Quantitative Data Management And Analysis With R Course

INTRODUCTION

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.

DURATION

5 days

WHO SHOULD ATTEND?

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

COURSE OBJECTIVES:

·       Analyze 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 & Code Used in the Course

·       Statistics in the Real World

·       Designing Studies & 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 & 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 Center

·       Measures of Variation

·       Charting & Graphing Continuous Data

·       Charting & 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 & 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.

·       We can also do this as tailor-made course to meet organization-wide needs. Contact us to find out more: training@skillsforafrica.org

·        The training will be conducted at SKILLS FOR AFRICA TRAINING INSTITUTE IN NAIROBI KENYA.

·       The training fee covers tuition fees, training materials, lunch and training venue. Accommodation and airport transfer are arranged for our participants upon request.

·       Payment should be sent to our bank account before start of training and proof of payment sent to: training@skillsforafrica.org