Applied Data Science Using PySpark Learn the End-to-End Predictive Model-Building Cycle

This comprehensive guide, featuring hand-picked examples of daily use cases, will walk you through the end-to-end predictive model-building cycle using the latest techniques and industry tricks. In Chapters 1, 2, and 3, we will begin by setting up the environment and covering the basics of PySpark, focusing on data manipulation. Chapter 4 delves into the art of variable selection, demonstrating various techniques available in PySpark. In Chapters 5, 6, and 7, we explore machine learning algorithms, their implementations, and fine-tuning techniques. Chapters 8 and 9 will guide you through machine learning pipelines and various methods to operationalize and serve models using Docker/API. Chapter 10 will demonstrate how to unlock the power of predictive models to create a meaningful impact on your business. Chapter 11 introduces some of the most widely used and powerful modeling frameworks to unlock real value from data.

In this new edition, you will learn predictive modeling frameworks that can quantify customer lifetime values and estimate the return on your predictive modeling investments. This edition also includes methods to measure engagement and identify actionable populations for effective churn treatments. Additionally, a dedicated chapter on experimentation design has been added, covering steps to efficiently design, conduct, test, and measure the results of your models. All code examples have been updated to reflect the latest stable version of Spark.

You will:

• Gain an overview of end-to-end predictive model building
• Understand multiple variable selection techniques and their implementations
• Learn how to operationalize models
• Perform data science experiments and learn useful tips

Chapter 1: Setting up the Pyspark Environment.- Chapter 2: PySpark Basics .- Chapter 3: Variable Selection.- Chapter 4: Variable Selection.- Chapter 5: Supervised Learning Algorithms.- Chapter 6: Model Evaluation.- Chapter 7: Unsupervised Learning and Recommendation Algorithms.- Chapter 8: Machine Learning Flow and Automated Pipelines.- Chapter 9: Deploying machine learning models.- Chapter 10: Experimentation.- Chapter 11: Modeling Frameworks.

Ramcharan Kakarla is currently Principal ML at Altice USA. He is a passionate data science and artificial intelligence advocate with 10 years of experience. He holds a master’s degree from Oklahoma State University with specialization in data mining. He is currently pursuing masters in management from University of California, LA. Prior to UCLA and OSU, he received his bachelor’s in electrical and electronics engineering from Sastra University in India. He was born and raised in the coastal town of Kakinada, India. He started his career working as a performance engineer with several Fortune 500 clients including State Farm, British Airways, Comcast and JP Morgan Chase. In his current role he is focused on building data science solutions and frameworks leveraging big data. He has published several papers and posters in the field of predictive analytics. He served as SAS Global Ambassador for the year 2015.

Sundar Krishnan is a Senior Data Science Manager at CVS Health. He has 12+ years of extensive experience leading cross-functional Data Science teams and is an AI, ML, and cloud platform expert. He has a proven track record of building high-performing teams and implementing innovative AI strategies to optimize operational costs and generate substantial revenue. Expert in 0 to 1 product development, successfully led teams from conception to market-ready products in Gen AI & data science. Sundar was born and raised in Tamil Nadu, India, and has a bachelor's degree from the Government College of Technology, Coimbatore. He completed his master's at Oklahoma State University, Stillwater. He blogs about his data science works on Medium in his spare time.

 Balaji Dhamodharan is an award winning global Data Science leader, guiding teams to develop and implement innovative, scalable ML solutions. He currently leads the AI/ML and MLOps strategy initiatives with NXP Semiconductors. He has over a decade of experience delivering large-scale technology solutions across diverse industries. His expertise spans Software Engineering, Enterprise AI platforms, AutoML, MLOps, and Generative AI technologies. Balaji holds Masters degrees in Management Information Systems and Data Science from Oklahoma State University and Indiana University. Originally from Chennai, India, Balaji currently resides in Austin, TX, USA.

Venkata Gunnu is a Senior Executive Director of Knowledge Management and Innovation at

JPM Chase. He is an executive with a successful background crafting enterprise-wide data and

data science solutions, GenAI, process improvements, and data and data science-centric

products. Concept to implementation strategist with demonstrated success controlling multiple

projects that elevate organizational efficiency while optimizing resources. Data-focused and

analytical with a track record of automating functions, standardizing data management protocol,and introducing new business intelligence solutions.