One of the most interesting things that you can do with stock market data is come up with a valuation model. The ultimate goal is to arrive at a decision about whether the stock might be overvalued or undervalued. There are two main ways to do this. Intrinsic valuation is generally more time consuming because it involves digging into the financial statements of a company to arrive at a valuation decision. The alternative method is relative valuation which will quickly provide a sense of how the stock is valued, but does not take into account a comprehensive set of factors. The basic idea is that it compares a stock's price and valuation ratios to similar stocks to arrive at a conclusion. In this section, we will value stocks using the simpler relative valuation method.
Practical Data Science Cookbook, Second Edition - Second Edition
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Practical Data Science Cookbook, Second Edition - Second Edition
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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.
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Current Title:
Practical Data Science Cookbook, Second Edition - Second Edition
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