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

Book Description

Thoughtful Data Science brings new strategies and a carefully crafted programmer's 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. You'll 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

Who this book is for

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

1. Perspectives on Data Science from a Developer
2. Data Science at Scale with Jupyter Notebooks and PixieDust
3. PixieApp under the Hood
4. Deploying PixieApps to the Web with the PixieGateway Server
5. Best Practices and Advanced PixieDust Concepts
6. Image Recognition with TensorFlow
7. Big Data Twitter Sentiment Analysis
8. Financial Time Series Analysis and Forecasting
9. US Domestic Flight Data Analysis Using Graphs
10. Final Thoughts