Data Mining Project
Master end-to-end data mining project development with hands-on experience in problem formulation, implementation, and evaluation.
Master end-to-end data mining project development with hands-on experience in problem formulation, implementation, and evaluation.
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.
Instructors:
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English
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What you'll learn
Design and propose comprehensive data mining projects
Implement solutions across the full data mining pipeline
Evaluate and analyze project results effectively
Present findings through professional technical reports
Identify potential improvements in project processes
Skills you'll gain
This course includes:
5.3 Hours PreRecorded video
3 peer reviews
Access on Mobile, Tablet, Desktop
FullTime access
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Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This comprehensive course guides students through the complete lifecycle of a real-world data mining project. Starting with project ideation and proposal development, students learn to scope, plan, and execute data mining initiatives. The curriculum covers key aspects including problem formulation, literature survey, methodology development, and evaluation strategies. Through hands-on experience, students develop practical skills in project management, technical implementation, and results presentation, preparing them for real-world data mining challenges.
Data Mining Project
Module 1 · 5 Hours to complete
Project Proposal
Module 2 · 4 Hours to complete
Project Checkpoint
Module 3 · 3 Hours to complete
Final Project Report
Module 4 · 4 Hours to complete
Fee Structure
Instructor
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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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Frequently asked questions
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