06201112 - Quantitative Research in International Business Case application

This course provides a hands-on introduction to data analysis using R and RStudio, focusing on a real-world dataset and practical applications. Students will learn how to manipulate, visualize, and model data efficiently using R’s powerful libraries. The course begins with data wrangling techniques, covering data import, cleaning, and transformation. It then explores exploratory data analysis (EDA) with visualizations and summary statistics. Next, students will apply statistical modeling and machine learning methods to extract insights from a complex dataset. The course also covers reproducible workflows using R Markdown and best practices for reporting results

Estimation of private study (outside of contact hours): 8 hours

DIMENSION OF SOCIAL RESPONSIBILITY

Some of the questions in the dataset are on CSR

TARGETED KNOWLEDGE AND SKILLS 

Data Wrangling – Import, clean, and transform real-world datasets using R.
Exploratory Data Analysis (EDA) – Generate visualizations and summary statistics to uncover patterns.
Statistical Modeling – Apply regression, hypothesis testing, and predictive analytics.
Machine Learning Basics – Use classification and clustering techniques for data-driven insights.
Reproducible Analysis – Structure workflows using R Markdown for transparent reporting.
Data Visualization – Create clear and compelling plots with ggplot2 and other libraries.

COURSE OUTLINE

1. Introduction to R, RStudio, and Data Handling
2. Data Wrangling and Transformation
3. Exploratory Data Analysis and Visualization
4. Statistical Modeling and Machine Learning Basics
5. Reproducible Research and Reporting with R Markdown

PRESCRIBED TEXTS AND PUBLICATIONS

1. Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. O'Reilly

RECOMMENDED TEXTS AND PUBLICATIONS

1. Xie, Y., Allaire, J. J., & Grolemund, G. (2018). R markdown: The definitive guide. Chapman and Hall/CRC.
2. Wickham, H. (2016). Getting Started with ggplot2. In ggplot2: Elegant graphics for data analysis (pp. 11-31). Cham: Springer International Publishing.

EMBLEMATIC BOOKS OR RESEARCH PAPERS REGARDING THE SUBJECT OF THE COURSE

1. Provost, F., & Fawcett, T. Data Science for Business.

TEXTS AND PUBLICATIONS OF IAELYON FACULTY ON THE SUBJECT OF THE COURSE

1. Deslée, A., & Cloarec, J. (2024). Safeguarding Privacy: Ethical Considerations in Data-Driven Marketing. In The Impact of Digitalization on Current Marketing Strategies (pp. 147-161). Emerald Publishing Limited.

Individual grade
Report, 1h

Weight: 100

TEACHING METHODS
Short lectures, data exercices, case application

NATURE OF MATERIALS
Powerpoint, cheatsheets, scripts 

TEACHING INNOVATIONS AND USE OF TECHNOLOGY
Access to posit.cloud solution for data analysis

PRE-REQUISITES IN TERMS OF KNOWLEDGE AND SKILLS
Basic statistice

ADVISED PRIOR READING
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science. O'Reilly 

RESOURCES AVAILABLE
Posit provides cheat sheets covering essential R tools for data analysis, visualization, machine learning, and reproducible research.