更新时间：2021-06-11 13:32:11
封面
版权页
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Contributors
About the author
About the reviewers
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Preface
Why am I writing this book?
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Chapter 1. Programming and Data Science – A New Toolset
What is data science
Is data science here to stay?
Why is data science on the rise?
What does that have to do with developers?
Putting these concepts into practice
Deep diving into a concrete example
Data pipeline blueprint
What kind of skills are required to become a data scientist?
IBM Watson DeepQA
Back to our sentiment analysis of Twitter hashtags project
Lessons learned from building our first enterprise-ready data pipeline
Data science strategy
Jupyter Notebooks at the center of our strategy
Summary
Chapter 2. Python and Jupyter Notebooks to Power your Data Analysis
Why choose Python?
Introducing PixieDust
SampleData – a simple API for loading data
Wrangling data with pixiedust_rosie
Display – a simple interactive API for data visualization
Filtering
Bridging the gap between developers and data scientists with PixieApps
Architecture for operationalizing data science analytics
Chapter 3. Accelerate your Data Analysis with Python Libraries
Anatomy of a PixieApp
Chapter 4. Publish your Data Analysis to the Web - the PixieApp Tool
Overview of Kubernetes
Installing and configuring the PixieGateway server
Chapter 5. Python and PixieDust Best Practices and Advanced Concepts
Use @captureOutput decorator to integrate the output of third-party Python libraries
Increase modularity and code reuse
Run Node.js inside a Python Notebook
Chapter 6. Analytics Study: AI and Image Recognition with TensorFlow
What is machine learning?
What is deep learning?
Getting started with TensorFlow
Image recognition sample application
Chapter 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis
Getting started with Apache Spark
Twitter sentiment analysis application
Part 1 – Acquiring the data with Spark Structured Streaming
Part 2 – Enriching the data with sentiment and most relevant extracted entity
Part 3 – Creating a real-time dashboard PixieApp
Part 4 – Adding scalability with Apache Kafka and IBM Streams Designer
Chapter 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting
Getting started with NumPy
Statistical exploration of time series
Putting it all together with the StockExplorer PixieApp
Time series forecasting using the ARIMA model
Chapter 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis
Introduction to graphs
Getting started with the networkx graph library
Part 1 – Loading the US domestic flight data into a graph
Part 2 – Creating the USFlightsAnalysis PixieApp
Part 3 – Adding data exploration to the USFlightsAnalysis PixieApp
Part 4 – Creating an ARIMA model for predicting flight delays
Chapter 10. The Future of Data Analysis and Where to Develop your Skills
Forward thinking – what to expect for AI and data science
References
Appendix A. PixieApp Quick-Reference
Annotations
Custom HTML attributes
Methods
Index
