Data Science for Marketing Analytics - Second Edition: A practical guide to forming a killer marketing strategy through data analysis with Python
暫譯: 行銷分析的數據科學 - 第二版：透過 Python 數據分析形成強大行銷策略的實用指南

Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali

Data Science for Marketing Analytics - Second Edition: A practical guide to forming a killer marketing strategy through data analysis with Python

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商品描述

Key Features

  • Use data analytics and machine learning in a sales and marketing context
  • Gain insights from data to make better business decisions
  • Build your experience and confidence with realistic hands-on practice

Book 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. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects.

You'll 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, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll 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, you'll 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 pandas
  • Form and test hypotheses using real data sets and analytics tools
  • Visualize patterns in customer behavior using Matplotlib
  • Use advanced machine learning models like random forest and SVM
  • Use various unsupervised learning algorithms for customer segmentation
  • Use supervised learning techniques for sales prediction
  • Evaluate and compare different models to get the best outcomes
  • Optimize models with hyperparameter tuning and SMOTE

Who this book is for

This marketing book is for anyone who wants to learn how to use Python for cutting-edge marketing analytics. Whether you're a developer who wants to move into marketing, or a marketing analyst who wants to learn more sophisticated tools and techniques, this book will get you on the right path.

Basic prior knowledge of Python and experience working with data will help you access this book more easily.

商品描述(中文翻譯)

**主要特點**

- 在銷售和行銷的背景下使用數據分析和機器學習
- 從數據中獲取洞察，以做出更好的商業決策
- 通過現實的實作練習來增強您的經驗和信心

**書籍描述**

釋放數據的力量，以達成您的行銷目標，這本針對商業的數據科學實用指南將幫助您。

本書將幫助您開始成為行銷分析大師的旅程，使用 Python 進行相關數據集的操作，並通過解決引人入勝的練習和活動來建立您的實務技能，這些活動模擬真實的市場分析專案。

您將學會像數據科學家一樣思考，建立您的問題解決能力，並發現如何以新的方式看待數據，以提供商業洞察並做出智能的數據驅動決策。

除了學習如何清理、探索和視覺化數據外，您還將實施機器學習算法並建立模型以進行預測。在閱讀本書的過程中，您將使用 Python 工具來分析銷售、視覺化廣告數據、預測收入、解決客戶流失問題，並實施客戶細分以了解行為。

到本書結束時，您將擁有知識、技能和信心，能夠實施數據科學和機器學習技術，以更好地理解您的行銷數據並改善決策。

**您將學到的內容**

- 使用 pandas 載入、清理和探索銷售和行銷數據
- 使用真實數據集和分析工具形成和測試假設
- 使用 Matplotlib 視覺化客戶行為中的模式
- 使用隨機森林和支持向量機（SVM）等先進的機器學習模型
- 使用各種無監督學習算法進行客戶細分
- 使用監督學習技術進行銷售預測
- 評估和比較不同模型以獲得最佳結果
- 通過超參數調整和 SMOTE 優化模型

**本書適合誰**

這本行銷書籍適合任何想學習如何使用 Python 進行尖端行銷分析的人。無論您是想轉向行銷的開發人員，還是想學習更複雜工具和技術的行銷分析師，本書都將引導您走上正確的道路。

對 Python 的基本先前知識和處理數據的經驗將幫助您更輕鬆地理解本書。

作者簡介

Mirza Rahim Baig is an avid problem solver who uses deep learning and artificial intelligence to solve complex business problems. He has more than a decade of experience in creating value from data, harnessing the power of the latest in machine learning and AI with proficiency in using unstructured and structured data across areas like marketing, customer experience, catalog, supply chain, and other eCommerce sub-domains. Rahim is also a teacher - designing, creating, teaching data science for various learning platforms. He loves making the complex easy to understand. He is also the co-author of The Deep Learning Workshop, a hands-on guide to start your deep learning journey and build your own next-generation deep learning models.

Gururajan Govindan is a data scientist, intrapreneur, and trainer with more than seven years of experience working across domains such as finance and insurance. He is also an author of The Data Analysis Workshop, a book focusing on data analytics. He is well known for his expertise in data-driven decision-making and machine learning with Python.

Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering. He has a keen interest in programming and AI and has applied that interest in mechanical engineering projects. He has also written multiple blogs on OpenCV, deep learning, and computer vision. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar. He is also an author of Computer Vision Workshop, a book focusing on OpenCV and its applications in real-world scenarios; as well as, Machine Learning for OpenCV (2nd edition) - which introduces how to use OpenCV for machine learning applications.

作者簡介(中文翻譯)

**Mirza Rahim Baig** 是一位熱衷於解決問題的專家，他利用深度學習和人工智慧來解決複雜的商業問題。他擁有超過十年的數據價值創造經驗，善於運用最新的機器學習和人工智慧技術，並熟練使用結構化和非結構化數據，涵蓋市場行銷、客戶體驗、產品目錄、供應鏈及其他電子商務子領域。Rahim 同時也是一位教師，為各種學習平台設計、創建和教授數據科學課程。他喜歡將複雜的概念簡化為易於理解的內容。他也是《The Deep Learning Workshop》的共同作者，這是一本實用指南，幫助讀者開始深度學習之旅並建立自己的下一代深度學習模型。

**Gururajan Govindan** 是一位數據科學家、內部創業者和培訓師，擁有超過七年的經驗，工作範圍涵蓋金融和保險等領域。他也是《The Data Analysis Workshop》的作者，這本書專注於數據分析。他以在數據驅動的決策制定和使用 Python 進行機器學習方面的專業知識而聞名。

**Vishwesh Ravi Shrimali** 畢業於 BITS Pilani，主修機械工程。他對程式設計和人工智慧有濃厚的興趣，並將這種興趣應用於機械工程項目中。他還撰寫了多篇關於 OpenCV、深度學習和計算機視覺的部落格文章。當他不在寫部落格或從事項目時，他喜歡長時間散步或彈奏他的原聲吉他。他也是《Computer Vision Workshop》的作者，這本書專注於 OpenCV 及其在現實場景中的應用；此外，他還撰寫了《Machine Learning for OpenCV（第二版）》，介紹如何使用 OpenCV 進行機器學習應用。

目錄大綱

  1. Data Preparation and Cleaning
  2. Data Exploration and Visualization
  3. Unsupervised Learning and Customer Segmentation
  4. Evaluating and Choosing the Best Segmentation Approach
  5. Predicting Customer Revenue Using Linear Regression
  6. More Tools and Techniques for Evaluating Regression Models
  7. Supervised Learning: Predicting Customer Churn
  8. Fine Tuning Classification Algorithms
  9. Multiclass Classification Algorithms

目錄大綱(中文翻譯)


  1. Data Preparation and Cleaning

  2. Data Exploration and Visualization

  3. Unsupervised Learning and Customer Segmentation

  4. Evaluating and Choosing the Best Segmentation Approach

  5. Predicting Customer Revenue Using Linear Regression

  6. More Tools and Techniques for Evaluating Regression Models

  7. Supervised Learning: Predicting Customer Churn

  8. Fine Tuning Classification Algorithms

  9. Multiclass Classification Algorithms

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