Statistics for Data Analysis Using R
4.5 (1,041 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,423 students enrolled

Statistics for Data Analysis Using R

Learn Programming in R & R Studio • Descriptive, Inferential Statistics • Plots for Data Visualization • Data Science
4.5 (1,041 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
5,428 students enrolled
Created by Sandeep Kumar ­
Last updated 5/2020
English
Current price: $100.99 Original price: $144.99 Discount: 30% off
5 hours left at this price!
30-Day Money-Back Guarantee
This course includes
  • 12.5 hours on-demand video
  • 13 articles
  • 36 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • You will first learn the basic statistical concepts, followed by application of these concepts using R Studio. This course is a nice combination of theory and practice.
  • Descriptive Statistics - Mean, Mode, Median, Skew, Kurtosis
  • Inferential Statistics - One and two sample z, t, Chi Square, F Tests, ANOVA, TukeyHSD and more.
  • Probability Distributions - Normal, Binomial and Poisson
  • You will learn R programming from the beginning level.
Course content
Expand all 110 lectures 12:29:50
+ 1. Getting Started with R and R Studio
9 lectures 39:18
Installing R and R Studio (Windows)
06:04
The First Look at the Functions in R
06:43
Saving the R Script File
06:14
Data Types in R
04:45
Simple Mathematical Operations
02:11
Download - Section 1 Notes and Codes
00:06
Section 1 - Practice Assignment
00:04
+ 2. Bonus Section: Descriptive Statistics Theory (lessons from my other course)
6 lectures 36:49
Introduction - Section 2
01:30
Understanding Basic Statistical Terms (Theory)
06:57
Descriptive Statistics (Theory)
04:29
Measurement of Central Tendency (Theory)
12:15
Measurement of Variation (Theory)
11:36
Download - Section 2 Slides
00:02
+ 3. Descriptive Statistics Using R
7 lectures 19:58
Introduction - Section 3
01:42
Getting Help
02:50
Measurement of Central Tendency - Median and Mode (Using R)
04:11
Measurement of Variation - Range, IQR and Standard Deviation (Using R)
04:24
Download - Section 3 Notes and Codes
00:02
Section 3 - Practice Assignment
00:04
+ 4. Vectors, Factors, Lists, Matrix and Data Frames in R
9 lectures 01:03:25
Introduction - Section 4
01:58
Introduction
08:49
Vectors Explained
08:59
Factors Explained
11:46
Lists Explained
05:38
Matrix Explained
13:35
Data Frames Explained
12:33
Download - Section 4 Notes and Codes
00:05
Section 4 - Practice Assignment
00:02
+ 5. Data Visualization
16 lectures 01:32:49
Introduction - Section 5
01:07
Your first plot in R
05:01
*** Scatter Plot ***
09:01
Add the Plot Main and Axis Lebel Text
06:23
Let's Draw Some Lines on the Plot
07:12
Change the Plot Characters (pch) from Circles to Plus Signs
03:30
Let's Look at Filtered Data
06:49
One is not enough, I want more plots on a single page!
06:54
Add text to the plot
08:56
Make plot colorful, and text bigger and bold
07:56
Time Series Plot
03:54
*** Box and Whisker Plot ***
12:41
Download - Section 5 Notes and Codes
00:03
Section 5 - Practice Assignment
00:02
+ 6. Descriptive Statistics Re-visited
3 lectures 12:33
Introduction - Section 6
01:21
Descriptive Statistics Using psych Package
11:09
Download - Section 6 Notes and Codes
00:03
+ 7. Bonus Section: Basic Probability Theory (lessons from my other course)
6 lectures 41:11
Introduction - Section 7
01:02
Probability Definition
07:48
Probability - Union and Intersection
09:36
Probability - The Law of Addition, Multiplication and Conditional Probability
16:18
Factorial, Permutations and Combinations
06:25
Download - Section 7 Slides
00:02
+ 8. Probability Distributions
16 lectures 02:10:42
Introduction - Section 8
01:46
Central Limit Theorem (Theory)
04:59
Central Limit Theorem Demonstration Using R
15:13
*** Normal Probability Distribution (Theory) ***
19:35
R Functions for Normal Distribution - rnorm, pnorm, qnorm and dnorm
11:31
Plotting Normal Distribution Using R Functions
07:23
*** Binomial Probability Distribution (Theory) ***
15:51
R Functions for Binomial Distribution - rbinom, pbinom, qbinom and dbinom
14:49
Plotting Binomial Distribution Using R Functions
03:26
Binomial Distribution using Visualize Package
05:57
*** Poisson Distribution (Theory) ***
06:16
R Functions for Poisson Distribution - rpois, ppois, qpois and dpois
06:18
Plotting Poisson Distribution Using R Functions
02:51
Poisson Distribution using Visualize Package
05:18
Download - Section 8 Notes and Codes
00:03
+ 9. Inferential Statistics - Hypothesis Tests
38 lectures 05:13:02
Introduction - Section 9
01:25
Types of Mean and Variance Tests
03:52
Hypothesis Testing - Types of Errors (Theory)
15:39
What is p value? (Theory)
04:10
*** Hypothesis Testing - One Sample Z Test (Theory) ***
13:06
One Sample z Test Using R
10:17
One Sample z Test using BSDA Package
04:30
*** One Sample t Test (Theory) ***
06:18
Visualizing One Sample t Test Results using Visualize Package
05:58
*** One Sample Variance Test - Chi Square Test (Theory) ***
05:46
One Sample Variance Test Using Envstats Package
10:52
Chi Square Distribution for One Sample Variance Test
07:49
*** Two Sample Z Test (Theory) ***
17:28
Two Sample Z Test Using R
09:15
Visualizing Two Sample Z Test Using Visualize Package
10:10
Two Sample Z Test for Populations with Different Means
02:22
*** Two Sample t Test (Theory) ***
08:44
Two Sample t Test (Equal Variance) Using R
08:48
Two Sample t Test (Unequal Variance) Using R
06:06
*** Paired t Test (Theory) ***
08:19
Paired t Test Using R
06:23
*** Two Sample Variance Test Using F Test (Theory) ***
11:03
Two Sample Variance Test (F Distribution) Using R
11:27
Visualizing Two Sample Variance Test Results using Visualize Package
05:04
*** ANOVA Introduction (Theory) ***
09:02
Understanding the concept behind ANOVA without doing any calculation.
11:29
Formulas and calculations in ANOVA (Theory)
05:12
ANOVA Example Using Manual Calculations (Theory)
14:04
Analysis of Variance (ANOVA) Using R
13:41
*** Goodness of Fit Test (Theory) ***
07:47
Goodness of Fit Test Using R - Example 1
07:35
Goodness of Fit Test Using R - Example 2
05:10
*** Contingency Tables (Theory) ***
14:06
Contingency Table Using R - Example 1
09:04
Contingency Table Using R - Example 2
12:30
Download - Section 9 Notes and Codes
00:05
Requirements
  • Basic school level mathematics will be helpful.
  • You will need to download and install R and R Studio on your PC or laptop. Both R and R Studio are for Free Software.
Description

Perform simple or complex statistical calculations using R Programming! - You don't need to be a programmer for this :)

Learn statistics, and apply these concepts in your workplace using R.

The course will teach you the basic concepts related to Statistics and Data Analysis,  and help you in applying these concepts. Various examples and data-sets are used to explain the application.

I will explain the basic theory first, and then I will show you how to use R to perform these calculations.

Following areas of statistics are covered:

Descriptive Statistics - Mean, Mode, Median, Quartile, Range, Inter Quartile Range, Standard Deviation. (Using base R function and the psych package)

Data Visualization - 3 commonly used charts: Histogram, Box and Whisker Plot and Scatter Plot (using base R commands)

Probability - Basic Concepts, Permutations, Combinations (Basic theory only)

Population and Sampling - Basic concepts (theory only)

Probability Distributions - Normal, Binomial  and Poisson Distributions (Base R functions and the visualize package)

Hypothesis Testing - One Sample and Two Samples - z Test, t-Test, F Test, Chi-Square Test

ANOVA - Perform Analysis of Variance (ANOVA) step by step doing the manual calculation and by using R.

Who this course is for:
  • Anyone who want to use statistics to make fact based decisions.
  • Anyone who wants to learn R and R Studio for career in data science.
  • Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.