 Getting Ready for Data Analytics with R and PythonXiaojing Dong ()
Chapter Chapter 1 in Marketing Analytics and Data Science, 2026, pp 1-16 from Springer Abstract: Abstract Marketing Analytics and Data Science is ultimately a hands-on discipline: you learn by working directly with data, building models, testing assumptions, and communicating results. R and Python make this possible in a practical and reliable way. They allow you to import data from spreadsheets, databases, and APIs; clean and reshape messy real-world datasets; visualize patterns and trends; and apply statistical and machine-learning methods at scale. In addition, programming turns data analysis into a repeatable workflow, so the results can be checked, updated with new data, and maybe reused for other similar projects without starting from scratch. In this textbook, we use R or Python not because marketing analytics or data science is “about coding,” but because these tools provide the most flexible way to turn questions into evidence and evidence into decisions. In addition, both R and Python have large, supportive user communities, where valuable resources are shared freely online. These resources, together with the recent development in AI programing assistants, have dramatically reduced the learning cost for these languages. Date: 2026 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-11130-2_1 Ordering information: This item can be ordered from DOI: 10.1007/978-3-032-11130-2_1 Access Statistics for this chapter More chapters in Springer Books from Springer |
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