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.
Data Mining Pipeline
Data Mining Pipeline
This course is part of Data Mining Foundations and Practice Specialization
Instructor: Qin (Christine) Lv
8,442 already enrolled
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Gain insight into a topic and learn the fundamentals.
3.7
(74 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 21 hours
Learn at your own pace
Build toward a degree
What you'll learn
Identify the key components of the data mining pipeline and describe how they're related.
Identify particular challenges presented by each component of the data mining pipeline.
Apply techniques to address challenges in each component of the data mining pipeline.
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Taught in English
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This course is part of the Data Mining Foundations and Practice Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
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.
What's included
8 videos3 readings2 peer reviews1 discussion prompt
This week covers data understanding by identifying key data properties and applying techniques to characterize different datasets.
What's included
6 videos1 programming assignment
This week explains why data preprocessing is needed and what techniques can be used to preprocess data.
What's included
6 videos1 programming assignment
This week covers the key characteristics of data warehousing and the techniques to support data warehousing.
What's included
4 videos1 programming assignment
Instructor
Instructor ratings
2.5 (20 ratings)Offered by
Recommended if you're interested in Data Analysis
Knowledge Accelerators
University of Colorado Boulder
Google Cloud
Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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Frequently asked questions
A cross-listed course is offered under two or more CU Boulder degree programs on Coursera. For example, Dynamic Programming, Greedy Algorithms is offered as both CSCA 5414 for the MS-CS and DTSA 5503 for the MS-DS.
· You may not earn credit for more than one version of a cross-listed course.
· You can identify cross-listed courses by checking your program’s student handbook.
· Your transcript will be affected. Cross-listed courses are considered equivalent when evaluating graduation requirements. However, we encourage you to take your program's versions of cross-listed courses (when available) to ensure your CU transcript reflects the substantial amount of coursework you are completing directly in your home department. Any courses you complete from another program will appear on your CU transcript with that program’s course prefix (e.g., DTSA vs. CSCA).
· Programs may have different minimum grade requirements for admission and graduation. For example, the MS-DS requires a C or better on all courses for graduation (and a 3.0 pathway GPA for admission), whereas the MS-CS requires a B or better on all breadth courses and a C or better on all elective courses for graduation (and a B or better on each pathway course for admission). All programs require students to maintain a 3.0 cumulative GPA for admission and graduation.
Yes. Cross-listed courses are considered equivalent when evaluating graduation requirements. You can identify cross-listed courses by checking your program’s student handbook.
You may upgrade and pay tuition during any open enrollment period to earn graduate-level CU Boulder credit for << this course/ courses in this specialization>>. Because << this course is / these courses are >> cross listed in both the MS in Computer Science and the MS in Data Science programs, you will need to determine which program you would like to earn the credit from before you upgrade.
MS in Data Science (MS-DS) Credit: To upgrade to the for-credit data science (DTSA) version of << this course / these courses >>, use the MS-DS enrollment form. See How It Works.
MS in Computer Science (MS-CS) Credit: To upgrade to the for-credit computer science (CSCA) version of << this course / these courses >>, use the MS-CS enrollment form. See How It Works.
If you are unsure of which program is the best fit for you, review the MS-CS and MS-DS program websites, and then contact datascience@colorado.edu or mscscoursera-info@colorado.edu if you still have questions.