How to Become Data Scientist Using R

How to Become Data Scientist 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 : 9 Feb - 18 May 2018
  • No. of Classes/ Sessions : 15
  • Total Hours : 32
  • Last Date of Registration : 9 Feb 2018
  • Class Schedule :
    • Friday - 9 am - 1 pm
  • venue : RH Home Center, Suit No. 539, Level: 5, 74/B/1 Green Road, Tejgaon, Dhaka - 1205.

Price: TK. 9,000

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: 33
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


Quantitative Data
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


Numerical Measures
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


Section: 34

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


Descriptive Statistics
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


Section: 35

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


Section: 36
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

Section: 37

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

Section: 38

Multiple Tables and Dates
196. Joining tables together
197. Nested Queries
198. Working with Dates

Curriculum

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

Tentative Class Start

9th February, 2018

Available Seat

10 / 16

who can join

Anyone who want to learn about machine learning.

Meet the Instructor