About this Course

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Flexible deadlines
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Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

data science professionals or domain experts, some experience working with data

Approx. 21 hours to complete
English

What you will learn

  • B​y 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.

  • Y​ou will be able to identify particular challenges presented by each component of the data mining pipeline.

  • Y​ou will be able to apply techniques to address challenges in each component of the data mining pipeline.

Skills you will gain

  • Data Pre-Processing
  • Data Warehousing
  • data understanding
  • data mining pipeline
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Intermediate Level

data science professionals or domain experts, some experience working with data

Approx. 21 hours to complete
English

Offered by

Placeholder

University of Colorado Boulder

Start working towards your degree

This Course is part of an online degree program offered by the University of Colorado Boulder. When you enroll in a for-credit non-degree course through the university and complete it online, it counts as credit hours towards a degree at CU-Boulder. All you have to do is apply through the university.

Syllabus - What you will learn from this course

Week
1
Week 1
6 hours to complete

Data Mining Pipeline

6 hours to complete
2 videos (Total 88 min), 3 readings, 2 quizzes
Week
2
Week 2
5 hours to complete

Data Understanding

5 hours to complete
2 videos (Total 71 min)
Week
3
Week 3
5 hours to complete

Data Preprocessing

5 hours to complete
2 videos (Total 77 min)
Week
4
Week 4
5 hours to complete

Data Warehousing

5 hours to complete
2 videos (Total 54 min)

About the Data Mining Foundations and Practice Specialization

Data Mining Foundations and Practice

Frequently Asked Questions

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