Book Image

Data Analysis with Python

By : David Taieb
Book Image

Data Analysis with Python

By: David Taieb

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.
Table of Contents (16 chapters)
Data Analysis with Python
Contributors
Preface
Other Books You May Enjoy
3
Accelerate your Data Analysis with Python Libraries
Index

Chapter 2. Python and Jupyter Notebooks to Power your Data Analysis

"The Best Line of Code is the One You Didn't Have to Write!"

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In the previous chapter, I gave a developer's perspective on data science based on real experience and discussed three strategic pillars required for successful deployment with in the enterprise: data, services, and tools. I also discussed the idea that data science is not only the sole purview of data scientists, but rather a team sport with a special role for developers.

In this chapter, I'll introduce a solution—based on Jupyter Notebooks, Python, and the PixieDust open source library—that focuses on three simple goals:

  • Democratizing data science by lowering the barrier to entry for non-data scientists

  • Increasing collaboration between developers and data scientists

  • Making it easier to operationalize data science analytics

Note

This solution only focuses on the tools pillar and not on data and services, which should be implemented independently, although we...