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Key benefits

Bridge your data analysis with the power of programming, complex algorithms, and AI

Use Python and its extensive libraries to power your way to new levels of data insight

Work with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time series

Explore this modern approach across with key industry case studies and hands-on projects

Description

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.

Who is this book for?

This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

What you will learn

A new toolset that has been carefully crafted to meet for your data analysis challenges

Full and detailed case studies of the toolset across several of today's key industry contexts

Become super productive with a new toolset across Python and Jupyter Notebook

Look into the future of data science and which directions to develop your skills next

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Mex$902.99

Hands-On Predictive Analytics with Python

Mex$1004.99

Hands-On Machine Learning for Algorithmic Trading

Mex$1353.99

Total Mex$ 3,261.97

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Raj Singh Jan 09, 2019

"Data Analysis with Python: A Modern Approach" is a very interesting addition to the popular literature in this hot technical area. There are numerous books out there that teach the underlying technologies and mathematical techniques used in data science, from Python to linear regression to neural network models. But there are very few books that detail real-world experience in operationalizing these skills. This is in my opinion the key contribution of this book to the data science community. So few people are willing to describe in detail the architecture of years of their work, plus also have open source libraries available that the reader can use to apply and then extend what they learn in their own projects.Before you buy this book you should understand what it's not. If you're looking for a book that will serve as a class in data science, this isn't it. Textbook-style books, by design, start with the assumption that the readers have a certain shared background. Then they build on that background chapter-by-chapter teaching a set of new skills. You are expected to apply what you learn in previous chapters in order to understand those that follow. This is also not a book that will teach you how to program. While there are a lot of code samples, they serve more to illustrate a functional point rather than to teach Python, or how to write PixieApp applications using PixieDust.What this book is good at is putting data science in the context of the broader business challenge, which is to apply data science to real-life operational systems, and share the insights derived by data science using web architectures with which most developers are familiar. If you're a moderately experienced programmer with some Python and data science skills, this book can really broaden your horizons to understand how your work can better be utilized throughout your organization, and how you can get going with open source tools instead of investing immediately in pricey software from the big data science players like IBM, Oracle, Matlab or Tableau. Hey you might end up not needing that expensive stuff once you get good with Jupyter notebooks and the Pixiedust tools!

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Data Analysis with Python: A Modern Approach

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