Principles of Data Science: Mathematical techniques and theory to succeed in data-driven industries

• [*] Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
• [*] More than just a math class, learn how to perform real-world data science tasks with R and Python
• [*] Create actionable insights and transform raw data into tangible value

• [*] Get to know the five most important steps of data science
• [*] Use your data intelligently and learn how to handle it with care
• [*] Bridge the gap between mathematics and programming
• [*] Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
• [*] Build and evaluate baseline machine learning models
• [*] Explore the most effective metrics to determine the success of your machine learning models
• [*] Create data visualizations that communicate actionable insights
• [*] Read and apply machine learning concepts to your problems and make actual predictions

You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you.