Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali
- 636 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Data Science for Marketing Analytics
A practical guide to forming a killer marketing strategy through data analysis with Python, 2nd Edition
Mirza Rahim Baig, Gururajan Govindan, Vishwesh Ravi Shrimali
About This Book
Turbocharge your marketing plans by making the leap from simple descriptive statistics in Excel to sophisticated predictive analytics with the Python programming language
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.
Frequently asked questions
Information
1. Data Preparation and Cleaning
Introduction
Data Models and Structured Data
- 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 in this column, you can run SQL queries and quickly access data at a specific row and column in the dataset easily. Furthermore, there are no empty rows, missing entries, or duplicate columns, thereby making this dataset quite easy to work with. What makes structured data most ubiquitous and easy to analyze is that it is stored in a standardized tabular format that makes adding, updating, deleting, and updating entries easy and programmable. With structured data, you may not have to put in much effort during the data preparation and cleaning stage. Data stored in relational databases such as MySQL, Amazon Redshift, and more are examples of structured data:
- Semi-structured data: You will not find semi-structured data to be stored in a strict, tabular hierarchy as you saw in Figure 1.1. However, it will still have its own hierarchies that group its elements and establish a relationship between them. For example, metadata of a song may include information about the cover art, the artist, song length, and even the lyrics. You can search for the artist's name and find the song you want. Such data does not have a fixed hierarchy mapping the unique column with rows in an expected format, and yet you can find the information you need. Another example of semi-structured data is a JSON file. JSON files are self-describing and can be understood easily. In Figure 1.2, you can see a JSON file that contains personally identifiable information of Jack Jones.Semi-structured data can be stored accurately in NoSQL databases.
- Unstructured data: Unstructured data may not be tabular, and even if it is tabular, the number of attributes or columns per observation may be completely arbitrary. The same data could be represented in different ways, and the attributes might not match each other, with values leaking into other parts. For example, think of reviews of various products stored in rows of an Excel sheet or a dump of the latest tweets of a company's Twitter profile. We can only search for specific keywords in that data, but we cannot store it in a relational database, nor will we be able to establish a concrete hierarchy between different elements or rows. Unstructured data can be stored as text files, CSV files, Excel files, images, and audio clips.