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 10. The Future of Data Analysis and Where to Develop your Skills

"We are creating and hiring to fill "new collar" jobs – entirely new roles in areas such as cybersecurity, data science, artificial intelligence and cognitive business."

Ginni Rometty, IBM Chairman, and CEO

Once again, let me thank you and congratulate you, the reader, for the long journey of reading through these long chapters and perhaps trying some or all of the sample code provided. I tried to provide a good mix between diving into the fundamentals of a particular topic, such as deep learning or time series analysis, and giving comprehensive example code for the practitioner. I especially hope that you found the idea of tightly integrating the data science analytics with the PixieApp application programming model in a single Jupyter Notebook interesting and novel. But, most importantly, I hope that you found it useful and something you can reuse in your...