TIM-6501 V3

IBM-SPSS (2000). The CRISP-DM 1.0 Step-by-Step Data Mining Guide. Retrieved from https://www.the-modeling-agency.com/crisp-dm.pdf
This guide is an industry-standard for managing data science projects. Pages 10-27 are directly relevant to each assignment completed in this class.

Larose, C.D., & Larose, D. T. (2015). Data mining and predictive analytics (2nd ed.). Wiley Press.
Reminder: Please complete the O’Reilly login steps (See Accessing O’Reilly link above) where you get an email that will allow your browser to keep your password, solving future access limitations.

• Read Chapter 1: An Introduction to Data Mining and Predictive Analytics.
  Chapter 1 provides a detailed exploration into data mining, specific tasks accomplished in the process, the importance of estimation and prediction, and the application of the Cross-industry Standard Process for Data Mining: CRISM-DM framework, the data science lifecycle framework used in this course.
• Read Chapter 2: Data Preprocessing.
  Chapter 2 details the importance of data preprocessing to create a data set that is capable of analysis. Examples of data cleaning tasks are described and visualizations provided. Describe the importance of the resource.