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’s 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’s 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 🙂
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?
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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)
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You get both the “how” and “why” for both the statistics and R programming. I’m really happy with this course. (5 stars byElizabeth Crook)
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Sandeep has such a clear approach, pedagogic and explains everything he does. Perfect for a novice like myself. (5 stars byHashim Al-Haboobi)
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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)
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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)
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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)
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This is a wonderful course, I do recommend it. The best Udemy course I took. (5 stars byJoao Alberto Arantes Do Amaral)
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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: Let's Draw Some Lines on the Plot
Lecture 6: Change the Plot Characters (pch) from Circles to Plus Signs
Lecture 7: Let's 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
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Sandeep Kumar, Quality Gurus Inc.
Experienced Quality Director • Six Sigma Coach • 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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