Books > Computing & IT > Applications of computing > Databases > Data capture & analysis
|
Buy Now
Data Modeling Master Class Training Manual - Steve Hoberman's Best Practices Approach to Understanding & Applying Fundamentals Through Advanced Modeling Techniques (Paperback)
Loot Price: R4,328
Discovery Miles 43 280
You Save: R998
(19%)
|
|
Data Modeling Master Class Training Manual - Steve Hoberman's Best Practices Approach to Understanding & Applying Fundamentals Through Advanced Modeling Techniques (Paperback)
Expected to ship within 12 - 17 working days
|
This is the fourth edition of the training manual for the Data
Modelling Master Class that Steve Hoberman teaches onsite and
through public classes. This text can be purchased prior to
attending the Master Class, the latest course schedule and detailed
description can be found on Steve Hoberman's website,
stevehoberman.com. The Master Class is a complete course on
requirements elicitation and data modeling, containing three days
of practical techniques for producing solid relational and
dimensional data models. After learning the styles and steps in
capturing and modelling requirements, you will apply a best
practices approach to building and validating data models through
the Data Model Scorecard(r). You will know not just how to build a
data model, but also how to build a data model well. Two case
studies and many exercises reinforce the material and enable you to
apply these techniques in your current projects. By the end of the
course, you will know how to: Explain data modeling building blocks
and identify these constructs by following a question-driven
approach to ensure model precision; Demonstrate reading a data
model of any size and complexity with the same confidence as
reading a book; Validate any data model with key "settings" (scope,
abstraction, timeframe, function, and format) as well as through
the Data Model Scorecard; Apply requirements elicitation techniques
including interviewing and prototyping; Build relational and
dimensional conceptual, logical, and physical data models through
two case studies; Practice finding structural soundness issues and
standards violations; Recognize situations where abstraction would
be most valuable and situations where abstraction would be most
dangerous; Use a series of templates for capturing and validating
requirements, and for data profiling; Express how to write clear,
complete, and correct definitions; Leverage the Grain Matrix,
enterprise data model, and available industry data models for a
successful enterprise architecture.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
|
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.