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Data Analysis with Python

Data Analysis with Python

By : David Taieb
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Data Analysis with Python

Data Analysis with Python

5 (1)
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 (14 chapters)
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3. Accelerate your Data Analysis with Python Libraries
13
Index

Chapter 1. Programming and Data Science – A New Toolset

"Data is a precious thing and will last longer than the systems themselves."

Tim Berners-Lee, inventor of the World Wide Web

(https://en.wikipedia.org/wiki/Tim_Berners-Lee)

In this introductory chapter, I'll start the conversation by attempting to answer a few fundamental questions that will hopefully provide context and clarity for the rest of this book:

  • What is data science and why it's on the rise
  • Why is data science here to stay
  • Why do developers need to get involved in data science

Using my experience as a developer and recent data science practitioner, I'll then discuss a concrete data pipeline project that I worked on and a data science strategy that derived from this work, which is comprised of three pillars: data, services, and tools. I'll end the chapter by introducing Jupyter Notebooks which are at the center of the solution I'm proposing in this book.

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Data Analysis with Python
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