Introduction in Data Analysis: A complete beginner's guide (English Edition) odf de graça baixar
por Ovidiu Caprariu
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Descrição do livro
Introduction to Data Analysis is the friendly, jargon-free guide that takes you from complete beginner to confident data analyst, one clear chapter at a time. What this book will do for you By the time you turn the last page, you will be able to take a raw dataset, clean it, analyse it, visualise it beautifully, and present your findings as a compelling story that moves people to act. These are skills that employers across every industry are actively looking for — and skills that will serve you for the rest of your career. What you will learn, chapter by chapter The book opens by answering the question every beginner asks: why does any of this matter? Through real mini case studies — from Netflix recommendations to hospital readmission rates — Chapter 1 shows you exactly how data analysis is already shaping the world around you, and why understanding it gives you a genuine advantage. Chapter 2 gives you the full picture: the key steps of a data analytics project, the roles on a data team, the tools professionals use, and the difference between data analysis and data science. You will finish it knowing exactly where you fit in the landscape. Chapter 3 grounds you in the fundamentals: what data actually is, where it comes from, why data quality matters more than anything else, and how organisations store and manage it. Understanding these foundations separates analysts who get things right from those who build on shaky ground. Chapter 4 introduces descriptive statistics — mean, median, standard deviation, distributions, and outliers — explained through real examples like analysing student grades, not abstract equations. You will learn to summarise any dataset in a handful of meaningful numbers. Chapter 5 is the chapter most books skip: data cleaning and preparation. Real data is always messy, and this chapter walks you through a real-world example of cleaning a chaotic sales dataset from start to finish, using the same professional standards practised in industry. Chapter 6 is where the magic happens. You will learn to create beautiful, meaningful charts and dashboards using four leading tools — Tableau, Microsoft Power BI, Looker Studio, and Google Sheets — all with step-by-step instructions and links to live interactive dashboards you can explore in your browser right now. The chapter closes with design principles for effective visuals and a complete guide to data storytelling: how to turn charts into narratives that inform, persuade, and inspire action. Chapter 7 introduces data mining — the science of finding hidden patterns in large datasets. You will learn how supermarkets use market basket analysis to decide which products to place next to each other, how fraud detection works, and how clustering algorithms discover customer segments nobody predicted. Chapter 8 brings everything to life through five detailed real-world examples: analysing social media sentiment, understanding customer behaviour, forecasting sales trends, predicting hospital patient readmissions, and measuring sports player performance. These are the exact use cases data analysts work on every day. Chapter 9 looks ahead at the forces reshaping the field: artificial intelligence, machine learning, generative AI, big data, cloud computing, and the rise of no-code tools that are making data analysis accessible to everyone. The book closes with a practical Conclusion covering how to start a career in data analytics — the roles available, the skills required, the certifications worth pursuing, and a realistic career path from beginner to senior. A comprehensive glossary and curated resource list give you everything you need to keep learning.
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