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  • Book Overview & Buying Data Science for Marketing Analytics
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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

Summary

In this chapter, you have learned how to structure datasets by arranging them in a tabular format. Then, you learned how to combine data from multiple sources. You also learned how to get rid of duplicates and needless columns. Along with that, you discovered how to effectively address missing values in your data. By learning how to perform these steps, you now have the skills to make your data ready for further analysis.

Data processing and wrangling are the most important steps in marketing analytics. Around 60% of the efforts in any project are spent on data processing and exploration. Data processing when done right can unravel a lot of value and insights. As a marketing analyst, you will be working with a wide variety of data sources, and so the skills you have acquired in this chapter will help you to perform common data cleaning and wrangling tasks on data obtained in a variety of formats.

In the next chapter, you will enhance your understanding of pandas and...

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Data Science for Marketing Analytics
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