Mastering on Data Science and Machine Learning using R

Mastering on Data Science and Machine Learning using R

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.

course at a glance

  • Date : 13 Sep - 1 Nov 2019
  • No. of Classes/ Sessions : 8
  • Total Hours : 32
  • Last Date of Registration : 13 Sep 2019
  • Class Schedule :
    • Friday - 9 AM - 1 PM
  • venue : RH Home Center, Level#5, Suite#539, 74/B/1 Green Road, Tejgaon, Dhaka-1205

Price: TK. 9,000
(Excluding VAT & TAX)

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: 1

Course Introduction
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

Section: 2

Introduction to R Basics
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

Section: 3

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: 4

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: 5

R Lists
39. List Basics

Section: 6

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: 7

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: 8

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: 9

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: 10

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: 11

Data Visualization Project
78. Data Visualization Project

Section: 12

Interactive Visualizations with Plotly
81. Overview of Plotly and Interactive Visualizations
82. Resources for Plotly and ggplot2

Section: 13

Statistics
Introduction
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

Quantitative Data
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

Numerical Measures
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


Section: 14

Probability Distributions
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

Descriptive Statistics
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

Section: 15

Creating Frequency Tables and Cross Tables
129. Frequency Tables in Base R
130. Frequency Tables with plyr
131. Building Cross Tables using xtabs
132. Building Cross Tables with CrossTable

Building Charts
133. Histograms
134. Cumulative Frequency Line Charts
135. Column Charts
136. Mean Plot Charts
137. Scatterplot Charts
138. Boxplot Charts

Checking Assumptions
139. Checking the Normality Assumption - Numerical Method
140. Checking the Normality Assumption - Graphical Methods
141. Detecting the Outliers

Performing Univariate Analyses
142. One-Sample T Test
143. Binomial Test
144. Chi-Square Test for Goodness-of-Fit

Section: 16

DataBase
Data Extraction, Filtering, and Aggregation
145. Getting Started
146. Writing your first query
147. Filters and Operands
148. Aggregate Functions
149. Grouping Aggregate Data with Group BY

Section: 17

Sorting, Conditional Filtering and Fuzzy Comparisons
150. rder By and Limit
151. Conditional Filtering with Case Statements
152. Comparisons using LIKE
153. Filtering the output of a query using HAVING

Section: 18

Multiple Tables and Dates
154. Joining tables together
155. Nested Queries
156. Working with Dates

Section: 19

Introduction to Machine Learning with R
157. Introduction to Machine Learning

Section: 20

Machine Learning with R - Linear Regression
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

Section: 21

Machine Learning with R - Logistic Regression
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

Section: 22

Machine Learning with R - K Nearest Neighbors
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

Section: 23

Machine Learning with R - Decision Trees and Random Forests
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

Section: 24

Machine Learning with R - Support Vector Machines
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

Section: 25

Machine Learning with R - K-means Clustering
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

Section: 26

Machine Learning with R - Natural Language Processing
182. Introduction to Natural Language Processing
183. Natural Language Processing with R - Part 1
184. Natural Language Processing with R - Part 2

Section: 27

Machine Learning with R - Neural Nets
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




Curriculum

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

Tentative Class Start

13th September, 2019

Available Seat

10 / 20

who can join

Anyone who want to learn about machine learning.

Meet the Instructor