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

Contributors

About the author

David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.

About the reviewers

Margriet Groenendijk is a data scientist and developer advocate for IBM. She has a background in climate research, where, at the University of Exeter, she explored large observational datasets and the output of global scale weather and climate models to understand the impact of land use on climate. Prior to that, she explored the effect of climate on the uptake of carbon from the atmosphere by forests during her PhD research at the Vrije Universiteit in Amsterdam.

Now adays, she explores ways to simplify working with diverse data using open source tools, IBM Cloud, and Watson Studio. She has experience with cloud services, databases, and APIs to access, combine, clean, and store different types of data. Margriet uses time series analysis, statistical data analysis, modeling and parameter optimisation, machine learning, and complex data visualization. She writes blogs and speaks about these topics at conferences and meetups.

va barbosa is a developer advocate for the Center for Open-Source Data & AI Technologies, where he helps developers discover and make use of data and machine learning technologies. This is fueled by his passion to help others, and guided by his enthusiasm for open source technology.

Always looking to embrace new challenges and fulfill his appetite for learning, va immerses himself in a wide range of technologies and activities. He has been an electronic technician, support engineer, software engineer, and developer advocate.

When not focusing on the developer experience, va enjoys dabbling in photography. If you can't find him in front of a computer, try looking behind a camera.

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