This course introduces the key steps involved in the data mining pipeline, including data understanding, data preprocessing, data warehousing, data modeling, interpretation and evaluation, and real-world applications. Data Mining Pipeline can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.
What You Will Learn
- By the end of this course, you will be able to identify the key components of the data mining pipeline and describe how they're related.
- You will be able to identify particular challenges presented by each component of the data mining pipeline.
- You will be able to apply techniques to address challenges in each component of the data mining pipeline.
Syllabus
WEEK 1
Data Mining Pipeline
This module provides an introduction to data mining and data mining pipeline, including the four views of data mining and the key components in the data mining pipeline.
WEEK 2
Data Understanding
This module covers data understanding by identifying key data properties and applying techniques to characterize different datasets.
WEEK 3
Data Preprocessing
This module explains why data preprocessing is needed and what techniques can be used to preprocess data.
WEEK 4
Data Warehousing
This module covers the key characteristics of data warehousing and the techniques to support data warehousing.
