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Data Science for Marketing Analytics - Second Edition

You're reading from  Data Science for Marketing Analytics - Second Edition

Product type Book
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Pages 636 pages
Edition 2nd Edition
Languages
Concepts
Authors (3):
Mirza Rahim Baig Mirza Rahim Baig
Profile icon Mirza Rahim Baig
LinkedIn
Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.
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Gururajan Govindan Gururajan Govindan
Profile icon Gururajan Govindan
LinkedIn
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.
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Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Profile icon Vishwesh Ravi Shrimali
LinkedIn
Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.
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Table of Contents (11) Chapters

Preface
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 Appendix

Introduction

"How does this data make sense to the business?" It's a critical question you'll need to ask every time you start working with a new, raw dataset. Even after you clean and prepare raw data, you won't be able to derive actionable insights from it just by scanning through thousands of rows and columns. To be able to present the data in a way that it provides value to the business, you may need group similar rows, re-arrange the columns, generate detailed charts, and more. Manipulating and visualizing the data to uncover insights that stakeholders can easily understand and implement is a key skill in a marketing analyst's toolbox. This chapter is all about learning that skill.

In the last chapter, you learned how you can transform raw data with the help of pandas. You saw how to clean the data and handle the missing values after which the data can be structured into a tabular form. The structured data can be further analyzed so that meaningful information can be extracted from it.

In this chapter, you'll discover the functions and libraries that help you explore and visualize your data in greater detail. You will go through techniques to explore and analyze data through solving some problems critical for businesses, such as identifying attributes useful for marketing, analyzing key performance indicators, performing comparative analyses, and generating insights and visualizations. You will use the pandas, Matplotlib, and seaborn libraries in Python to solve these problems.

Let us begin by first understanding how we can identify the attributes that will help us derive insights from our data.

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Data Science for Marketing Analytics - Second Edition
Published in: Sep 2021 Publisher: Packt ISBN-13: 9781800560475
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Authors (3)

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Profile icon Mirza Rahim Baig
LinkedIn
Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.
Read more
See other products by Mirza Rahim Baig
Profile icon Gururajan Govindan
LinkedIn
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.
See other products by Gururajan Govindan
Profile icon Vishwesh Ravi Shrimali
LinkedIn
Vishwesh Ravi Shrimali graduated from BITS Pilani, where he studied mechanical engineering, in 2018. He also completed his Masters in Machine Learning and AI from LJMU in 2021. He has authored - Machine learning for OpenCV (2nd edition), Computer Vision Workshop and Data Science for Marketing Analytics (2nd edition) by Packt. When he is not writing blogs or working on projects, he likes to go on long walks or play his acoustic guitar.
See other products by Vishwesh Ravi Shrimali
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