This training is jointly organized by BITM & PONDIT. Training will be held in PONDIT.
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:
- 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.
Course Outline :Section: 1Course Introduction1. 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
Section: 2Introduction to R Basics9. 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
Section: 3R Matrices24. 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: 4R Data Frames32. 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: 5R Lists39. List Basics
Section: 6Data Input and Output with R40. 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: 7R Programming Basics45. 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: 8Advanced R Programming55. 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: 9Data Manipulation with R61. 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: 10Data Visualization with R68. 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: 11Data Visualization Project78. Data Visualization Project
Section: 12
Interactive Visualizations with Plotly81. Overview of Plotly and Interactive Visualizations
82. Resources for Plotly and ggplot2
Section: 13StatisticsIntroduction83. Qualitative Data
84. Frequency Distribution of Qualitative Data
85. Relative Frequency Distribution of Qualitative Data
86. Bar Graph
87. Pie Chart
88. Category Statistics
Quantitative Data89. 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
Numerical Measures98. 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
Section: 14Probability Distributions112. 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
Descriptive Statistics120. 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
Section: 15Creating Frequency Tables and Cross Tables129. Frequency Tables in Base R
130. Frequency Tables with plyr
131. Building Cross Tables using xtabs
132. Building Cross Tables with CrossTable
Building Charts133. Histograms
134. Cumulative Frequency Line Charts
135. Column Charts
136. Mean Plot Charts
137. Scatterplot Charts
138. Boxplot Charts
Checking Assumptions139. Checking the Normality Assumption - Numerical Method
140. Checking the Normality Assumption - Graphical Methods
141. Detecting the Outliers
Performing Univariate Analyses142. One-Sample T Test
143. Binomial Test
144. Chi-Square Test for Goodness-of-Fit
Section: 16DataBaseData Extraction, Filtering, and Aggregation145. Getting Started
146. Writing your first query
147. Filters and Operands
148. Aggregate Functions
149. Grouping Aggregate Data with Group BY
Section: 17Sorting, Conditional Filtering and Fuzzy Comparisons150. rder By and Limit
151. Conditional Filtering with Case Statements
152. Comparisons using LIKE
153. Filtering the output of a query using HAVING
Section: 18Multiple Tables and Dates154. Joining tables together
155. Nested Queries
156. Working with Dates
Section: 19Introduction to Machine Learning with R157. Introduction to Machine Learning
Section: 20Machine Learning with R - Linear Regression158. 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
Section: 21Machine Learning with R - Logistic Regression162. 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
Section: 22Machine Learning with R - K Nearest Neighbors166. 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
Section: 23Machine Learning with R - Decision Trees and Random Forests170. 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
Section: 24Machine Learning with R - Support Vector Machines174. 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
Section: 25
Machine Learning with R - K-means Clustering178. 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
Section: 26Machine Learning with R - Natural Language Processing182. Introduction to Natural Language Processing
183. Natural Language Processing with R - Part 1
184. Natural Language Processing with R - Part 2
Section: 27Machine Learning with R - Neural Nets185. 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
Curriculum
Module |
Mastering on Data Science and Machine Learning using R |
32 Hrs |