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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 Manipulation

Now that we have deconstructed the structure of the pandas DataFrame down to its basics, the remainder of the wrangling tasks, that is, creating new DataFrames, selecting or slicing a DataFrame into its parts, filtering DataFrames for some values, joining different DataFrames, and so on, will become very intuitive. Let's start by selecting and filtering in the following section.

Note

Jupyter notebooks for the code examples listed in this chapter can be found at the following links: https://packt.link/xTvR2 and https://packt.link/PGIzK.

Selecting and Filtering in pandas

If you wanted to access a particular cell in a spreadsheet, you would do so by addressing that cell in the familiar format of (column name, row name). For example, when you call cell A63, A refers to the column and 63 refers to the row. Data is stored similarly in pandas, but as (row name, column name) and we can use the same convention to access cells in a DataFrame.

For example, look...

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