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.

**This training is jointly organized by BITM & Business Accelerate BD Ltd.Training will be held in Business Accelerate BD Ltd. Course outline :**

Section: 1

**Course Introduction**

1. Introduction to Course

2. Course Curriculum

3. What is Data Science?

4. Course FAQ

**Section: 2**

**Course Best Practices**

5. How to Get Help in the Course!

Quiz 1: Welcome to the Course.

6. Installation and Set-Up

Section: 3

**Windows Installation Set-Up**

7. Windows Installation Procedure

**Section: 4**

**Development Environment Overview**

10. Development Environment Overview

11. Course Notes

**Section: 5**

**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

**Section: 6**

**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

**Section: 7**

**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

**Section: 8**

**R Lists**

39. List Basics

**Section: 9**

**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

**Section: 10**

**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

**Section: 11**

**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

**Section: 12**

**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

**Section: 13**

**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

**Section: 14**

**Data Visualization Project**

78. Data Visualization Project

79. Data Visualization Project - Solutions Walkthrough - Part 1

80. Data Visualization Project Solutions Walkthrough - Part 2

**Section: 15**

**Interactive Visualizations with Plotly**

81. Overview of Plotly and Interactive Visualizations

82. Resources for Plotly and ggplot2

**Section: 16**

**Capstone Data Project**

83. Introduction to Capstone Project

84. Capstone Project Solutions Walkthrough

**Section: 17**

**Introduction to Machine Learning with R**

85. Introduction to Machine Learning

**Section: 18**

**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

**Section: 19**

**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

**Section: 20**

**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

**Section: 21**

**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

**Section: 22**

**Machine Learning with R - K Nearest Neighbors**

100. Introduction to K Nearest Neighbors

101. K Nearest Neighbors with R

**Section: 23**

**Machine Learning Project - K Nearest Neighbors**

102. Introduction K Nearest Neighbors Project

103. K Nearest Neighbors Project Solutions

**Section: 24**

**Machine Learning with R - Decision Trees and Random Forests**

104. Introduction to Tree Methods

105. Decision Trees and Random Forests with R

**Section: 25**

**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

**Section: 26**

**Machine Learning with R - Support Vector Machines**

109. Introduction to Support Vector Machines

110. Support Vector Machines with R

**Section: 27**

**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

**Section: 28**

Machine Learning with R - K-means Clustering

114. Introduction to K-Means Clustering

115. K Means Clustering with R

**Section: 29**

**Machine Learning Project - K-means Clustering**

116. Introduction to K Means Clustering Project

117. K Means Clustering Project - Solutions Walkthrough

**Section: 30**

**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

**Section: 31**

**Machine Learning with R - Neural Nets**

121. Introduction to Neural Nets

122. Neural Nets with R

**Section: 32**

**Machine Learning Project - Neural Nets**

123. Introduction to Neural Nets Project

124. Neural Nets Project - Solutions**Section: 33Statistics**

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 Plot

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

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

171. Frequency Tables in Base R

172. Frequency Tables with plyr

173. Building Cross Tables using xtabs

174. Building Cross Tables with CrossTable

175. Histograms

176. Cumulative Frequency Line Charts

177. Column Charts

178. Mean Plot Charts

179. Scatterplot Charts

180. Boxplot Charts

181. Checking the Normality Assumption - Numerical Method

182. Checking the Normality Assumption - Graphical Methods

183. Detecting the Outliers

184. One-Sample T Test

185. Binomial Test

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

DataBase

187. Getting Started

188. Writing your first query

189. Filters and Operands

190. Aggregate Functions

191. Grouping Aggregate Data with Group BY

192. rder By and Limit

193. Conditional Filtering with Case Statements

194. Comparisons using LIKE

195. Filtering the output of a query using HAVING

196. Joining tables together

197. Nested Queries

198. Working with Dates

Project-5 | NFL Data Analysis | 7 Hrs |

Project-4 | Recommendation System | 7 Hrs |

Project-3 | Twitter Analytics | 6 Hrs |

Project-2 | Stock Market Prediction | 6 Hrs |

Project-1 | Flight Delay Prediction | 6 Hrs |