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Managing Your Data Science Projects - Learn Salesmanship, Presentation, and Maintenance of Completed Models (Paperback, 1st ed.)
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Managing Your Data Science Projects - Learn Salesmanship, Presentation, and Maintenance of Completed Models (Paperback, 1st ed.)
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At first glance, the skills required to work in the data science
field appear to be self-explanatory. Do not be fooled. Impactful
data science demands an interdisciplinary knowledge of business
philosophy, project management, salesmanship, presentation, and
more. In Managing Your Data Science Projects, author Robert de
Graaf explores important concepts that are frequently overlooked in
much of the instructional literature that is available to data
scientists new to the field. If your completed models are to be
used and maintained most effectively, you must be able to present
and sell them within your organization in a compelling way. The
value of data science within an organization cannot be overstated.
Thus, it is vital that strategies and communication between teams
are dexterously managed. Three main ways that data science strategy
is used in a company is to research its customers, assess risk
analytics, and log operational measurements. These all require
different managerial instincts, backgrounds, and experiences, and
de Graaf cogently breaks down the unique reasons behind each. They
must align seamlessly to eventually be adopted as dynamic models.
Data science is a relatively new discipline, and as such, internal
processes for it are not as well-developed within an operational
business as others. With Managing Your Data Science Projects, you
will learn how to create products that solve important problems for
your customers and ensure that the initial success is sustained
throughout the product's intended life. Your users will trust you
and your models, and most importantly, you will be a more
well-rounded and effectual data scientist throughout your career.
Who This Book Is For Early-career data scientists, managers of data
scientists, and those interested in entering the field of data
science
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