R Statistics for Data Science Certification Training

R Statistics for Data Science Certification Training in Richmond, VA

Learn the fundamentals of R programming and core statistical techniques essential for data analysis.
Work on hands-on projects using real-world datasets to apply your skills in practical scenarios.
Master data manipulation and visualization using popular R packages like dplyr, ggplot2, and other tools in the tidyverse.
Get introduced to machine learning concepts and algorithms using R libraries such as caret and randomForest.
Engage with interactive lessons, coding exercises, and quizzes to reinforce your understanding.
Analyze real case studies from industries like finance, healthcare, and marketing.
Earn a recognized certificate upon completion to boost your resume and LinkedIn profile.
Learn at your own pace with flexible access to course materials anytime, anywhere.
Receive support from experienced instructors and access to discussion forums or mentorship.
Get career guidance, portfolio tips, and resume-building support to prepare for data science roles.

R Statistics for Data Science Course Overview

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

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R Statistics for Data Science Course Topics

Goal: In this module, you will be introduced to data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters.

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

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

Topics:

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

Hands-On/Demo:

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

Goal: In this module, you will learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

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

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

Topics:

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

Hands-On/Demo:

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

R Statistics for Data Science Frequently Asked Questions

Yes, we do offer additional discounts to group and corporate training customers. Please email us at support@encertify.com to find out more about our group discount offerings.

Use the "Submit your query"section in this page or check "Contact Us" section. Alternatively, please send an email to support@encertify.com to find out more about our course offerings.

If you're unemployed right now, or you're a student taking this course for career growth, we do provide additional discounts for you on selected courses. Please email support@encertify.com to avail this benefit and discount coupon.

Note: These discounts are available on selected courses only.

We do not offer placement or placement assistance services at this time. However, our training is designed to equip you with in-demand skills, hands-on experience, and certification readiness to help you confidently pursue new career opportunities. Many of our learners have successfully transitioned into new roles or advanced in their careers based on the knowledge and certifications gained through our programs