leading a team of analysts

3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics

4. Business Analysts who want to understand Machine Learning (ML) Techniques

5. Information Architects who want to gain expertise in Predictive Analytics

6. 'R' professionals who want to captivate and analyze Big Data

7. Hadoop Professionals who want to learn R and ML techniques

8. Analysts wanting to understand Data Science methodologies**After completion of this training course, you will be able to: **

This training has a clear focus on the vital concepts of business analytics and R. By the end of the training, participants will be able to:

1. Work on data exploration, data visualization, and predictive modeling techniques with ease.

2. Gain fundamental knowledge on analytics and how it assists with decision making.

3. Work with confidence using the R language.

4. Understand and work on statistical concepts like linear & logistic regression, cluster analysis, and forecasting.

5. Develop a structured approach to use statistical techniques and R language.

6. Perform sharp data analysis to make business decisions.

The pre-requisites for learning 'Mastering Data Analytics with R' include basic statistics knowledge. We provide a complimentary course "Statistics Essentials for R" to all the participants who enroll for the Data Analytics with R Training. This course helps you brush up your statistics skills.

Yes, the course presents both the business and technical benefits of Big Data analytics and Data Visualization. The data mining and technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.

Towards the end of the Course, you will be working on a live project. Here are the few Industry-wise case studies e.g. Finance, Retail, Media, Aviation, Sports etc. which you can take up as your project work:

Industry : Aviation

Description : The goal of this project is to predict the Arrival Time of a flight given the parameters like:"UniqueCarrier", "DepDelay", "AirTime", "Distance", "ArrDelay", etc. Whether these attributes affect the arrival delay and if yes, to which extent? Construct a model and predict the arrival delay.Compute the (Source Airport - Destination Airport) mean scheduled time, actual and inflight time with the help of MapReduce in R and visualize the results using R.

Industry : Finance

Description : This problem is about making predictions on the stock market data.The dataset contains the daily quotes of the SP500 stock index from 1970-01-02 to 2009-09-15 (10,000+ daily sessions). For each day information is given on the Open, High, Low and Close prices, and also for the Volume and adjusted close price.

Industry : Social Media

Description : This problem is about social media analytics. This can be defined as Measuring, Analyzing, and Interpreting interactions and associations between people, topics and ideas. The dataset to be analyzed is captured by Live Twitter Streaming. This problem is mainly about how to use twitter analytics to find meaningful data by performing Sentiment analysis of the tweets obtained and visualizing the conclusions.

Industry : e-commerce

Description : The problem of creating recommendations given a large data set from directly elicited ratings is a widely potential area which was lately boosted by players like Amazon, Netflix, Google to name a few. In this project, you are given a collection of real world data from the different users involving the products they like, rating assigned to the product, etc. and you have to create and come up with recommendations for the users.

Industry : Sports

Description : The dataset is a set of tweets by fans from a NFL game. This project is about analyzing the tweets posted by football fans all over the world on the NFL tournament semi-finals and find out insights like: top 10 most popular topics being discussed, most talked about team etc.

Course Introduction

1. Introduction to Course

2. Course Curriculum

3. What is Data Science?

4. Course FAQ

Course Best Practices

5. How to Get Help in the Course!

Quiz 1: Welcome to the Course.

6. Installation and Set-Up

Windows Installation Set-Up

7. Windows Installation Procedure

Development Environment Overview

10. Development Environment Overview

11. Course Notes

Introduction to R Basics

13. Introduction to R Basics

14. Arithmetic in R

15. Variables

16. R Basic Data Types

17. Vector Basics

18. Vector Operations

19. Vector Indexing and Slicing

20. Getting Help with R and RStudio

21. Comparison Operators

22. R Basics Training Exercise

23. R Basics Training Exercise - Solutions Walkthrough

R Matrices

24. Introduction to R Matrices

25. Creating a Matrix

26. Matrix Arithmetic

27. Matrix Operations

28. Matrix Selection and Indexing

29. Factor and Categorical Matrices

30. Matrix Training Exercise

31. Matrix Training Exercises - Solutions Walkthrough

R Data Frames

32. Introduction to R Data Frames

33. Data Frame Basics

34. Data Frame Indexing and Selection

35. Overview of Data Frame Operations - Part 1

36. Overview of Data Frame Operations - Part 2

37. Data Frame Training Exercise

38. Data Frame Training Exercises - Solutions Walkthrough

R Lists

39. List Basics

Data Input and Output with R

40. Introduction to Data Input and Output with R

41. CSV Files with R

42. Excel Files with R

43. SQL with R

44. Web Scraping with R

R Programming Basics

45. Introduction to Programming Basics

46. Logical Operators

47. if, else, and else if Statements

48. Conditional Statements Training Exercise

49. Conditional Statements Training Exercise - Solutions Walkthrough

50. While Loops

51. For Loops

52. Functions

53. Functions Training Exercise

54. Functions Training Exercise - Solutions

Advanced R Programming

55. Introduction to Advanced R Programming

56. Built-in R Features

57. Apply

58. Math Functions with R

59. Regular Expressions

60. Dates and Timestamps

Data Manipulation with R

61. Data Manipulation Overview

62. Guide to Using Dplyr

63. Guide to Using Dplyr - Part 2

64. Pipe Operator

65. Dplyr Training Exercise

66. Dplyr Training Exercise - Solutions Walkthrough

67. Guide to Using Tidyr

Data Visualization with R

68. Overview of ggplot2

69. Histograms

70. Scatterplots

71. Barplots

72. Boxplots

73. 2 Variable Plotting

74. Coordinates and Faceting

75. Themes

76. ggplot2 Exercises

77. ggplot2 Exercise Solutions

Data Visualization Project

78. Data Visualization Project

79. Data Visualization Project - Solutions Walkthrough - Part 1

80. Data Visualization Project Solutions Walkthrough - Part 2

Interactive Visualizations with Plotly

81. Overview of Plotly and Interactive Visualizations

82. Resources for Plotly and ggplot2

Capstone Data Project

83. Introduction to Capstone Project

84. Capstone Project Solutions Walkthrough

Introduction to Machine Learning with R

85. Introduction to Machine Learning

Machine Learning with R - Linear Regression

86. Introduction to Linear Regression

87. Linear Regression with R - Part 1

88. Linear Regression with R - Part 2

89. Linear Regression with R - Part 3

Machine Learning Project - Linear Regression

90. Introduction to Linear Regression Project

91. ML - Linear Regression Project - Solutions Part 1

92. ML - Linear Regression Project - Solutions Part 2

Machine Learning with R - Logistic Regression

93. Introduction to Logistic Regression

94. Logistic Regression with R - Part 1

95. Logistic Regression with R - Part 2

Machine Learning Project - Logistic Regression

96. Introduction to Logistic Regression Project

97. Logistic Regression Project Solutions - Part 1

98. Logistic Regression Project Solutions - Part 2

99. Logistic Regression Project - Solutions Part 3

Machine Learning with R - K Nearest Neighbors

100. Introduction to K Nearest Neighbors

101. K Nearest Neighbors with R

Machine Learning Project - K Nearest Neighbors

102. Introduction K Nearest Neighbors Project

103. K Nearest Neighbors Project Solutions

Machine Learning with R - Decision Trees and Random Forests

104. Introduction to Tree Methods

105. Decision Trees and Random Forests with R

Machine Learning Project - Decision Trees and Random Forests

106. Introduction to Decision Trees and Random Forests Project

107. Tree Methods Project Solutions - Part 1

108. Tree Methods Project Solutions - Part 2

Machine Learning with R - Support Vector Machines

109. Introduction to Support Vector Machines

110. Support Vector Machines with R

Machine Learning Project - Support Vector Machines

111. Introduction to SVM Project

112. Support Vector Machines Project - Solutions Part 1

113. Support Vector Machines Project - Solutions Part 2

114. Introduction to K-Means Clustering

115. K Means Clustering with R

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Machine Learning Project - K-means Clustering

116. Introduction to K Means Clustering Project

117. K Means Clustering Project - Solutions Walkthrough

Machine Learning with R - Natural Language Processing

118. Introduction to Natural Language Processing

119. Natural Language Processing with R - Part 1

120. Natural Language Processing with R - Part 2

Machine Learning with R - Neural Nets

121. Introduction to Neural Nets

122. Neural Nets with R

Machine Learning Project - Neural Nets

123. Introduction to Neural Nets Project

124. Neural Nets Project - Solutions

Statistics

Introduction

125. Qualitative Data

126. Frequency Distribution of Qualitative Data

127. Relative Frequency Distribution of Qualitative Data

128. Bar Graph

129. Pie Chart

130. Category Statistics

131. Frequency Distribution of Quantitative Data

132. Histogram

133. Relative Frequency Distribution of Quantitative Data

134. Cumulative Frequency Distribution

135. Cumulative Frequency Graph

136. Cumulative Relative Frequency Distribution

137. Cumulative Relative Frequency Graph

138. Stem-and-Leaf Plo

t 139. Scatter Plot

140. Mean

141. Median

142. Quartile

143. Percentile

144. Range

145. Interquartile Range

146. Box Plot

147. Variance

148. Standard Deviation

149. Covariance

150. Correlation Coefficient

151. Central Moment

152. Skewness

153. Kurtosis

Probability Distributions

154. Binomial Distribution

155. Poisson Distribution

156. Continuous Uniform Distribution

157. Exponential Distribution

158. Normal Distribution

159. Chi-squared Distribution

160. Student t Distribution

161. F Distribution

162. Using Base R to Generate Statistical Indicators

163. Descriptive Statistics with the psych Package

164. Descriptive Statistics with the pastecs Package

165. Determining the Skewness and Kurtosis

166. Computing Quantiles

167. Determining the Mode

168. Getting the Statistical Indicators by Group with DoBy

169. Getting the Statistical Indicators by Group with DescribeBy

170. Getting the Statistical Indicators by Group with stats

Creating Frequency Tables and Cross Tables

171. Frequency Tables in Base R

172. Frequency Tables with plyr

173. Building Cross Tables using xtabs

174. Building Cross Tables with CrossTable

Building Charts

175. Histograms

176. Cumulative Frequency Line Charts

177. Column Charts

178. Mean Plot Charts

179. Scatterplot Charts

180. Boxplot Charts

Checking Assumptions

181. Checking the Normality Assumption - Numerical Method

182. Checking the Normality Assumption - Graphical Methods

183. Detecting the Outliers

Performing Univariate Analyses

184. One-Sample T Test

185. Binomial Test

186. Chi-Square Test for Goodness-of-Fit

DataBase

Data Extraction, Filtering, and Aggregation

187. Getting Started

188. Writing your first query

189. Filters and Operands

190. Aggregate Functions

191. Grouping Aggregate Data with Group BY

Sorting, Conditional Filtering and Fuzzy Comparisons

192. rder By and Limit

193. Conditional Filtering with Case Statements

194. Comparisons using LIKE

195. Filtering the output of a query using HAVING

Multiple Tables and Dates

196. Joining tables together

197. Nested Queries

198. Working with Dates

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