Data Science for Marketing Analytics - Second Edition
Cód:
491_9781800560475
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Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming languageKey Features:Use data analytics and machine learning in a sales and marketing contextGain insights from data to make better business decisionsBuild your experience and confidence with realistic hands-on practiceBook Description:Unleash the power of data to reach your marketing goals with this practical guide to data science for business.This book will help you get started on your journey to becoming a master of marketing analytics with Python. Youll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.Youll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions.As well as learning how to clean, explore, and visualize data, youll implement machine learning algorithms and build models to make predictions. As you work through the book, youll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior.By the end of this book, youll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.What You Will Learn:Load, clean, and explore sales and marketing data using pandasForm and test hypotheses using real data sets and analytics toolsVisualize patterns in customer behavior using MatplotlibUse advanced machine learning models like random forest and SVMUse various unsupervised learning algorithms for customer segmentationUse supervised learning techniques for sales predictionEvaluate and compare different models to get the best outcomesOptimize models
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