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

Getting started with Apache Spark


The term big data can rightly feel vague and imprecise. What is the cut-off for considering any dataset big data? Is it 10 GB, 100 GB, 1 TB or more? One definition that I like is: big data is when the data cannot fit into the memory available in a single machine. For years, data scientists have been forced to sample large datasets, so they could fit into a single machine, but that started to change as parallel computing frameworks that are able to distribute the data into a cluster of machines made it possible to work with the dataset in its entirety, provided of course that the cluster had enough machines. At the same time, advances in cloud technologies made it possible to provision on demand a cluster of machines that are adapted to the size of the dataset.

Today, there are multiple frameworks (most of the time available as open source) that can provide robust, flexible parallel computing capabilities. Some of the most popular include Apache Hadoop (http...