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January 28, 2025
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Launch Your Career in Data Science. Master core data mining concepts, techniques, and hands-on skills.
Instructor: Qin (Christine) Lv
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(50 reviews)
Recommended experience
Intermediate level
Learners should have some experience working with data, Python programming, data structures and algorithms, and basic concepts of probability.
(50 reviews)
Recommended experience
Intermediate level
Learners should have some experience working with data, Python programming, data structures and algorithms, and basic concepts of probability.
Data mining pipeline: data understanding, preprocessing, warehousing
Data mining methods: frequent patterns, classification, clustering, outliers
Data mining project: project formulation, design, implementation, reporting
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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.
Data Mining can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
Specialization logo image courtesy of Diego Gonzaga, available here on Unsplash: https://unsplash.com/photos/QG93DR4I0NE
Applied Learning Project
There are programming assignments that cover specific aspects of the data mining pipeline and methods. Furthermore, the Data Mining Project course provides step-by-step guidance and hands-on experience of formulating, designing, implementing, and reporting of a real-world data mining project.
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.
Identify the core functionalities of data modeling in the data mining pipeline
Apply techniques that can be used to accomplish the core functionalities of data modeling and explain how they work.
Evaluate data modeling techniques, determine which is most suitable for a particular task, and identify potential improvements.
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.
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.
This Specialization 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
This Specialization 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
University of Colorado Boulder
Degree · 24 months
University of Colorado Boulder
Degree · 24 months
University of Colorado Boulder
Degree · 2 years
University of Colorado Boulder
Degree · 2 years
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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The specialization requires about five to ten hours of work a week for twelve weeks to complete.
Learners should have some experience working with data, Python programming, data structures and algorithms, and basic concepts of probability.
Yes, it's recommended that learners new to data mining take the three courses in the specialization in sequence.
No, but the Data Mining specialization is part of CU Boulder's Master's of Science in Data Science program. Learners enrolled in this program will earn credit toward the degree by completing this specialization.
Upon completing the specialization, you will be able to work with data at all stages of the data-mining pipeline, employ different data-mining methods, and design and implement a data-mining project.
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.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aid.
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