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

Part 2 – Enriching the data with sentiment and most relevant extracted entity


In this part, we enrich the Twitter data with sentiment information, for example, positive, negative, and neutral. We also want to extract the most relevant entity from the tweet, for example, sport, organization, and location. This extra information will be analyzed and visualized by the real-time dashboard that we'll build in the next section. The algorithms used to extract sentiment and entity from an unstructured text belong to a field of computer science and artificial intelligence called natural language processing (NLP). There are plenty of tutorials available on the web that provide algorithm examples on how to extract sentiment. For example, you can find a comprehensive text analytic tutorial on the scikit-learn repo at https://github.com/scikit-learn/scikit-learn/blob/master/doc/tutorial/text_analytics/working_with_text_data.rst.

However, for this sample application, we are not going to build our own NLP...