June 2017
Beginner to intermediate
27 pages
35m
English
Content preview from Development Workflows for Data Scientists
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Foreword
The field of data science has taken all industries by storm. Data scientist positions are consistently in the top-ranked best job listings, and new job opportunities with titles like data engineer and data analyst are opening faster than they can be filled. The explosion of data collection and subsequent backlog of big data projects in every industry has lead to the situation in which "we’re drowning in data and starved for insight.”
To anyone who lived through the growth of software engineering in the previous two decades, this is a familiar scene. The imperative to maintain a competitive edge in software by rapidly delivering higher-quality products to market, led to a revolution in software development methods and tooling; it is the manifesto for Agile software development, Agile operations, DevOps, Continuous Integration, Continuous Delivery, and so on.
Much of the analysis performed by scientists in this fast-growing field occurs as software experimentation in languages like R and Python. This raises the question: what can data science learn from software development?
Ciara Byrne takes us on a journey through the data science and analytics teams of many different companies to answer this question. She leads us through their practices and priorities, their tools and techniques, and their capabilities and concerns. It’s an illuminating journey that shows that even though the pace of change is rapid and the desire for the knowledge and insight from data is ever growing, ...
Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Read now
Unlock full access
More than 5,000 organizations count on O’Reilly
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.