The Data‑Science Pipeline

Part I of the textbook provides a step-by-step conceptual roadmap of the data-science pipeline, equipping readers with practical tools and a disciplined workflow for international business analytics. Each chapter in this part pairs fundamental theory with hands-on R/Python examples and checklists, ensuring that best practices of reproducible research are woven into every stage. By the end of Part I, readers will have a robust framework that accelerates analysis while avoiding the “garbage-in, garbage-out” pitfalls common in data projects. The three chapters of Part I cover the following core stages:

Problem Formulation & Scoping: Turning an ill-defined question into a testable hypothesis or concrete prediction task. This chapter emphasizes framing clear research questions that align with domain goals in international business, since a well-defined problem guides which data to gather and which methods to apply. Techniques for scoping feasible projects are introduced to ensure that analyses remain relevant to business and policy decisions.
Data Acquisition & Governance: Identifying and obtaining relevant data while upholding ethical and legal standards. Readers learn how to design reproducible data collection pipelines – from querying databases and APIs to web scraping and surveys – with attention to privacy, consent, and data ownership considerations. Practical guidance is given for using R packages and APIs (e.g. World Bank indicators, OECD, etc.) to source international business data. Reproducibility is stressed by demonstrating how to script data imports and document data sources for transparency.
Data Cleaning & Preprocessing: Efficiently preparing raw data for analysis – often the most time-consuming phase of a project. Strategies are presented for handling missing values, detecting outliers or anomalies, normalizing and encoding variables (e.g. creating factor variables or dummy codes), and reshaping datasets into “tidy” structures. The chapter shows how maintaining well-organized data frames and logs of cleaning steps makes downstream analysis more transparent and repeatable. This stage includes exploratory data analysis (EDA) as a crucial iterative step: using visualizations and summary statistics to uncover patterns or data issues that inform further refinement of the questions and data processing. Throughout the EDA process, the text emphasizes an iterative mindset – insights from plots and summaries lead to revisiting earlier steps (e.g. redefining variables or collecting additional data) in order to sharpen the analysis.
Linking to Modeling & Interpretation: Part I concludes by bridging into the modeling phases developed in later parts of the book. It highlights how choices made early in the pipeline – from measurement scales and feature engineering to how data is split into training and testing sets – set the stage for sound inference and predictive performance in machine learning. The reader sees how a solid foundation in data preparation and problem formulation ensures that the sophisticated modeling techniques in subsequent chapters can be applied effectively. This forward link reinforces that no machine learning method can salvage poorly collected or mis-specified data, underlining the mantra that rigorous upfront work enables credible results later on.

Throughout Part I, reproducibility is a unifying theme. Chapters illustrate how to implement each step using literate programming and version control tools, so that every result can be traced back to the exact code and data that produced it. By following the pipeline approach in Part I, an analyst develops a transparent, repeatable workflow that will be carried through to more advanced modeling in Parts II–IV. In summary, Part I lays the groundwork for ethical, reproducible data science in international business, from a question’s conception to a clean, analyzable dataset, ensuring that when readers proceed to apply machine learning, they do so on solid footing.

# The Data‑Science Pipeline Part I of the textbook provides a step-by-step **conceptual roadmap of the data-science pipeline**, equipping readers with practical tools and a disciplined workflow for international business analytics. Each chapter in this part pairs fundamental theory with hands-on *R/Python* examples and checklists, ensuring that best practices of reproducible research are woven into every stage. By the end of Part I, readers will have a robust framework that accelerates analysis while avoiding the “garbage-in, garbage-out” pitfalls common in data projects. The three chapters of Part I cover the following core stages: * **Problem Formulation & Scoping:** Turning an ill-defined question into a testable hypothesis or concrete prediction task. This chapter emphasizes framing clear research questions that align with domain goals in international business, since a well-defined problem guides which data to gather and which methods to apply. Techniques for scoping feasible projects are introduced to ensure that analyses remain relevant to business and policy decisions. * **Data Acquisition & Governance:** Identifying and obtaining relevant data while upholding ethical and legal standards. Readers learn how to design reproducible data collection pipelines – from querying databases and APIs to web scraping and surveys – with attention to privacy, consent, and data ownership considerations. Practical guidance is given for using R packages and APIs (e.g. World Bank indicators, OECD, etc.) to source international business data. Reproducibility is stressed by demonstrating how to script data imports and document data sources for transparency. * **Data Cleaning & Preprocessing:** Efficiently preparing raw data for analysis – often the most time-consuming phase of a project. Strategies are presented for handling missing values, detecting outliers or anomalies, normalizing and encoding variables (e.g. creating factor variables or dummy codes), and reshaping datasets into “tidy” structures. The chapter shows how maintaining well-organized data frames and logs of cleaning steps makes downstream analysis more transparent and repeatable. This stage includes *exploratory data analysis (EDA)* as a crucial iterative step: using visualizations and summary statistics to uncover patterns or data issues that inform further refinement of the questions and data processing. Throughout the EDA process, the text emphasizes an iterative mindset – insights from plots and summaries lead to revisiting earlier steps (e.g. redefining variables or collecting additional data) in order to sharpen the analysis. * **Linking to Modeling & Interpretation:** Part I concludes by bridging into the modeling phases developed in later parts of the book. It highlights how choices made early in the pipeline – from measurement scales and feature engineering to how data is split into training and testing sets – **set the stage for sound inference and predictive performance** in machine learning. The reader sees how a solid foundation in data preparation and problem formulation ensures that the sophisticated modeling techniques in subsequent chapters can be applied effectively. This forward link reinforces that no machine learning method can salvage poorly collected or mis-specified data, underlining the mantra that rigorous upfront work enables credible results later on. Throughout Part I, **reproducibility** is a unifying theme. Chapters illustrate how to implement each step using literate programming and version control tools, so that every result can be traced back to the exact code and data that produced it. By following the pipeline approach in Part I, an analyst develops a **transparent, repeatable workflow** that will be carried through to more advanced modeling in Parts II–IV. In summary, Part I lays the groundwork for **ethical, reproducible data science** in international business, from a question’s conception to a clean, analyzable dataset, ensuring that when readers proceed to apply machine learning, they do so on solid footing.