Description:Finding a good data scientist has been likened to hunting for a unicorn. The required combination of software engineering skills, mathematical fluency, and business savvy are simply very hard to find in one person. On top of that, good data science is not just rote application of trainable skillsets, but rather requires the ability to think critically in all these areas. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The author describes the classic machine learning algorithms, including the mathematics needed to understand what's really going on. Classical statistics is taught so that readers learn to think critically about the interpretation of data and its common pitfalls. In addition, basic software engineering and computer science skillsets often lacking in data scientists are given a central place in the book. Visualization tools are reviewed, and their central importance in data science is highlighted. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter. All of these are topics explained in the context of solving real-world modern data problems. Chapter coverage includes: Introduction: Becoming a Unicorn; Data Science Programming Languages; Visualizations; Software Engineering Concepts; Data Formats; Mathematical Foundations; Classical Statistics; Machine Learning; Computer Science Concepts; Software Packages; Big Data Tools; Common Domains of Application; and Communicating Results.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with The Data Science Handbook. To get started finding The Data Science Handbook, you are right to find our website which has a comprehensive collection of manuals listed. Our library is the biggest of these that have literally hundreds of thousands of different products represented.
Description: Finding a good data scientist has been likened to hunting for a unicorn. The required combination of software engineering skills, mathematical fluency, and business savvy are simply very hard to find in one person. On top of that, good data science is not just rote application of trainable skillsets, but rather requires the ability to think critically in all these areas. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The author describes the classic machine learning algorithms, including the mathematics needed to understand what's really going on. Classical statistics is taught so that readers learn to think critically about the interpretation of data and its common pitfalls. In addition, basic software engineering and computer science skillsets often lacking in data scientists are given a central place in the book. Visualization tools are reviewed, and their central importance in data science is highlighted. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter. All of these are topics explained in the context of solving real-world modern data problems. Chapter coverage includes: Introduction: Becoming a Unicorn; Data Science Programming Languages; Visualizations; Software Engineering Concepts; Data Formats; Mathematical Foundations; Classical Statistics; Machine Learning; Computer Science Concepts; Software Packages; Big Data Tools; Common Domains of Application; and Communicating Results.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with The Data Science Handbook. To get started finding The Data Science Handbook, you are right to find our website which has a comprehensive collection of manuals listed. Our library is the biggest of these that have literally hundreds of thousands of different products represented.