Basic setup
Ankur Awadhiya · 2026
This chapter prepares the reader to work effectively by building a practical foundation for computational analysis. It frames technical setup as a research skill: good organization, reproducible workflows, and clear documentation reduce errors and make results easier to verify and share. The chapter introduces the essentials of working in a statistical computing environment, focusing on how data are represented and manipulated in code. It explains how to import and export data reliably, and why careful handling of formats, missing values, and data types matters for downstream modeling. The chapter then develops core ideas of data preparation: structuring information so that variables and observations are consistently organized, reshaping tables to match analytic needs, and producing summaries that reveal patterns while preserving meaning. Visualization is presented as an analytic tool rather than decoration—used to detect anomalies, explore relationships, and communicate uncertainty. The chapter also emphasizes practical habits that support long-term productivity: writing readable code, building analyses in small steps, and keeping a clear record of decisions and outputs. By the end, readers are equipped to move from raw files to analysis-ready data and to produce interpretable figures and summaries that support later modeling chapters. The overall message is that strong machine learning begins with strong data practice and a disciplined workflow, not with sophisticated algorithms alone.