Visualizations and graphs are excellent for identifying obvious patterns in the dataset. However, as trends emerge from multiple sources, more in-depth reporting is required, as well as descriptions of the techniques used for those not directly involved in the project. Instead of creating these reports by hand, application-oriented analyses make use of template languages to dynamically construct documents at the time of analysis. Jinja2 is a Python library that is used to generate documents by combining a template-usually an HTML file, but it can be any kind of text file-with a context, a data source that is used to fill in the template. This combination is ideal to report on the analyses that we're performing.
Practical Data Science Cookbook, Second Edition - Second Edition
By :
Practical Data Science Cookbook, Second Edition - Second Edition
By:
Overview of this book
As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use.
Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Preparing Your Data Science Environment
Driving Visual Analysis with Automobile Data with R
Creating Application-Oriented Analyses Using Tax Data and Python
Modeling Stock Market Data
Visually Exploring Employment Data
Driving Visual Analyses with Automobile Data
Working with Social Graphs
Recommending Movies at Scale (Python)
Harvesting and Geolocating Twitter Data (Python)
Forecasting New Zealand Overseas Visitors
Customer Reviews