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Название: Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers Автор: Iain Brown Издательство: Wiley Год: 2024 Страниц: 432 Язык: английский Формат: epub (true) Размер: 13.0 MB
Unlock the Power of data: Transform Your Marketing Strategies with Data Science.
In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively.
Marketing Data Science equips organizations with the power to make data-driven decisions, optimize marketing expenditures, elevate customer experiences, and secure a competitive edge. By harnessing advanced techniques, such as Machine Learning (see Chapter 5), natural language processing (NLP) (see Chapter 6), and Big Data analytics (see Chapter 11), marketing data scientists can discover latent opportunities, foresee customer behavior, and devise personalized marketing strategies that resonate with target audiences.
Web scraping involves extracting data from websites and online platforms using automated tools and scripts. This method can be useful for collecting data on product listings, customer reviews, competitor pricing, and other publicly available information. Web scraping tools such as Beautiful Soup or Scrapy in Python are popular choices for this purpose. Although web scraping can yield large volumes of data, it may require technical expertise, and the quality and structure of the data may vary significantly across websites.
When it comes to choosing between Hadoop and Spark, the decision often boils down to the specific requirements of the task at hand: • Data processing speed. Although Hadoop and Spark offer distributed processing, Spark often outperforms Hadoop in terms of speed, primarily due to its in-memory computing capabilities. Hence, for tasks that require real-time processing, Spark is generally more suitable. • Ease of use. Spark, with its high-level application programming interface, is considered more developer-friendly than Hadoop's MapReduce. It supports multiple languages such as Java, Scala, and Python, offering versatility in development.
• Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing Data Science. • Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. • Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. • Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. • Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative.
Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable.
CHAPTER 1: Introduction to Marketing Data Science CHAPTER 2: Data Collection and Preparation CHAPTER 3: Descriptive Analytics in Marketing CHAPTER 4: Inferential Analytics and Hypothesis Testing CHAPTER 5: Predictive Analytics and Machine Learning CHAPTER 6: Natural Language Processing in Marketing CHAPTER 7: Social Media Analytics and Web Analytics CHAPTER 8: Marketing Mix Modeling and Attribution CHAPTER 9: Customer Journey Analytics CHAPTER 10: Experimental Design in Marketing CHAPTER 11: Big Data Technologies and Real-Time Analytics CHAPTER 12: Generative Artificial Intelligence and Its Applications in Marketing CHAPTER 13: Ethics, Privacy, and the Future of Marketing Data Science About the Website Index
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