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.
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.
- 1. Getting Started with R and R Studio
- 2. Bonus Section: Descriptive Statistics Theory (lessons from my other course)
- 3. Descriptive Statistics Using R
- 4. Vectors, Factors, Lists, Matrix and Data Frames in R
- 5. Data Visualization
- 6. Descriptive Statistics Re-visited
- 7. Bonus Section: Basic Probability Theory (lessons from my other course)
- 8. Probability Distributions
- 9. Inferential Statistics – Hypothesis Tests
- Bonus Section