David Taieb - Thoughtful Data Science: A Programmer’s Toolset for Data Analysis and Artificial Intelligence with Python, Jupyter Notebook, and PixieDust
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- Book:Thoughtful Data Science: A Programmer’s Toolset for Data Analysis and Artificial Intelligence with Python, Jupyter Notebook, and PixieDust
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Bridge the gap between developer and data scientist by creating a modern open-source, Python-based toolset that works with Jupyter Notebook, and PixieDust.
Key Features- Think deeply as a developer about your strategy and toolset in data science
- Discover the best tools that will suit you as a developer in your data analysis
- Accelerate the road to data insight as a programmer using Jupyter Notebook
- Deep dive into multiple industry data science use cases
Thoughtful Data Science brings new strategies and a carefully crafted programmers toolset to work with modern, cutting-edge data analysis. This new approach is designed specifically to give developers more efficiency and power to create cutting-edge data analysis and artificial intelligence insights.
Industry expert David Taieb bridges the gap between developers and data scientists by creating a modern open-source, Python-based toolset that works with Jupyter Notebook, and PixieDust. Youll find the right balance of strategic thinking and practical projects throughout this book, with extensive code files and Jupyter projects that you can integrate with your own data analysis.
David Taieb introduces four projects designed to connect developers to important industry use cases in data science. The first is an image recognition application with TensorFlow, to meet the growing importance of AI in data analysis. The second analyses social media trends to explore big data issues and natural language processing. The third is a financial portfolio analysis application using time series analysis, pivotal in many data science applications today. The fourth involves applying graph algorithms to solve data problems. Taieb wraps up with a deep look into the future of data science for developers and his views on AI for data science.
What you will learn- Bridge the gap between developer and data scientist with a Python-based toolset
- Get the most out of Jupyter Notebooks with new productivity-enhancing tools
- Explore and visualize data using Jupyter Notebooks and PixieDust
- Work with and assess the impact of artificial intelligence in data science
- Work with TensorFlow, graphs, natural language processing, and time series
- Deep dive into multiple industry data science use cases
- Look into the future of data analysis and where to develop your skills
This book is for established developers who want to bridge the gap between programmers and data scientists. With the introduction of PixieDust from its creator, the book will also be a great desk companion for the already accomplished Data Scientist. Some fluency in data interpretation and visualization is also assumed since this book addresses data professionals such as business and general data analysts. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.
Table of Contents- Perspectives on Data Science from a developer
- Data Science at scale with Jupyter Notebooks and PixieDust
- PixieApp under the hood
- Deploying PixieAoos to the web with the PixieGateway Server
- Best Practices and Advanced PixieDust Concepts
- Image Recognition with TensorFlow
- Big Data Twitter Sentiment Analysis
- Financial Time Series Analysis and Forecasting
- US domestic flight data analysis using Graphs
- Final thoughts
David Taieb: author's other books
Who wrote Thoughtful Data Science: A Programmer’s Toolset for Data Analysis and Artificial Intelligence with Python, Jupyter Notebook, and PixieDust? Find out the surname, the name of the author of the book and a list of all author's works by series.
![Dan Toomey [Dan Toomey] - Jupyter for Data Science](/uploads/posts/book/119624/thumbs/dan-toomey-dan-toomey-jupyter-for-data-science.jpg)
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Copyright 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Acquisition Editors: Frank Pohlmann, Suresh M Jain
Project Editors: Savvy Sequeira, Kishor Rit
Content Development Editor: Alex Sorrentino
Technical Editor: Bhagyashree Rai
Proofreader: Safis Editing
Indexers: Priyanka Dhadke
Graphics: Tom Scaria
Production Coordinator: Sandip Tadge
First published: June 2018
Production reference: 1300718
Published by Packt Publishing Ltd.
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ISBN 978-1-78883-996-9
www.packtpub.com
To Alexandra, Solomon, Zachary, Victoria and Charlotte:
Thank you for your support, unbounded love, and infinite patience. I would not have been able to complete this work without all of you.
To Fernand and Gisele:
Without whom I wouldn't be where I am today. Thank you for your continued guidance all these years.
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David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.
I want to give special thanks to all of the following dear friends at IBM who contributed to the development of PixieDust and/or provided invaluable support during the writing of this book: Brad Noble, Jose Barbosa, Mark Watson, Raj Singh, Mike Broberg, Jessica Mantaro, Margriet Groenendijk, Patrick Titzler, Glynn Bird, Teri Chadbourne, Bradley Holt, Adam Cox, Jamie Jennings, Terry Antony, Stephen Badolato, Terri Gerber, Peter May, Brady Paterson, Kathleen Francis, Dan O'Connor, Muhtar (Burak) Akbulut, Navneet Rao, Panos Karagiannis, Allen Dean, and Jim Young.
Margriet Groenendijk is a data scientist and developer advocate for IBM. She has a background in climate research, where, at the University of Exeter, she explored large observational datasets and the output of global scale weather and climate models to understand the impact of land use on climate. Prior to that, she explored the effect of climate on the uptake of carbon from the atmosphere by forests during her PhD research at the Vrije Universiteit in Amsterdam.
Now adays, she explores ways to simplify working with diverse data using open source tools, IBM Cloud, and Watson Studio. She has experience with cloud services, databases, and APIs to access, combine, clean, and store different types of data. Margriet uses time series analysis, statistical data analysis, modeling and parameter optimisation, machine learning, and complex data visualization. She writes blogs and speaks about these topics at conferences and meetups.
va barbosa is a developer advocate for the Center for Open-Source Data & AI Technologies, where he helps developers discover and make use of data and machine learning technologies. This is fueled by his passion to help others, and guided by his enthusiasm for open source technology.
Always looking to embrace new challenges and fulfill his appetite for learning, va immerses himself in a wide range of technologies and activities. He has been an electronic technician, support engineer, software engineer, and developer advocate.
When not focusing on the developer experience, va enjoys dabbling in photography. If you can't find him in front of a computer, try looking behind a camera.
If you're interested in becoming an author for Packt, please visit authors.packtpub.com and apply today. We have worked with thousands of developers and tech professionals, just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for, or submit your own idea.
"Developers are the most-important, most-valuable constituency in business today, regardless of industry." |
| -- Stephen O'Grady, author of The New Kingmakers |
First, let me thank you and congratulate you, the reader, for the decision to invest some of your valuable time to read this book. Throughout the chapters to come, I will take you on a journey of discovering or even re-discovering data science from the perspective of a developer and will develop the theme of this book which is that data science is a team sport and that if it is to be successful, developers will have to play a bigger role in the near future and better collaborate with data scientists. However, to make data science more inclusive to people of all backgrounds and trades, we first need to democratize it by making data simple and accessible this is in essence what this book is about.
As I'll explain in more detail in ,
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