UAlbany W4 Cross Industry Standard Process for Data Mining Discussion

WK 4, MIS6210

PART I

Overview

• In this assignment, you will consider the role of the Cross-Industry Standard Process for Data Mining (CRISP-DM) model used in analyzing (data mining) data to convert data into knowledge.
  

  Tasks

  According to the CRISP-DM model, data mining is a process that consists of six phases:

  1. Business Understanding
  2. Data Understanding
  3. Data Preparation
  4. Modeling
  5. Evaluation
  6. Deployment

  Give a brief description of each phase. What are its inputs and outputs? Do you believe all six phases are important to generate a valid model or do you think we can skip some of them?

• PART II

• Course Project Part 4—Data Mining: Myths and Facts

  Overview

  In this assignment, you will explore the different truths (and lies) about data mining. Understanding the limitations and opportunities data mining provides gives you a better understanding of what you can do as an analyst or what to expect from data mining as a manager.

  Tasks

  Data mining has been used in analyzing data since the 1990s. The term has been surrounded by many myths and facts. Perform a search on the Internet and write a short paper listing the myths and your thoughts on why each myth is invalid (or valid). Focus on the facts that help managers make decisions and try to address myths related to decision making.

  Submission Details:

  • Write the paper in 2 to 3 pages in a Microsoft Word document.