Data Mining Pipeline
Master the essential steps of data mining, from data understanding to warehousing. Perfect for data science professionals.
Master the essential steps of data mining, from data understanding to warehousing. Perfect for data science professionals.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Mining Foundations and Practice Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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-Lv-1733769506157.avif&w=3840&q=75)
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What you'll learn
Identify and understand key components of data mining pipeline
Apply techniques for effective data preprocessing
Master data warehousing concepts and implementations
Handle data quality issues and cleaning methods
Perform data integration and transformation
Implement OLAP and data cube operations
Skills you'll gain
This course includes:
5.3 Hours PreRecorded video
3 programming assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course explores the fundamental components of the data mining pipeline, covering essential stages from initial data understanding to advanced warehousing techniques. Students learn practical approaches to data preprocessing, including cleaning, integration, and transformation methods. The curriculum emphasizes data quality assessment, dimensionality reduction, and OLAP operations, providing hands-on experience with real-world data challenges and solutions.
Data Mining Pipeline
Module 1 · 5 Hours to complete
Data Understanding
Module 2 · 5 Hours to complete
Data Preprocessing
Module 3 · 5 Hours to complete
Data Warehousing
Module 4 · 4 Hours to complete
Fee Structure
Instructor
-Lv-1733769506157.avif&w=256&q=75)
Professor
Qin (Christine) Lv is a Professor in the Department of Computer Science at the University of Colorado Boulder, where she specializes in full-stack data analytics, integrating systems, algorithms, and applications for effective data analytics in ubiquitous computing and scientific discovery. She earned her B.E. with honors from Tsinghua University and both an M.A. and Ph.D. from Princeton University, where she was advised by Professor Kai Li. Dr. Lv's research interests include mobile and wearable computing, social networks, spatial-temporal data, anomaly detection, similarity search, and recommender systems, with interdisciplinary applications in environmental research and renewable energy. She has received several prestigious awards, including the SenSys 2018 Best Paper Runner-up Award and the 2017 Google Faculty Research Award, and has held significant roles in professional activities, such as General Co-chair for UbiComp 2021. Dr. Lv can be reached at her office in Engineering Center ECCR 1B24 or via email.
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