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Data Science for Marketing Analytics

Data Science for Marketing Analytics - Second Edition

By : Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali
4.3 (203)
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Data Science for Marketing Analytics

Data Science for Marketing Analytics

4.3 (203)
By: Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali

Overview of this book

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.
Table of Contents (11 chapters)
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Preface

Data Models and Structured Data

When you build an analytical solution, the first thing that you need to do is to build a data model. A data model is an overview of the data sources that you will be using, their relationships with other data sources, where exactly the data from a specific source is going to be fetched, and in what form (such as an Excel file, a database, or a JSON from an internet source).

Note

Keep in mind that the data model evolves as data sources and processes change.

A data model can contain data of the following three types:

  • Structured Data: Also known as completely structured or well-structured data, this is the simplest way to manage information. The data is arranged in a flat tabular form with the correct value corresponding to the correct attribute. There is a unique column, known as an index, for easy and quick access to the data, and there are no duplicate columns. For example, in Figure 1.1, employee_id is the unique column. Using the data...
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Data Science for Marketing Analytics
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