Textbook in PDF format

The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists.
With Confident Data Science, learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discipline, from primers on the key analytic and visualization tools to tips for pitching to and working with clients.
Adam Ross Nelson draws upon his expertise as a data science consultant and, as someone who made moved into the industry late in his career, to provide an overview of Data Science, including its key concepts, its history and the knowledge required to become a successful data scientist. Whether you are considering a career in this industry or simply looking to expand your knowledge, Confident Data Scienceis the essential guide to the world of Data Science.
In the first biggest family of Data Science techniques there is supervised machine learning. Supervised Machine Learning involves generating an algorithm that can make its predictions based on patterns the algorithm learned (figuratively speaking) from training data. The training data are often historic data. For example, when training spam detection algorithms, data scientists used emails from users who had previously marked those emails as either spam or not spam. The algorithms are capable of recognizing patterns characteristic of spammy emails, and conversely the patterns characteristic of not-spammy emails. Based on those patterns, the algorithms review new emails and then generate a prediction that indicates whether newly arrived emails might be spam.
The second biggest family of Data Science techniques is called unsupervised machine learning. Where supervised machine learning has a specific input and a specific output, unsupervised machine learning is much more open ended. Unsupervised machine learning is usually a set of tools data scientists turn to when they are looking to better understand large amounts of highly complex data