This course offers step-by-step guidance and hands-on experience of designing and implementing a real-world data mining project, including problem formulation, literature survey, proposed work, evaluation, discussion and future work.
제공자:
이 강좌에 대하여
data science professionals or domain experts, some experience working with data, completion of Data Mining Pipeline and Data Mining Methods
배울 내용
Identify the key components of and propose a real-world data mining project.
Design and develop real-world solutions across the full data mining pipeline.
Summarize and present the key findings of the data mining project.
Analyze the overall project process and identify possible improvements.
귀하가 습득할 기술
- data mining project design and development
- data mining project formulation
- data mining project summary and presentation
- data mining project process analysis and improvement
data science professionals or domain experts, some experience working with data, completion of Data Mining Pipeline and Data Mining Methods
제공자:

콜로라도 대학교 볼더 캠퍼스
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
석사 학위 취득 시작
강의 계획표 - 이 강좌에서 배울 내용
Introduction to Data Mining Project
This module provides a general introduction of data mining project from the architect's perspective, focusing on the the initial brainstorming of project ideas.
Project Proposal
This module discusses in detail what should be included in the project proposal.
Project Checkpoint
This module focuses on checking the status of the project, identifying the progress so far and any changes to the initial proposal.
Project Final Report
This module discusses in detail the final project report, highlighting the importance of summarizing the key findings and analyzing the overall project process.
Data Mining Foundations and Practice 특화 과정 정보
The Data Mining specialization is intended for data science professionals and domain experts who want to learn the fundamental concepts and core techniques for discovering patterns in large-scale data sets. This specialization consists of three courses: (1) Data Mining Pipeline, which introduces the key steps of data understanding, data preprocessing, data warehouse, data modeling and interpretation/evaluation; (2) Data Mining Methods, which covers core techniques for frequent pattern analysis, classification, clustering, and outlier detection; and (3) Data Mining Project, which offers guidance and hands-on experience of designing and implementing a real-world data mining project.

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