Practical Data Science with Python 3
Synthesizing Actionable Insights from Data
Like traveling, writing a book is more enjoyable when accompanied by your family. I am thankful to my wife, Zorica, and sons, Andrej and Stefan, for all their great support.
This book amalgamates data science and software engineering in a pragmatic manner. It guides the reader through topics from these worlds and exemplifies concepts through software. As a reader, you will gain insight into areas rarely covered in textbooks, since they are hard to explain and illustrate. You will see the Cynefin framework in action via examples that give an overarching context and systematic approach for your data science endeavors.
The book also introduces you to the most useful Python 3 data science frameworks and tools: Numpy, Pandas, scikit-learn, matplotlib, Seaborn, Dask, Apache Spark, PyTorch, and other auxiliary frameworks. All examples are self-contained and allow you to reproduce every piece of content from the book, including graphs. The exercises at the end of each chapter advise you how to further deepen your knowledge.
Finally, the book explains, again using lots of examples, all phases of a data science life cycle model: from project initiation to data exploration and retrospection. The aim is to equip you with necessary comprehension pertaining to major areas of data science so that you may see the forest for the trees .
I would like to thank Apress for giving me an opportunity and full support for writing this book about data science. Comments and help from James Markham, Aditee Mirashi, and Celestin Suresh John were invaluable.
I am also grateful for excellent remarks from Jojo John Moolayil, who was the technical reviewer on this book.
Table of Contents
About the Author and About the Technical Reviewer
About the Author
is a Senior Member of IEEE and Professional Member of ACM. He is an IEEE Software Engineering Certified Instructor. Ervin is an owner of the software consulting company Expro I.T. Consulting, Serbia. He has an MSc in computer science, and a PhD in electrical engineering (his thesis was an application of software engineering and computer science in the domain of electrical power systems). Ervin is also a technical advisor of the open-source project Mainflux.
About the Technical Reviewer
is an artificial intelligence professional and published author of three books on machine learning, deep learning, and IoT. He is currently working with Amazon Web Services as a Research Scientist – AI in AWS’s office in Vancouver, BC.
Jojo was born and raised in Pune, India, and graduated from the University of Pune with a major in Information Technology Engineering. His passion for problem-solving and data-driven decision-making led him to start a career with Mu Sigma Inc., the world’s largest pure-play analytics provider. There, he was responsible for developing machine learning and decision science solutions for large, complex problems for healthcare and telecom giants. He later worked with Flutura (an IoT analytics startup) and General Electric with a focus on industrial AI, in Bangalore, India.
In his current role with AWS, he works on researching and developing large-scale AI solutions for combating fraud and enriching the customer’s payment experience in the cloud. He is also actively involved as a tech reviewer and AI consultant with leading publishers and has reviewed over a dozen books on machine learning, deep learning, and business analytics.
You can reach out to Jojo at https://www.jojomoolayil.com/ , https://www.linkedin.com/in/jojo62000 , and https://twitter.com/jojo62000 .