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

Summary


Machine learning is a vast topic that enjoys tremendous growth, both in research and development. In this chapter, we've explored only a tiny fraction of the state of the art in connection with machine learning algorithms, namely, using a deep learning neural network to perform image recognition. For some readers who are just beginning to get familiar with machine learning, the sample PixieApps and associated algorithms code may be too deep to digest at one time. However, the underlying aim was to demonstrate how to iteratively build an application that leverages a machine learning model. We happened to use a convolutional neural network model for image recognition, but any other model would do.

Hopefully, you got a good idea of how PixieDust and the PixieApp programming model can help you with your own project, and I strongly encourage you to use this sample application as a starting point to build your own custom application using the machine learning of your choice. I also recommend...