Applied Statistics using R with Data Processing

About this Course:

This is the bite size course to learn R Programming for Applied Statistics. In CRISP DM data mining process, Applied Statistics is at the Data Understanding stage. This course also covers Data processing, which is at the Data Preparation Stage.

You will need to know some R programming, and you can learn R programming from my "Create Your Calculator: Learn R Programming Basics Fast" course. You will learn R Programming for applied statistics and you will be able

You can take the course as follows, I may allow you to have the SVBook certificate in Data Mining using R in future after you passed a quiz and completed all the courses below:

• Create Your Calculator: Learn R Programming Basics Fast (R Basics)
• Applied Statistics using R with Data Processing (Data Understanding and Data Preparation)
• Advanced Data Visualizations using R with Data Processing (Data Understanding and Data Preparation, in future)
• Machine Learning with R (Modeling and Evaluation)
• I will create advanced data visualizations using R for data understanding stage and includes some data processing for data preparation stage in future.

References:

This course is actually based on the Learn R for Applied Statistics book I have published at Apress.

Basic knowledge:

• Computer Knowledge
• Basic coding knowledge

Who is the target audience?

Any one who desires to learn it.

Course Completion Certificate

What you will learn:

Content:

• Getting Started
• Getting Started 2
• Getting Started 3
• Data Mining Process
• Download Data set
• Read Data set
• Mode
• Median
• Mean
• Range
• Range 2
• Range 3
• IQR
• Qunatile
• Population Variance
• Sample Variance
• Variance
• Standard Deviation
• Normal Distribution
• Skewness and Kurtosis
• Summary() and Str()
• Correlation
• Covariance
• Inferential Statistics Tests
• One Sample T Test
• Two Sample Unpaired T Test
• Two Sample Unpaired T Test (Variance not Equal)
• Two Sample Paired T Test
• Chi Square Test
• One Way ANOVA
• Two Way ANOVA
• MANOVA
• Simple Linear Regression
• Multiple LInear Regression
• Data Processing: Select Variables
• Data Processing: Sort Data
• Data Processing: Filter Data
• Data Processing: Remove Missing Values and Remove Duplicates

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