Course Details

Training on Quantitative Data Management and Analysis with R Software

INTRODUCTION

The "Quantitative Data Management and Analysis with R Software" training course is designed to equip participants with the essential skills needed to effectively manage, analyze, and visualize quantitative data using R, a powerful and versatile programming language widely used in statistical computing and data analysis.

This course will cover the fundamental concepts of quantitative data management, including data importation, cleaning, and transformation, as well as advanced statistical techniques for data analysis. Participants will learn how to leverage R's extensive libraries and functions to perform descriptive and inferential statistics, create compelling data visualizations, and derive meaningful insights from complex datasets.

Through hands-on exercises and real-world examples, participants will gain practical experience in using R for various data analysis tasks, making this training suitable for researchers, analysts, and anyone involved in data-driven decision-making across diverse fields such as business, healthcare, social sciences, and academia.

By the end of the course, attendees will be proficient in managing and analyzing quantitative data with R, empowering them to effectively communicate their findings and enhance their data analysis capabilities.

DURATION

5 days

TARGET AUDIENCE

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

COURSE OBJECTIVE

By the end of this course, participants will be able to:

1. Understand the fundamentals of R and its role in quantitative data management and analysis.
2. Import, clean, and prepare quantitative datasets for analysis using R.
3. Utilize R functions and packages for descriptive statistics and exploratory data analysis.
4. Conduct inferential statistical analyses including t-tests, ANOVA, and regression analysis.
5. Implement advanced statistical techniques such as multivariate analysis and time series analysis.
6. Create compelling data visualizations using R’s graphical capabilities and packages (e.g., ggplot2).
7. Interpret and communicate statistical results effectively to diverse audiences.
8. Apply best practices in data management to ensure data integrity and reproducibility.
9. Explore the use of R Markdown for reporting and documenting analysis workflows.
10. Address ethical considerations in data analysis and ensure responsible use of quantitative data.

COURSE OUTLINE

Module 1: Introduction to R and RStudio

• Overview of R and its applications in quantitative data analysis
• Installing and setting up R and RStudio
• Navigating the RStudio interface: scripts, console, and environment
• Basic R syntax: variables, data types, and functions

Module 2: Data Management in R

• Importing data from various formats (CSV, Excel, databases)
• Data cleaning and preparation: handling missing values, duplicates, and outliers
• Data transformation techniques: reshaping, filtering, and summarizing data
• Creating and managing data frames and lists in R

Module 3: Descriptive Statistics and Exploratory Data Analysis

• Calculating measures of central tendency and dispersion
• Generating frequency tables and visualizing distributions
• Using R functions for exploratory data analysis (EDA)
• Identifying patterns and relationships in data through visualizations

Module 4: Inferential Statistics

• Overview of hypothesis testing and confidence intervals
• Conducting t-tests (independent, paired) and interpreting results
• Performing Analysis of Variance (ANOVA) for comparing means
• Understanding p-values, effect sizes, and statistical power

Module 5: Regression Analysis

• Introduction to linear regression: assumptions, interpretation, and diagnostics
• Conducting multiple regression analysis and assessing model fit
• Exploring logistic regression for binary outcomes: modeling and interpretation
• Handling categorical variables and interaction effects in regression models

Module 6: Advanced Statistical Techniques

• Overview of multivariate analysis methods (e.g., factor analysis, cluster analysis)
• Conducting time series analysis: components, trends, and seasonality
• Implementing non-parametric tests and when to use them
• Introduction to machine learning techniques in R (optional)

Module 7: Data Visualization with R

• Introduction to data visualization principles and best practices
• Creating static and interactive visualizations with base R and ggplot2
• Customizing plots: titles, labels, legends, and themes
• Visualizing relationships between variables through scatterplots and heatmaps

Module 8: Reporting and Documenting Analysis

• Introduction to R Markdown for creating dynamic reports
• Structuring R Markdown documents: text, code chunks, and output formats
• Generating reproducible reports with embedded R code and results
• Best practices for documenting analysis workflows

Module 9: Ethical Considerations in Data Analysis

• Understanding ethical issues in quantitative research
• Responsible data handling and ensuring data privacy
• Addressing bias and ensuring fairness in data analysis
• Best practices for transparent reporting and reproducibility

CERTIFICATION

• Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

• Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

• Airport pick up and accommodation is arranged upon request

TERMS OF PAYMENT

• Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com