Data Analysis with Python (ebook)(audiobook)(audiobook)

Spis treści

• Data Analysis with Python
  • Table of Contents
  • Data Analysis with Python
    • Why subscribe?
    • PacktPub.com
  • Contributors
    • About the author
    • About the reviewers
    • Packt is searching for authors like you
  • Preface
    • Why am I writing this book?
    • Who this book is for
    • What this book covers
    • To get the most out of this book
      • Download the example code files
      • Download the color images
      • Conventions used
    • Get in touch
      • Reviews
  • 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
      • Why are Notebooks so popular?
    • Summary
  • 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
    • Summary
  • 3. Accelerate your Data Analysis with Python Libraries
    • Anatomy of a PixieApp
      • Routes
      • Generating requests to routes
      • A GitHub project tracking sample application
      • Displaying the search results in a table
      • Invoking the PixieDust display() API using pd_entity attribute
      • Invoking arbitrary Python code with pd_script
      • Making the application more responsive with pd_refresh
      • Creating reusable widgets
    • Summary
  • 4. Publish your Data Analysis to the Web - the PixieApp Tool
    • Overview of Kubernetes
    • Installing and configuring the PixieGateway server
      • PixieGateway server configuration
      • PixieGateway architecture
      • Publishing an application
      • Encoding state in the PixieApp URL
      • Sharing charts by publishing them as web pages
      • PixieGateway admin console
      • Python Console
      • Displaying warmup and run code for a PixieApp
    • Summary
  • 5. Python and PixieDust Best Practices and Advanced Concepts
    • Use @captureOutput decorator to integrate the output of third-party Python libraries
      • Create a word cloud image with @captureOutput
    • Increase modularity and code reuse
      • Creating a widget with pd_widget
      • PixieDust support of streaming data
        • Adding streaming capabilities to your PixieApp
      • Adding dashboard drill-downs with PixieApp events
      • Extending PixieDust visualizations
      • Debugging
        • Debugging on the Jupyter Notebook using pdb
        • Visual debugging with PixieDebugger
        • Debugging PixieApp routes with PixieDebugger
        • Troubleshooting issues using PixieDust logging
        • Client-side debugging
    • Run Node.js inside a Python Notebook
    • Summary
  • 6. Analytics Study: AI and Image Recognition with TensorFlow
    • What is machine learning?
    • What is deep learning?
    • Getting started with TensorFlow
      • Simple classification with DNNClassifier
    • Image recognition sample application
      • Part 1 Load the pretrained MobileNet model
      • Part 2 Create a PixieApp for our image recognition sample application
      • Part 3 Integrate the TensorBoard graph visualization
      • Part 4 Retrain the model with custom training data
    • Summary
  • 7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis
    • Getting started with Apache Spark
      • Apache Spark architecture
      • Configuring Notebooks to work with Spark
    • Twitter sentiment analysis application
    • Part 1 Acquiring the data with Spark Structured Streaming
      • Architecture diagram for the data pipeline
      • Authentication with Twitter
      • Creating the Twitter stream
      • Creating a Spark Streaming DataFrame
      • Creating and running a structured query
      • Monitoring active streaming queries
      • Creating a batch DataFrame from the Parquet files
    • Part 2 Enriching the data with sentiment and most relevant extracted entity
      • Getting started with the IBM Watson Natural Language Understanding service
    • Part 3 Creating a real-time dashboard PixieApp
      • Refactoring the analytics into their own methods
      • Creating the PixieApp
    • Part 4 Adding scalability with Apache Kafka and IBM Streams Designer
      • Streaming the raw tweets to Kafka
      • Enriching the tweets data with the Streaming Analytics service
      • Creating a Spark Streaming DataFrame with a Kafka input source
    • Summary
  • 8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting
    • Getting started with NumPy
      • Creating a NumPy array
      • Operations on ndarray
      • Selections on NumPy arrays
      • Broadcasting
    • Statistical exploration of time series
      • Hypothetical investment
      • Autocorrelation function (ACF) and partial autocorrelation function (PACF)
    • Putting it all together with the StockExplorer PixieApp
      • BaseSubApp base class for all the child PixieApps
      • StockExploreSubApp first child PixieApp
      • MovingAverageSubApp second child PixieApp
      • AutoCorrelationSubApp third child PixieApp
    • Time series forecasting using the ARIMA model
      • Build an ARIMA model for the MSFT stock time series
      • StockExplorer PixieApp Part 2 add time series forecasting using the ARIMA model
    • Summary
  • 9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis
    • Introduction to graphs
      • Graph representations
      • Graph algorithms
      • Graph and big data
    • Getting started with the networkx graph library
      • Creating a graph
      • Visualizing a graph
    • Part 1 Loading the US domestic flight data into a graph
      • Graph centrality
    • 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
    • Summary
  • 10. The Future of Data Analysis and Where to Develop your Skills
    • Forward thinking what to expect for AI and data science
    • References
  • A. PixieApp Quick-Reference
    • Annotations
    • Custom HTML attributes
    • Methods
  • Other Books You May Enjoy
    • Leave a review let other readers know what you think
  • Index

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