Data scientists are big data wranglers. They take an enormous mass of messy data points and use their formidable skills in math, statistics and programming to clean, massage and organize them.

- Work on data exploration, data visualization, and predictive modeling techniques with ease.
- Gain fundamental knowledge on analytics and how it assists with decision making.
- Work with confidence using the R language.
- Understand and work on statistical concepts like linear & logistic regression, cluster analysis, and forecasting.
- Develop a structured approach to use statistical techniques and R language.
- Perform sharp data analysis to make business decisions.

1. Introduction to Course

2. Course Curriculum

3. What is Data Science?

4. How to Get Help in the Course!

5. Installation and Set-Up

6. Windows Installation Procedure

7. Development Environment Overview

8. Course Notes

9. Introduction to R Basics

10. Arithmetic in R

11. Variables

12. R Basic Data Types

13. Vector Basics

14. Vector Operations

15. Vector Indexing and Slicing

16. Getting Help with R and RStudio

17. Comparison Operators

18. R Basics Training Exercise

19. R Basics Training Exercise - Solutions Walkthrough

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

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

39. List Basics

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

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

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

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

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

78. Data Visualization Project

81. Overview of Plotly and Interactive Visualizations

82. Resources for Plotly and ggplot2

83. Qualitative Data

84. Frequency Distribution of Qualitative Data

85. Relative Frequency Distribution of Qualitative Data

86. Bar Graph

87. Pie Chart

88. Category Statistics

89. Frequency Distribution of Quantitative Data

90. Histogram

91. Relative Frequency Distribution of Quantitative Data

92. Cumulative Frequency Distribution

93. Cumulative Frequency Graph

94. Cumulative Relative Frequency Distribution

95. Cumulative Relative Frequency Graph

96. Stem-and-Leaf Plot

97. Scatter Plot

98. Mean

99. Median

100. Quartile

101. Percentile

102. Range

103. Interquartile Range

104. Box Plot

105. Variance

106. Standard Deviation

107. Covariance

108. Correlation Coefficient

109. Central Moment

110. Skewness

111. Kurtosis

112. Binomial Distribution

113. Poisson Distribution

114. Continuous Uniform Distribution

115. Exponential Distribution

116. Normal Distribution

117. Chi-squared Distribution

118. Student t Distribution

119. F Distribution

120. Using Base R to Generate Statistical Indicators

121. Descriptive Statistics with the psych Package

122. Descriptive Statistics with the pastecs Package

123. Determining the Skewness and Kurtosis

124. Computing Quantiles

125. Determining the Mode

126. Getting the Statistical Indicators by Group with DoBy

127. Getting the Statistical Indicators by Group with DescribeBy

128. Getting the Statistical Indicators by Group with stats

129. Frequency Tables in Base R

130. Frequency Tables with plyr

131. Building Cross Tables using xtabs

132. Building Cross Tables with CrossTable

133. Histograms

134. Cumulative Frequency Line Charts

135. Column Charts

136. Mean Plot Charts

137. Scatterplot Charts

138. Boxplot Charts

139. Checking the Normality Assumption - Numerical Method

140. Checking the Normality Assumption - Graphical Methods

141. Detecting the Outliers

142. One-Sample T Test

143. Binomial Test

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

145. Getting Started

146. Writing your first query

147. Filters and Operands

148. Aggregate Functions

149. Grouping Aggregate Data with Group BY

150. rder By and Limit

151. Conditional Filtering with Case Statements

152. Comparisons using LIKE

153. Filtering the output of a query using HAVING

154. Joining tables together

155. Nested Queries

156. Working with Dates

157. Introduction to Machine Learning

158. Introduction to Linear Regression

159. Linear Regression with R - Part 1

Machine Learning Project - Linear Regression

160. Introduction to Linear Regression Project

161. ML - Linear Regression Project - Solutions Part 1

162. Introduction to Logistic Regression

163. Logistic Regression with R - Part 1

Machine Learning Project - Logistic Regression

164. Introduction to Logistic Regression Project

165. Logistic Regression Project Solutions - Part 1

166. Introduction to K Nearest Neighbors

167. K Nearest Neighbors with R

Machine Learning Project - K Nearest Neighbors

168. Introduction K Nearest Neighbors Project

169. K Nearest Neighbors Project Solutions

170. Introduction to Tree Methods

171. Decision Trees and Random Forests with R

Machine Learning Project - Decision Trees and Random Forests

172. Introduction to Decision Trees and Random Forests Project

173. Tree Methods Project Solutions - Part 1

174. Introduction to Support Vector Machines

175. Support Vector Machines with R

Machine Learning Project - Support Vector Machines

176. Introduction to SVM Project

177. Support Vector Machines Project - Solutions Part 1

178. Introduction to K-Means Clustering

179. K Means Clustering with R

Machine Learning Project - K-means Clustering

180. Introduction to K Means Clustering Project

181. K Means Clustering Project - Solutions Walkthrough

182. Introduction to Natural Language Processing

183. Natural Language Processing with R - Part 1

184. Natural Language Processing with R - Part 2

185. Introduction to Neural Nets

186. Neural Nets with R

Machine Learning Project - Neural Nets

187. Introduction to Neural Nets Project

188. Neural Nets Project - Solutions

Module | Mastering on Data Science and Machine Learning using R | 32 Hrs |