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Practical Business Intelligence: From Big Data and AI to Decisions, with Python
Paperback
Business GeneralGeneral Computers
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Publisher: Independently Published
Pages: 286
Weight: 1.10
Height: 0.60 Width: 7.00 Depth: 10.00
Language: English
Eight people sit in a meeting and somebody asks why sales fell last month. Three of them open spreadsheets and produce three different numbers. Nobody is lazy, nobody is wrong, and the meeting moves on without an answer. Closing that gap is what business intelligence is, and it is what this book teaches.
Practical Business Intelligence is a complete one-semester course by university lecturers Robertus Setiawan Aji Nugroho and Yustinus Baskoro Anindra Nugroho, built around one chain: data arrives whether you use it or not, AI finds the patterns in it, visualization makes those patterns visible, and a decision is the only point of the exercise.
Sixteen chapters map to sixteen class meetings. Each opens with a real situation, explains the ideas in plain language, and hands you a lab you run yourself, free, in Google Colab. Python only. No cluster, no licences, no installation.
Results you can check, including the untidy onesEvery number printed in this book came from a real run, which is why some of them are uncomfortable:
- A one-line rule a manager could write on a napkin comes within 1.2 points of a tuned random forest
- A properly randomised A/B test fails to detect an effect that is genuinely there
- The same churn model loses 104,894 or makes 7,253, on one assumption that lives in marketing, not in the data
- An anomaly detector misses 94 percent of the physically impossible records
- A retail file of a million rows does not fit in a spreadsheet, then hands you a false conclusion in the first lab
- Data warehouses, star schemas, and the grain that sinks most projects
- ETL with an audit trail, six dimensions of data quality, and who owns the number
- Columnar storage and partitioning: 49 million rows answered in a fraction of a second, on one machine
- KPI trees, OLAP, cohorts, and the averages that quietly lie
- Machine learning priced in money rather than accuracy, with leakage and baselines taken seriously
- Honest charts, and a worked comparison of a bad dashboard and a good one
- Choosing a BI tool, then building the same dashboard in Looker Studio and in Python
- Marketing: RFM segmentation that predicts the next quarter, customer value, and experiments that can detect something
- Retail and supply chain: ABC classification, basket rules, and forecasts measured against baselines that usually win
- Customer experience: sentiment, themes, and where a large language model belongs in a BI stack
- Risk and fraud: rare events, investigation queues, cost matrices, and explaining one decision
- Public sector: open data, boundaries that move, and why a map of counts is a map of population
The book closes with a guided project that takes you from a raw dataset to a deployed dashboard, a written page, and a presentation you can defend.
Who this book is forUndergraduates, lecturers who need a course rather than a reference, analysts handed a dashboard and a deadline, and anyone who has to make a number mean something. A little Python helps. Nothing else is assumed.
Each chapter also places its idea in an organisation with a small budget, one technical person and a patchy connection, which is the ordinary condition of most organisations everywhere. All terms are explained, with a glossary at the back.
A companion volume, Practical Big Data Analytics, covers the storage machinery this book keeps in the background. The two can be taught in sequence or apart.
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