Course Description
R is a programming language and related to software environemnt for statistical computin and graphics. R programming languages is mostlyt used in graphics, it also used mostly by statisticians for developing softwares. Through this course one will be learning about basic R functions, special numerical vlaues, array and matrix, repository and packages, installing a package, how to calculate variance, co-variance, cumulative frequency, learn about statistics, probability and distribution, random examples, discrete example and many as such concept about R.
In this Training Bundle you will learn about 2 Major Concepts in about 150+ lectures:
Pre-Requisites:
- Passion to learn about R Programming Courses
- Computer with Internet Connection
Learning Objectives:
- Hours of content with Practical Examples!
- Learn Practical Training on R Programming
Target Audience:
- People from Analytics domain
- Anyone interested in R Programming related Training.
How to access:-
Step 1 - eduCBA sends the redemption coupon along with the registration link.
Step 2 - Student will register with his register email id and coupon
Step 3 - Student receive the course access and password on his/her email id
Step 4 - Login using your email id and password and start learning
Benefits of online course:
1. High Quality Content
2. Learn Anywhere Anytime & at Your Pace
3. 24X7 Customer Support
4. Complete Student Support throughout the Program
5. Online Video Training Material
6. Lifetime course access
For any query call/miss call-
Mob-+918587999769/9818826705
Curriculum
Section 1: Business Analytics Using R
1 Course Introduction
2 Course Curriculum
3 Discriminant Analysis
4 Introduction to R & Analytics
5 Evolution of Business Analytics
6 Business Example- Hotel
7 Data for Business Analytics
8 Ordinal Data
9 Decision Model Example
10 Descriptive Decision Models
11 Business Analytics Life Cycle
12 Model deployment
13 Steps in Problem Solving Process
14 Software used in Business Analytics
15 Getting Started with R
16 Installing R Studio
17 Basics of R
18 Basic R Functions
19 Data Types
20 Recycling Rule
21 Special Numerical Values
22 Parallel Summary Functions
23 Logical Conjunctions
24 Pasting Strings together
25 Type Coercion
26 Array & Matrix
27 Factor
28 Repository & Packages
29 Installing a Package
30 Importing Data
31 Importing Data SPSS
32 Working with Data
33 Data Aggregation
34 Data Manipulation & Statistics Basics
35 Merging
36 Data Creation
37 Merge Example
38 What is Statistics
39 Variables
40 Quantiles
41 Calculating Variance
42 Calculating Covariance
43 Cumulative Frequency
44 Library (mass)
45 Head (faithful)
46 Scatter Plot
47 Control Flow
48 Statistics, Probability & Distribution
49 Random Variable
50 Random Example
51 Discrete Example
52 Practice problem
53 Continuous Case
54 Exponential Distribution Practice Problem
55 Expected Value
56 Gambling Example
57 Deal or no deal
58 Distribution details
59 Binomial Distribution continued
60 Expected Value from Binomial
61 Uniform Random Variables
62 Probability distributions examples
63 Probability distributions examples continued
64 Business Analytics using R
65 Normal PDF
66 What is Normal, Not Normal
67 SAT Example
68 Example- Birth Weights
69 dNorm, pNorm, qNorm
70 Understanding Estimation
71 Properties of Good Estimators
72 Central Limit Theorem
73 Kurtosis
74 Constructing Central Limit Theorem
75 Confidence Intervals for the Mean
76 Confidence Intervals Examples
77 Computer Lab Example
78 t-distribution
79 t-distribution continued
80 R Examples
81 Standard error of the mean
82 Downloading the Package
83 Sample Differences
84 Hypothesis Generation & Testing
85 Hypothesis Testing
86 One sided P Value
87 Power & Sample Size
88 Testing Hypothesis using R
89 Calculating the Z value
90 Lower Tail proportion of population proportion
91 Forecasting
92 Time Series Analysis Applications
93 Approaches to Forecasting
94 Observation Components
95 Traditional Approaches
96 Double Exponentional Smoothing
97 ARIMA Steps
98 Forecasting Performance
99 Univariate ARIMA
100 R Visualization
101 Why Visualize
102 Overlaying Plots
103 Graphs representation of Data
104 Graphs representation of Data continued
105 Advanced Graphs
106 Bubble Charts
107 Anova
108 Concept of effect
109 Estimate of Treatment effect
110 Factorial Anova
111 Regression
112 Regression Model
113 Linear Relationship
114 Output of Regression Model
Section 2: R Programming - Data Science and Analytics with R
115 Overview and History of R
116 Datatypes and Basic Operations – Part1_1 part 01
117 Datatypes and Basic Operations – Part1_1 part 02
118 Datatypes and Basic Operations – Part1_2 Part 01
119 Datatypes and Basic Operations – Part1_2 Part 02
120 Datatypes and Basic Operations – Part1_2 Part 03_part01
121 Datatypes and Basic Operations – Part1_2 Part 03_part 02 summary
122 Datatypes and Basic Operations – Part2_1
123 Datatypes and Basic Operations – Part2_2
124 ?ReadingData-1
125 ?ReadingData-2
126 ReadingData-3
127 ReadingData-4a
128 ReadingData-4b
129 Debugging-1
130 ControlStructures
131 Functions Part 01
132 Functions Part 02
133 ScopingRules1 Part 01
134 ScopingRules1 Part 02
135 ScopingRules2
136 Looping1
137 Looping2
138 Looping3
139 Simulation_part-1
140 Simulation_part-2
141 Plotting1
142 Plotting2
143 Plotting3_part-1
144 Plotting3_part-2
145 Plotting4
146 Plotting5
147 PlottingColors1
148 PlottingColors2
149 Date and TimePart1and 5.Date and TimePart2
150 Date andTimePart3
151 RegEx1
152 RegEx2
153 RegEx3_part-1
154 RegEx3_part-2
155 Classes and Methods1_part-1
156 Classes and Methods1_part-2
157 Classes and Methods2_part-1
158 Classes and Methods2_part-2
159 DebuggingPart2
