Название: Data Science: The Executive Summary - A Technical Book for Non-Technical People
Автор: Field Cady
Издательство: Wiley
Год: 2021
Страниц: 190
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Tap into the power of data science with this comprehensive resource for non-technical professionals.

Data Science: The Executive Summary – A Technical Book for Non-Technical Professionals is a comprehensive resource for people in non-engineer roles who want to fully understand data science and analytics concepts. Accomplished data scientist and author Field Cady describes both the “business side” of data science, including what problems it solves and how it fits into an organization, and the technical side, including analytical techniques and key technologies.

If data scientists are known for anything, it is their ability to deploy Machine Learning models to solve problems. The chapter 5 will cover what Machine Learning (ML) is, what you can do with it, and the key things to understand in order to get the most out of it.
Machine Learning basically means any method of using the computer to find patterns in data – the term “pattern recognition” is often used synonymously. Machine learning falls into two broad categories called “supervised learning” and “unsupervised learning.” Both of them are based on finding patterns in historical data.

You do not need to be a computer programmer in order to work effectively with data scientists, understand the problems they are solving, and leverage their work. However first‐hand experience with software engineering doesn't hurt, and there are times when it is extremely helpful. I would like to discuss how you can dip your toe into coding if you so choose. There are two approaches you can take. One is to learn to write “real code” – developing scripts and software tools in production languages like Python, jаvascript, and C++.

The most popular dedicated technical computing language among data scientists is an open‐source option called R. The data science community is largely split between the people who primarily use R and those who prefer Python. Python has a small lead that is steadily growing; what I always tell people is to use the one they already know, but if they don't know either then Python is the one to learn. An important thing to note is that different industries tend to be quite partial to different tools, even when the work itself is similar. While self‐styled “data scientists” tend to use Python, R is more common in the statistics community.

Data Science: The Executive Summary covers topics like:

Assessing whether your organization needs data scientists, and what to look for when hiring them
When Big Data is the best approach to use for a project, and when it actually ties analysts’ hands
Cutting edge Artificial Intelligence, as well as classical approaches that work better for many problems
How many techniques rely on dubious mathematical idealizations, and when you can work around them

Perfect for executives who make critical decisions based on data science and analytics, as well as mangers who hire and assess the work of data scientists, Data Science: The Executive Summary also belongs on the bookshelves of salespeople and marketers who need to explain what a data analytics product does. Finally, data scientists themselves will improve their technical work with insights into the goals and constraints of the business situation.