Training on Statistical Data Analysis using R

About the Course 

R is an open-source programming language that provides a wide variety of statistical and graphical techniques.  R has “become the de-facto standard for writing statistical software among statisticians. This course will give you a solid foundation in creating statistical analysis solutions using the R language, and how to carry out a range of commonly used analytical processes.

Target Participants 

This course is intended for Data Scientists, Data Analysts, Business Intelligence Analysts and any other professional who want to explore the vast range of analytical and graphical capabilities of R.

Course duration 

Online

7 Days

Classroom-based  

5 Days

What you will learn  

By the end of this training the participants will be able to learn: 

• An introduction to R, basic data types, and R/RStudio installation 
• Overview of base R concepts and specific data wrangling packages in R 
• Connecting to databases, executing database queries in R 
• How to use R for graphical summary 
• R programming 
• How to carry out a range of analyses using R 

Course Outline 

Introduction to Statistical Analysis 

• Explain the basic steps of the research process 
• Explain differences between populations and samples 
• Explain differences between experimental and non-experimental research designs 
• Explain differences between independent and dependent variables 

Introduction to R software for statistical computing 

• Overview of the R Studio IDE 
• Installing, loading and updating R packages 
• Creating objects in R 
• Data types 
• Data structures 
• Sorting vectors and data frames 
• Directory management commands 
• Direct data entry in R (for small data sets) 
• Importing data from other software 
• Decision structures (if, if-else, if-else if-else) 
• Repetitive structures (for and while loops) 
• Other important programming functions (break, next, warn, stop) 

Data Wrangling and Cleaning in R 

• Working with variables 
• Transform continuous variables to categorical variables 
• Add new variables to data frames
• Handling missing values 
• Sub-setting data frames 
• Appending and merging data frames 
• Spit data frames 
• Stack and unstack data frames 

Explanatory Data Analysis (EDA) in R 

• Creating tables of frequencies and proportions  
• Cross tabulations of categorical variables 
• Descriptive statistics for continuous variables 

Data Visualization using R base package 

• Introduction to graphs and charts in R 
• Customizing graph attributes (titles, axes, text, legends) 
• Graphs for categorical variables 
• Graphs for continuous variables 
• Graphs to investigate relationship between variables 

Mean Comparison Tests in R 

• One Sample T Test 
• Independent Samples T Test  
• Paired Samples T Test 
• One-way analysis of variance (ANOVA) 

Tests of Associations in R 

• Chi-Square test of independence 
• Pearson's Correlation 
• Spearman's Rank-Order Correlation 

Predictive Regression Models using R 

• Linear Regression 
• Multiple Linear Regression 
• Binary Logistic Regression 
• Ordinal Logistic Regression 

Training Approach 

This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

Training manuals and additional reference materials are provided to the participants.

Certification 

Upon successful completion of this course, participants will be issued with a certificate.

Tailor-Made Course 

We can also do this as a tailor-made course to meet organization-wide needs. Contact us to find out more [email protected]