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


In this chapter, we touched upon the topic of time series analysis and forecasting. Of course, we've only scratched the surface, and there is certainly much more to explore. It is also a very important field for the industry, especially in the finance world, with very active research. For example, we see more and more data scientists trying to build time series forecasting models based on recurrent neural network (https://en.wikipedia.org/wiki/Recurrent_neural_network) algorithms, with great success. We've also demonstrated how Jupyter Notebooks combined with PixieDust and the ecosystem of libraries, such as pandas, numpy, and statsmodels, help accelerate the development of analytics as well as its operationalization into applications that are consumable by the line of business user.

In the next chapter, we will look at another important data science use case: graphs. We'll build a sample application related to flight travel and discuss how and when we should apply graph algorithms...