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Quantitative Data Management and Analysis with R Course

This course is designed for participants who plan to use R for the management, coding, analysis and visualization of qualitative data. The course’s content is spread over seven modules and includes: Basics of Applied Statistical Modelling, Essentials of the R Programming, Statistical Tools, Probability Distributions, Statistical Inference, Relationship between Two Different Quantitative Variables and Multivariate Analysis. The course is entirely hands-on and uses sample data to learn R basics and advanced features.

Who Should Attend?

Statistician, analyst, or a budding data scientist and beginners who want to learn how to analyse data with R,

Course Objective:

• Analyse t data by applying appropriate statistical techniques
• Interpret the statistical analysis
• Identify statistical techniques a best suited to data and questions
• Strong foundation in fundamental statistical concepts
• Implement different statistical analysis in R and interpret the results
• Build intuitive data visualizations
• Carry out formalized hypothesis testing
• Implement linear modelling techniques such multiple regressions and GLMs
• Implement advanced regression analysis and multivariate analysis

Course content

Module One: Basics of Applied Statistical Modelling

• Introduction to the Instructor and Course
• Data and Code Used in the Course
• Statistics in the Real World
• Designing Studies and Collecting Good Quality Data
• Different Types of Data

Module Two: Essentials of the R Programming

• Rationale for this section
• Introduction to the R Statistical Software and R Studio
• Different Data Structures in R
• Reading in Data from Different Sources
• Indexing and Subletting of Data
• Data Cleaning: Removing Missing Values
• Exploratory Data Analysis in R

Module Three: Statistical Tools

• Quantitative Data
• Measures of Centre
• Measures of Variation
• Charting and Graphing Continuous Data
• Charting and Graphing Discrete Data
• Deriving Insights from Qualitative/Nominal Data

Module Four: Probability Distributions

• Data Distribution: Normal Distribution
• Checking for Normal Distribution
• Standard Normal Distribution and Z-scores
• Confidence Interval-Theory
• Confidence Interval-Computation in R

Module Five: Statistical Inference

• Hypothesis Testing
• T-tests: Application in R
• Non-Parametric Alternatives to T-Tests
• One-way ANOVA
• Non-parametric version of One-way ANOVA
• Two-way ANOVA
• Power Test for Detecting Effect

Module Six: Relationship between Two Different Quantitative Variables

• Explore the Relationship Between Two Quantitative Variables
• Correlation
• Linear Regression-Theory
• Linear Regression-Implementation in R
• Conditions of Linear Regression
• Multi-collinearity
• Linear Regression and ANOVA
• Linear Regression with Categorical Variables and Interaction Terms
• Analysis of Covariance (ANCOVA)
• Selecting the Most Suitable Regression Model
• Violation of Linear Regression Conditions: Transform Variables
• Other Regression Techniques When Conditions of OLS Are Not Met
• Regression: Standardized Major Axis (SMA) Regression
• Polynomial and Non-linear regression
• Linear Mixed Effect Models
• Generalized Regression Model (GLM)
• Logistic Regression in R
• Poisson Regression in R
• Goodness of fit testing

Module Seven: Multivariate Analysis

• Introduction Multivariate Analysis
• Cluster Analysis/Unsupervised Learning
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Correspondence Analysis
• Similarity and Dissimilarity Across Sites
• Non-metric multi-dimensional scaling (NMDS)
• Multivariate Analysis of Variance (MANOVA)

General Notes

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.

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