Statistics for Data Analysis Using R

Statistics for Data Analysis Using R, available at $84.99, has an average rating of 4.49, with 111 lectures, based on 2313 reviews, and has 12613 subscribers.

You will learn about Learn R programming from the ground up, starting from the basics and progressing to advanced data analysis techniques. Learn the basic statistical concepts first, followed by practical application using R Studio, combining theory and practice for effective learning. Master descriptive statistics, including mean, mode, median, skewness, and kurtosis, and how to apply these concepts to your data analysis. Understand and perform inferential statistics such as one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and TukeyHSD, and more. Explore probability distributions, including normal, binomial, and Poisson, and their applications in data analysis. Develop the skills to perform data manipulation, visualization, and statistical analysis using R. Apply statistical concepts in real-world scenarios, enhancing your problem-solving abilities and decision-making skills. Boost your career prospects with a strong foundation in statistics and R programming, valuable skills in today鈥檚 data-driven job market. Equip yourself with the tools to handle large datasets and perform complex statistical analyses with confidence. Enhance your ability to make data-driven decisions by mastering the use of R for statistical analysis. This course is ideal for individuals who are Anyone who want to use statistics to make fact based decisions. or Anyone who wants to learn R and R Studio for career in data science. or Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language. It is particularly useful for Anyone who want to use statistics to make fact based decisions. or Anyone who wants to learn R and R Studio for career in data science. or Anyone who thinks Statistics is confusing and wants to learn it in plain and simple language.

Enroll now: Statistics for Data Analysis Using R

Summary

Title: Statistics for Data Analysis Using R

Price: $84.99

Average Rating: 4.49

Number of Lectures: 111

Number of Published Lectures: 111

Number of Curriculum Items: 111

Number of Published Curriculum Objects: 111

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Learn R programming from the ground up, starting from the basics and progressing to advanced data analysis techniques.

Learn the basic statistical concepts first, followed by practical application using R Studio, combining theory and practice for effective learning.

Master descriptive statistics, including mean, mode, median, skewness, and kurtosis, and how to apply these concepts to your data analysis.

Understand and perform inferential statistics such as one and two-sample z-tests, t-tests, Chi-Square tests, F-tests, ANOVA, and TukeyHSD, and more.

Explore probability distributions, including normal, binomial, and Poisson, and their applications in data analysis.

Develop the skills to perform data manipulation, visualization, and statistical analysis using R.

Apply statistical concepts in real-world scenarios, enhancing your problem-solving abilities and decision-making skills.

Boost your career prospects with a strong foundation in statistics and R programming, valuable skills in today鈥檚 data-driven job market.

Equip yourself with the tools to handle large datasets and perform complex statistical analyses with confidence.

Enhance your ability to make data-driven decisions by mastering the use of R for statistical analysis.

Who Should Attend

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.

Target Audiences

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.

Perform simple or complex statistical calculations using R Programming! – You don’t need to be a programmer for this 馃檪

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.

The 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.

What are other students saying about this course?

This course is a perfect mix of theory and practice. I highly recommend it for those who want to not only get good with R, but to also become proficient in statistics. (5 stars byAaron Verive)

You get both the 鈥渉ow鈥?and 鈥渨hy鈥?for both the statistics and R programming. I鈥檓 really happy with this course. (5 stars byElizabeth Crook)

Sandeep has such a clear approach, pedagogic and explains everything he does. Perfect for a novice like myself. (5 stars byHashim Al-Haboobi)

Very clear explanation. Coming from a non-technical background, it is immensely helpful that Prof. Sandeep Kumar is explaining all the minor details to prevent any scope for confusion. (5 stars byAnn Mary Biju)

I had a limited background in R and statistics going into this course. I feel like this gave me the perfect foundation to progress to more complex topics in both of those areas. I’m very happy I took this course. (5 stars byThach Phan)

Dr. Kumar is a fantastic teacher who takes you step by step. Can’t say enough about his approach. Detailed. Not only clear descriptions of statistics but you will learn many details that make R easier to use and understand. (5 stars byJames Reynolds)

This is a wonderful course, I do recommend it. The best Udemy course I took. (5 stars byJoao Alberto Arantes Do Amaral)

The course exceeded my expectations and i would like to thank the instructor Mr Sandeep Kumar for creating such an amazing course. The best thing about this course is the Theory incorporated that helps you understand what you are going to code in R. I have really learnt a lot. If you a looking for the best course for R then look no further because this is the best there can be. (5 stars byKipchumba Brian)

What are you waiting for?

This course comes with Udemy’s 30 days money-back guarantee. If you are not satisfied with the course, get your money back.

I hope to see you in the course.

Course Curriculum

Chapter 1: 1. Getting Started with R and R Studio

Lecture 1: Introduction – Section 1

Lecture 2: Installing R and R Studio (Windows)

Lecture 3: The First Look of R and R Studio. R you ready?

Lecture 4: The First Look at the Functions in R

Lecture 5: Saving the R Script File

Lecture 6: Data Types in R

Lecture 7: Simple Mathematical Operations

Lecture 8: Download – Section 1 Notes and Codes

Lecture 9: Section 1 – Practice Assignment

Chapter 2: 2. Bonus Section: Descriptive Statistics Theory (lessons from my other course)

Lecture 1: Introduction – Section 2

Lecture 2: Understanding Basic Statistical Terms (Theory)

Lecture 3: Descriptive Statistics (Theory)

Lecture 4: Measurement of Central Tendency (Theory)

Lecture 5: Measurement of Variation (Theory)

Lecture 6: Download – Section 2 Slides

Chapter 3: 3. Descriptive Statistics Using R

Lecture 1: Introduction – Section 3

Lecture 2: Getting Help

Lecture 3: Measurement of Central Tendency – Mean (Using R)

Lecture 4: Measurement of Central Tendency – Median and Mode (Using R)

Lecture 5: Measurement of Variation – Range, IQR and Standard Deviation (Using R)

Lecture 6: Download – Section 3 Notes and Codes

Lecture 7: Section 3 – Practice Assignment

Chapter 4: 4. Vectors, Factors, Lists, Matrix and Data Frames in R

Lecture 1: Introduction – Section 4

Lecture 2: Introduction

Lecture 3: Vectors Explained

Lecture 4: Factors Explained

Lecture 5: Lists Explained

Lecture 6: Matrix Explained

Lecture 7: Data Frames Explained

Lecture 8: Download – Section 4 Notes and Codes

Lecture 9: Section 4 – Practice Assignment

Chapter 5: 5. Data Visualization

Lecture 1: Introduction – Section 5

Lecture 2: Your first plot in R

Lecture 3: *** Scatter Plot ***

Lecture 4: Add the Plot Main and Axis Lebel Text

Lecture 5: Lets Draw Some Lines on the Plot

Lecture 6: Change the Plot Characters (pch) from Circles to Plus Signs

Lecture 7: Lets Look at Filtered Data

Lecture 8: One is not enough, I want more plots on a single page!

Lecture 9: Add text to the plot

Lecture 10: Make plot colorful, and text bigger and bold

Lecture 11: Multiple pairs of scatter diagrams – when one plot is not enough!

Lecture 12: Time Series Plot

Lecture 13: *** Histogram ***

Lecture 14: *** Box and Whisker Plot ***

Lecture 15: Download – Section 5 Notes and Codes

Lecture 16: Section 5 – Practice Assignment

Chapter 6: 6. Descriptive Statistics Re-visited

Lecture 1: Introduction – Section 6

Lecture 2: Descriptive Statistics Using psych Package

Lecture 3: Download – Section 6 Notes and Codes

Chapter 7: 7. Bonus Section: Basic Probability Theory (lessons from my other course)

Lecture 1: Introduction – Section 7

Lecture 2: Probability Definition

Lecture 3: Probability – Union and Intersection

Lecture 4: Probability – The Law of Addition, Multiplication and Conditional Probability

Lecture 5: Factorial, Permutations and Combinations

Lecture 6: Download – Section 7 Slides

Chapter 8: 8. Probability Distributions

Lecture 1: Introduction – Section 8

Lecture 2: Central Limit Theorem (Theory)

Lecture 3: Central Limit Theorem Demonstration Using R

Lecture 4: *** Normal Probability Distribution (Theory) ***

Lecture 5: R Functions for Normal Distribution – rnorm, pnorm, qnorm and dnorm

Lecture 6: Plotting Normal Distribution Using R Functions

Lecture 7: Introducting visualize Package

Lecture 8: *** Binomial Probability Distribution (Theory) ***

Lecture 9: R Functions for Binomial Distribution – rbinom, pbinom, qbinom and dbinom

Lecture 10: Plotting Binomial Distribution Using R Functions

Lecture 11: Binomial Distribution using Visualize Package

Lecture 12: *** Poisson Distribution (Theory) ***

Lecture 13: R Functions for Poisson Distribution – rpois, ppois, qpois and dpois

Lecture 14: Plotting Poisson Distribution Using R Functions

Lecture 15: Poisson Distribution using Visualize Package

Lecture 16: Download – Section 8 Notes and Codes

Chapter 9: 9. Inferential Statistics – Hypothesis Tests

Lecture 1: Introduction – Section 9

Lecture 2: Types of Mean and Variance Tests

Lecture 3: Hypothesis Testing – Types of Errors (Theory)

Lecture 4: What is p value? (Theory)

Lecture 5: *** Hypothesis Testing – One Sample Z Test (Theory) ***

Lecture 6: One Sample z Test Using R

Lecture 7: One Sample z Test using BSDA Package

Lecture 8: *** One Sample t Test (Theory) ***

Lecture 9: One Sample t Test Using R

Lecture 10: Visualizing One Sample t Test Results using Visualize Package

Lecture 11: *** One Sample Variance Test – Chi Square Test (Theory) ***

Lecture 12: One Sample Variance Test Using Envstats Package

Lecture 13: Chi Square Distribution for One Sample Variance Test

Lecture 14: *** Two Sample Z Test (Theory) ***

Lecture 15: Two Sample Z Test Using R

Lecture 16: Visualizing Two Sample Z Test Using Visualize Package

Lecture 17: Two Sample Z Test for Populations with Different Means

Lecture 18: *** Two Sample t Test (Theory) ***

Lecture 19: Two Sample t Test (Equal Variance) Using R

Instructors

Sandeep Kumar, 颅 Quality Gurus Inc.
Experienced Quality Director 鈥?Six Sigma Coach 鈥?Consultant

Rating Distribution

1 stars: 14 votes

2 stars: 27 votes

3 stars: 230 votes

4 stars: 802 votes

5 stars: 1240 votes

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