Data Analysis with Python

Data Analysis with Python

By : David Taieb

Buy this Book

Data Analysis with Python

By: David Taieb

Buy this Book

Overview of this book

Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence.

Data Analysis with Python

Contributors

Preface

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Free Chapter

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

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

Accelerate your Data Analysis with Python Libraries

Anatomy of a PixieApp

Summary

Publish your Data Analysis to the Web - the PixieApp Tool

Overview of Kubernetes

Installing and configuring the PixieGateway server

Summary

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

Summary

Analytics Study: AI and Image Recognition with TensorFlow

What is machine learning?

What is deep learning?

Getting started with TensorFlow

Image recognition sample application

Summary

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

Summary

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

Summary

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

Summary

The Future of Data Analysis and Where to Develop your Skills

Forward thinking – what to expect for AI and data science

References

PixieApp Quick-Reference

Annotations

Custom HTML attributes

Methods

Index

Customer Reviews

5 star

4 star

3 star

2 star

1 star

Summary

In this chapter, we've explored various advanced concepts, tools, and best practices that added more tools to our toolbox, ranging from advanced techniques for PixieApps (Streaming, how to implement a route by integrating third-party libraries with @captureOutput, PixieApp events, and better modularity with pd_app), to essential developer tools like the PixieDebugger. We've also covered the details of how to create your own custom visualization using the PixieDust display() API. We also discussed pixiedust_node, which is an extension of the PixieDust framework that lets developers who are more comfortable with JavaScript work with data in their favorite language.

Throughout the remainder of this book, we are going to put all these lessons learned to good use by building industry use case data pipelines, starting with a Deep Learning Visual Recognition application in Chapter 6, Analytics Study: AI and Image Recognition with TensorFlow.

A developer quick-reference guide for the PixieApp...

Data Analysis with Python

By : David Taieb

Data Analysis with Python

By: David Taieb

Overview of this book

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