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14.5 MB
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BC7156139A00AE1083CC76FA6430A91D7A334C64
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April 22, 2026, 2:20 p.m.
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(Last updated: April 22, 2026, 2:25 p.m.)
| File | Size |
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| Larson D. Modern Business Analytics.Increasing the Value..with Python and R 2025.pdf | 14.5 MB |
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188.7 MB
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2024-11-12
| Uploaded by indexFroggy | Size 188.7 MB | Health [ 21 /3 ] | Added 2024-11-12 |
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SOURCE: Larson D. Modern Business Analytics.Increasing the Value..with Python and R 2025
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MEDIAINFO
Textbook in PDF format Deriving business value from analytics is a challenging process. Turning data into information requires a business analyst who is adept at multiple technologies including databases, programming tools, and commercial analytics tools. This practical guide shows programmers who understand analysis concepts how to build the skills necessary to achieve business value. Author Deanne Larson, Data Science practitioner and academic, helps you bridge the technical and business worlds to meet these requirements. You'll focus on developing these skills with R and Python using real-world examples. You'll also learn how to leverage methodologies for successful delivery. Learning methodology combined with open source tools is key to delivering successful business analytics and value. The world of business analytics has changed significantly over the past decade, and it’s clear that tools like Python and R are becoming essential for any serious data professional. I wrote this book because I saw a need for a comprehensive resource that blends the theoretical foundations of analytics with practical, hands-on coding in these two languages. This book fills the gap by not only teaching you how to perform analytics but also showing you how to apply those skills to real-world business problems. By the end, I hope you’ll feel more confident in your ability to extract meaningful insights from data and contribute more effectively to your organization’s decision-making processes. The current trend in analytics is to use open source tools: the leading tools are Python and R. Python is a full programming language that covers the programming capabilities to complete the data engineering as well as the Machine Learning algorithms to create models. R is used for statistics and analysis with strong visualization capabilities. Both tools are fueled by the different packages and libraries available. Statisticians and academics developed R, and it currently has more than 12,000 packages available in its open source repository. Each package (synonymously used with library) contains many statistical capabilities needed by a business analyst. R surpasses Python with its output through visualization and the ability to publish findings in a document. The largest drawback to R is the ability to use the code in a production environment. While possible to do, R is hard to automate and use for operational processing. Python has the same capabilities as R but excels at deploying and implementing large-scale analytics. Python code is easier to maintain and support. It was first a programming language before it was used in analytics. Today, most Machine Learning capabilities are available in Python first, then R. Python uses application programming interfaces (APIs) easily, and if you want to productionalize your Machine Learning code, Python is the simplest approach. This book shows you how to: Apply business analytics methodologies to achieve successful results Cleanse and transform data using R and Python Use R and Python to complete exploratory data analysis Create predictive models to solve business problems in R and Python Use Python, R, and business analytics tools to handle large volumes of data Commit code to GitHub to collaborate with data engineers and data scientists Measure success in business analytics I don’t get excited about too many books, but I am about this one. I published several books about data science applications in the business domain and have been a practitioner my whole professional life. So I’m aware of the need for a book on business analytics that stresses, explains, and illustrates both R and Python for analyzing business data. This book satisfies this need and then some. The chapters on advanced data visualization and working with modern data types alone take these languages into territory all business data scientists need to be aware of. I highly recommend this book to increase your background in Data Science. - Walter R. Paczkowski, Ph.D., President, Data Analytics Corp
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