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Data Mining Project

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Data Mining Project

This course is part of Data Mining Foundations and Practice Specialization

Instructor: Qin (Christine) Lv

4,443 already enrolled

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

30 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

Build toward a degree

Learn more

What you'll learn

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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

1 assignment

Taught in English

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

Data Mining Project 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.

This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Mariana Proença, available here on Unsplash: https://unsplash.com/photos/_WgnXndHmQ4

This week provides you with a general introduction of the Data Mining Project course from the architect's perspective, focusing on the the initial brainstorming of project ideas which will prepare you for the rest of the course.

What's included

11 videos5 readings2 discussion prompts

11 videos Total 127 minutes

Meet Your Instructor! 5 minutes
Preparing for this Specialization 9 minutes
Introduction to Data Mining Project 13 minutes
Data Mining Project: Four Views 12 minutes
Project Scoping and Brainstorming 9 minutes
Overview of Data Mining Projects 18 minutes
Mobile/Wearable/IoT Sensing Projects 11 minutes
Data Fusion & Anomaly Detection Projects 16 minutes
Earthquake & NBA OSN Analysis Projects 13 minutes
Presentation: Purpose and Planning 9 minutes
Presentation: Slides and Presenting 12 minutes

5 readings Total 45 minutes

Earn Academic Credit for Your Work! 10 minutes
Course Support 10 minutes
About This Course 10 minutes
Assessment Expectations 5 minutes
AI Citation and Acknowledgement 10 minutes

2 discussion prompts Total 190 minutes

Introduce Yourself 10 minutes
Project Ideas 180 minutes

This week discusses in detail what should be included in your project proposal and ends with an opportunity to craft your own.

What's included

7 videos1 assignment1 peer review

7 videos Total 93 minutes

Project Proposal 1 12 minutes
Project Proposal 2 14 minutes
Project Proposal Submissions 11 minutes
Project Proposal Review 1 13 minutes
Project Proposal Review 2 11 minutes
Example: Solar Farm Project 12 minutes
Example: OSN Hazards Project 20 minutes

1 assignment Total 5 minutes

AI Policy Quiz 5 minutes

1 peer review Total 180 minutes

Your Project Proposal 180 minutes

This week focuses in on checking the status of your project. After reviewing your project, you will take some time to incorporate the progress you've made with updates to your initial proposal.

What's included

3 videos1 peer review

This week discusses in detail the final project report, highlighting the importance of summarizing the key findings and analyzing the overall project process.

What's included

4 videos1 peer review

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

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

Instructor

Qin (Christine) Lv

University of Colorado Boulder

3 Courses 16,854 learners

Offered by

University of Colorado Boulder

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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.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.