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
Dates | Fees | Location | Apply |
---|---|---|---|
09/09/2024 - 13/09/2024 | $2950 | Kigali |
Physical Class Online Class |
14/10/2024 - 18/10/2024 | $2950 | Kigali |
Physical Class Online Class |
11/11/2024 - 15/11/2024 | $1500 | Mombasa |
Physical Class Online Class |
09/12/2024 - 13/12/2024 | $1500 | Nairobi |
Physical Class Online Class |