Table of Contents

Chapter 1: What is Productive and Efficient Data Science.- Chapter 2: Better Programming Principles for Efficient Data Science.- Chapter 3: How to Use Python Data Science Packages more Productively.- Chapter 4: Writing Machine Learning Code More Productively.- Chapter 5: Modular and Productive Deep Learning Code.- Chapter 6: Build Your Own Machine Learning Estimator/Package.- Chapter 7: Some Cool Utility Packages.- Chapter 8: Testing the Machine Learning Code.- Chapter 9: Memory and Timing Profiling.- Chapter 10: Scalable Data Science.- Chapter 11: Parallelized Data Science.- Chapter 12: GPU-Based Data Science for High Productivity.- Chapter 13: Other Useful Skills to Master.- Chapter 14: Wrapping It Up.

About the Author

Dr. Tirthajyoti Sarkar lives in the San Francisco Bay area works as a Data Science and Solutions Engineering Manager at Adapdix Corp., where he architects Artificial intelligence and Machine learning solutions for edge-computing based systems powering the Industry 4.0 and Smart manufacturing revolution across a wide range of industries. Before that, he spent more than a decade developing best-in-class semiconductor technologies for power electronics. 
He has published data science books, and regularly contributes highly cited AI/ML-related articles on top platforms such as KDNuggets and Towards Data Science. Tirthajyoti has developed multiple open-source software packages in the field of statistical modeling and data analytics. He has 5 US patents and more than thirty technical publications in international journals and conferences. 
He conducts regular workshops and participates in expert panels on various AI/ML topics and contributes tothe broader data science community in numerous ways. Tirthajyoti holds a Ph.D. from the University of Illinois and a B.Tech degree from the Indian Institute of Technology, Kharagpur.