The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies.

If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud.

• Explore ways that predictive analytics can provide direct input back to your business
• Understand mathematical tools commonly used in predictive analytics
• Learn the development frameworks used in predictive analytics applications
• Appreciate the role of predictive analytics in the machine learning process
• Examine industry implementations of predictive analytics
• Build, train, and retrain predictive models using Python and TensorFlow

Table of Contents

Chapter 1. Data Analytics in the Modern Enterprise

Chapter 2. Predictive Analytics: An Operational Necessity

Chapter 3. The Mathematics and Algorithms Behind Predictive Analytics

Chapter 4. Working with Data

Chapter 5. Python and scikit-learn for Predictive Analytics

Chapter 6. TensorFlow and Keras for Predictive Analytics

Chapter 7. Predictive Analytics for Business Problem-Solving

Chapter 8. Exploring AWS Cloud Provider Services for Al/ML

Chapter 9. Food for Thought

About the Author

Nooruddin Abbas Ali is a Principal Solutions Architect at MongoDB. This select global team works with innovative customers and partners looking to disrupt their respective industries. It is the team's belief that data is the key to unlocking business potential and bringing about the sustainable disruption that moves the human race forward. He has been in the tech and service management industry for over 20 years, where he has held several positions in development, professional services, management, pre-sales, and thought leadership. He has worked for 0-1 startups, large public cloud providers (Alibaba Cloud), hypergrowth data platform companies (MongoDB), and end-to-end large system integrators.

He has been working with distributed data systems and cloud technologies for over a decade. Nooruddin has experience and credentials in multiple cloud technologies, architecture best practices, Aritificial Intelligence (AI) and Application Data platforms. This unique combination of data and cloud skills, coupled with his experience at various levels of the enterprise spectrum, has allowed him to amass the knowledge and field experience required to build this book that is multi-faceted yet in-depth on core foundations of predictive analytics.