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Development Workflows for Data Scientists: Enabling Fast, E fficient, and Reproducible Results for Data Science Teams
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 so ware 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, the dual disciplines of software engineering and data science are up for the task.
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 so ware 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, the dual disciplines of software engineering and data science are up for the task.
28 pages, ebook
First published March 1, 2017
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September 4, 2017
Nice, brief and to-the-point review of different cases (companies and organizations) approach to data science and the challenges it comes with in terms of productivity, reliability, reproducibility and more. It links to some of these companies Github repositories and knowledge platforms as well as describe the tooling and good practices they use to maximise the outcome of their data-related efforts.
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