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.
This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more:
MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder
MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder
Course logo image courtesy of Francesco Ungaro, available here on Unsplash: https://unsplash.com/photos/C89G61oKDDA
Overview
Syllabus
- Data Mining Pipeline
- This week provides you with an introduction to the Data Mining Specialization and this course, Data Mining Pipeline. As you begin, you will get introduced to the four views of data mining and the key components in the data mining pipeline.
- Data Understanding
- This week covers data understanding by identifying key data properties and applying techniques to characterize different datasets.
- Data Preprocessing
- This week explains why data preprocessing is needed and what techniques can be used to preprocess data.
- Data Warehousing
- This week covers the key characteristics of data warehousing and the techniques to support data warehousing.
Taught by
Qin (Christine) Lv
