R Statistics for Data Science Certification Training in Mainz, Germany

• Understand various data types
• Learn Various variable types
• List the uses of Variable types
• Explain Population and Sample
• Discuss Sampling techniques
• Understand Data representation

• Introduction to Data Types
• Numerical parameters to represent data
• Mean
• Mode
• Median
• Sensitivity
• Information Gain
• Entropy
• Statistical parameters to represent data

• Estimating mean, median and mode using R
• Calculating Information Gain and Entropy

• Understand rules of probability
• Learn about dependent and independent events
• Implement conditional, marginal and joint probability using Bayes Theorem
• Discuss probability distribution
• Explain Central Limit Theorem

• Uses of probability
• Need of probability
• Bayesian Inference
• Density Concepts
• Normal Distribution Curve

• Calculating probability using R
• Conditional, Joint and Marginal Probability using R
• Plotting a Normal distribution curve

Goal: In this module, you will be able to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint). 

Objectives: At the end of this Module, you should be able to:

• Understand the concept of point estimation using confidence margin
• Demonstrate the use of Level of Confidence and Confidence Margin
• Draw meaningful inferences using margin of error
• Explore hypothesis testing and its different levels

Topics:

• Point Estimation
• Confidence Margin
• Hypothesis Testing
• Levels of Hypothesis Testing

Hands-On/Demo:

• Calculating and generalizing point estimates using R
• Estimation of Confidence Intervals and Margin of Error

Goal: In this module, you will learn the different methods of testing the alternative hypothesis. 

Objectives: At the end of this module, you should be able to:

• Understand Parametric and Non-Parametric testing
• Learn various types of Parametric testing
• Explain A/B testing

Topics:

• Parametric Test
• Parametric Test Types
• Non- Parametric Test
• A/B testing

Hands-On/Demo:

• Perform P test and T tests in R

Goal: In this module, you will get an introduction to Clustering which forms the basis for machine learning. 

Objectives: At the end of this module, you should be able to:

• Understand the concept of Association and Dependence
• Explain Causation and Correlation
• Learn the concept of Covariance
• Discuss Simpson’s paradox
• Illustrate Clustering Techniques

Topics:

• Association and Dependence
• Causation and Correlation
• Covariance
• Simpson’s Paradox
• Clustering Techniques

Hands-On/Demo:

• Correlation and Covariance in R
• Hierarchical clustering in R
• K means clustering in R

Goal: In this module, you will be able to learn about the roots of Regression Modelling using statistics. 

Objectives: At the end of this module, you should be able to:

• Understand the concept of Linear Regression
• Explain Logistic Regression
• Implement WOE
• Differentiate between heteroscedasticity and homoscedasticity
• Learn concept of residual analysis

Topics:

• Logistic and Regression Techniques
• Problem of Collinearity
• WOE and IV
• Residual Analysis
• Heteroscedasticity
• Homoscedasticity

Hands-On/Demo:

• Perform Linear and Logistic Regression in R
• Analyze the residuals using R
• Calculation of WOE values using R