QUANTITATIVE DATA MANAGEMENT AND ANALYSIS WITH R COURSE

QUANTITATIVE DATA MANAGEMENT AND ANALYSIS WITH R COURSE
Register online:  https://skillsforafrica.org/course/122
Organizer: Skills for Africa Training Institute (https://skillsforafrica.org)
Course fee: USD 1,200

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: [email protected]
•        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: [email protected]