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Quantitative Data Management And Analysis With R Course

By: Data-Afrique Consultants

Kenya

08 - 12 Jul, 2024  5 days

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USD 2,950

Venue: Nairobi

Other Dates

Venue Date Fee  
Nairobi, Kenya 12 - 16 Aug, 2024 USD1500
Nairobi, Kenya 09 - 13 Sep, 2024 USD1500
Kigali, Rwanda 14 - 18 Oct, 2024 USD2950
Mombasa, Kenya 11 - 15 Nov, 2024 USD1500
Nairobi, Kenya 09 - 13 Dec, 2024 USD1500

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 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 & 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.
Nairobi Jul 08 - 12 Jul, 2024
Nairobi, Kenya 12 - 16 Aug, 2024
Nairobi, Kenya 09 - 13 Sep, 2024
Kigali, Rwanda 14 - 18 Oct, 2024
Mombasa, Kenya 11 - 15 Nov, 2024
Nairobi, Kenya 09 - 13 Dec, 2024

Class Session: 08:00:am - 16:00:pm

USD 2,950.00(Kigali)
USD 4,500.00(Dubai)
USD 1,500.00(Nairobi)
USD 1,500.00(Mombasa)
(Convert Currency)

DUNCAN KARIUKI +254723360025

DATA-AFRIQUE CONSULTANCY

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