QUANTITATIVE DATA MANAGEMENT AND ANALYSIS WITH R COURSE

QUANTITATIVE DATA MANAGEMENT AND ANALYSIS WITH R COURSE

Register online: https://bit.ly/2HuPEVv
Organizer: DATA-AFRIQUE CONSULTANCY (www.data-afriqueconsultancy.org)
Course fee: USD 1000
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 Objective:
•         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 DATA-AFRIQUE TRAINING CENTRE, 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]