What this book covers
Chapter 1, How to Sound Like a Data Scientist, introduces the basic terminology used by data scientists and looks at the types of problem we will be solving throughout this book.
Chapter 2, Types of Data, looks at the different levels and types of data out there and shows how to manipulate each type. This chapter will begin to deal with the mathematics needed for data science.
Chapter 3, The Five Steps of Data Science, uncovers the five basic steps of performing data science, including data manipulation and cleaning, and shows examples of each step in detail.
Chapter 4, Basic Mathematics, explains the basic mathematical principles that guide the actions of data scientists by presenting and solving examples in calculus, linear algebra, and more.
Chapter 5, Impossible or Improbable – a Gentle Introduction to Probability, is a beginner's guide to probability theory and how it is used to gain an understanding of our random universe.
Chapter 6, Advanced Probability, uses principles from the previous chapter and introduces and applies theorems, such as Bayes' Theorem, in the hope of uncovering the hidden meaning in our world.
Chapter 7, Basic Statistics, deals with the types of problem that statistical inference attempts to explain, using the basics of experimentation, normalization, and random sampling.
Chapter 8, Advanced Statistics, uses hypothesis testing and confidence intervals to gain insight from our experiments. Being able to pick which test is appropriate and how to interpret p-values and other results is very important as well.
Chapter 9, Communicating Data, explains how correlation and causation affect our interpretation of data. We will also be using visualizations in order to share our results with the world.
Chapter 10, How to Tell Whether Your Toaster Is Learning – Machine Learning Essentials, focuses on the definition of machine learning and looks at real-life examples of how and when machine learning is applied. A basic understanding of the relevance of model evaluation is introduced.
Chapter 11, Predictions Don't Grow on Trees, or Do They?, looks at more complicated machine learning models, such as decision trees and Bayesian predictions, in order to solve more complex data-related tasks.
Chapter 12, Beyond the Essentials, introduces some of the mysterious forces guiding data science, including bias and variance. Neural networks are introduced as a modern deep learning technique.
Chapter 13, Case Studies, uses an array of case studies in order to solidify the ideas of data science. We will be following the entire data science workflow from start to finish multiple times for different examples, including stock price prediction and handwriting detection.
Chapter 14, Microsoft Databricks Case Studies, will harness the power of the Microsoft data environment as well as Apache Spark to put our machine learning in high gear. This chapter makes use of parallelization and advanced visualization software to get the most out of our data.
Chapter 15, Building Machine Learning Models with Azure Databricks and Azure ML, looks at the different technologies that a data scientist can use on Microsoft Azure Platform, which help in managing big data projects without having to worry about infrastructure and computing power.
