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Lesson 1

Introduction to Database Requirements Analysis

Earlier in this course, we took a brief look at the stages of the database lifecycle (DBLC). This module examines the critically important first stage in the DBLC: Requirements Analysis. This is the stage in the design cycle when you find out everything you can about the data the client needs to store in the database and the conditions under which that data needs to be accessed.
Keep in mind, too, that a single pass through this stage rarely yields all the information the database designer needs. Be prepared to return to the tasks associated with Requirements Analysis several times during the course of designing a database.

Learning objectives for Module 4

After completing this module, you will be able to:
  1. Explain the purpose of Requirements Analysis
  2. Identify business objects and describe their characteristics
  3. Explain the importance of business rules
  4. Explain the purpose of interviewing users of data
  5. Explain the purpose of the data flow diagram
  6. List reasons for creating user views
  7. Describe the documents produced during Requirements Analysis
The next lesson explains the overall purpose of Requirements Analysis.

Collecting Data

Collecting data is relatively easy, but turning raw information into something useful requires that you know how to extract precisely what you need. In this module, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You will learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications. Along the way, you will experiment with concepts through hands-on exercises at various points in the moule.
  1. Use graphics to describe data with one, two, or dozens of variables
  2. Develop conceptual models using back-of-the-envelope calculations, as well as scaling and probability arguments
  3. Mine data with computationally intensive methods such as simulation and clustering
  4. Make your conclusions understandable through reports, dashboards, and other metrics programs
  5. Understand financial calculations, including the time value of money
  6. Use dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situations
  7. Become familiar with different open source programming environments for data analysis