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Data Mining Pipeline

Qin (Christine) Lv

3.7

Based on 78 ratings

, see reviews

Qin (Christine) Lv

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.

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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.

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

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What's inside

Syllabus

Data Mining Pipeline

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.

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Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores multiple facets and perspectives on relevant topics in data mining, including the methodology, interpretability, and real-world impact of data mining

Provides hands-on labs, interactive materials, demonstrations, and exercises to reinforce learning

Provides an accessible entry point for beginners interested in learning about data mining, as it covers the fundamentals and key concepts of the field

Ensures understanding of each step in the data mining pipeline through well-structured and organized content

Provides opportunities for learners to apply their knowledge through hands-on exercises and projects, fostering practical skills development

Equips learners with the ability to identify and address real-world data mining problems and challenges

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Reviews summary

Data mining pipeline fundamentals and practice

Students say this course offers an excellent foundational understanding of the data mining pipeline, praised for its clear explanations and ability to demystify complex concepts. It provides a comprehensive overview, making it ideal for beginners and professionals seeking a broad introduction. While some earlier feedback noted a primarily theoretical approach, recent reviews highlight significant improvements with the addition of more practical applications and hands-on examples. Its well-structured lectures and engaging quizzes contribute to effective learning, though more experienced learners might find some content superficial, suggesting a need for supplementary advanced study.

Explanations are clear and easy to understand.

"The instructor's explanations were incredibly clear, making complex topics digestible."

"The explanations were clear and jargon-free, making it accessible even for someone without a strong technical background."

"The instructor is clearly knowledgeable and passionate about the subject. Their explanations were always clear and engaging."

Provides a clear, comprehensive introduction to the data mining pipeline.

"This course gave me a fantastic overview of the entire data mining pipeline."

"A truly excellent introductory course! It covers all the fundamental steps from data understanding to warehousing."

"It provided a great conceptual framework... felt comprehensive for its scope. Perfect for someone like me starting in the field."

Emphasizes real-world application and hands-on examples.

"The hands-on examples were very helpful for understanding practical applications."

"I appreciated the real-world applications discussed in this course. It wasn't just theory; they showed how these concepts are used in industry."

"The course material felt up-to-date and directly applicable. I'm now much more confident in understanding the full lifecycle..."

Shows significant improvement over time based on feedback.

"While I initially felt it lacked sufficient hands-on exercises and up-to-date examples, recent additions have greatly improved this aspect."

"The material now feels much more practical and directly applicable, a welcome change from how it seemed in earlier versions."

"The instructor seems to have revised content over time, addressing prior comments about depth and real-world relevance, making it stronger."

May lack depth for those with prior data science experience.

"However, if you already have some experience, you might find the content a bit too superficial."

"I was hoping for more in-depth coverage of specific techniques or advanced tools."

"It's a good conceptual groundwork, but not much hands-on beyond basic examples."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Mining Pipeline with these activities:

Practice data mining techniques on Kaggle

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Apply and reinforce data mining concepts and techniques.

Browse courses on Data Mining

Show steps

Create an account on Kaggle
Join the corresponding Data Mining competition
Explore the competition's dataset, research the problem domain
Develop data mining solution using techniques learned in the module
Submit your solution, analyze results

Follow along with open-source data mining tutorials

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Supplement course materials with practical examples of data mining in real-world settings.

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Search for data mining tutorials on hosting websites like YouTube
Select a tutorial relevant to the module's topic and watch the video
Follow along with the tutorial, applying techniques and concepts to the provided dataset
Review the tutorial and identify any areas for further research

Compile a collection of data mining resources

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Build a comprehensive reference list of tools and techniques used in data mining.

Browse courses on Data Analytics Tools

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Search for data mining resources online, such as articles, books, and videos
Review and evaluate the resources, selecting the most relevant and informative ones
Organize the resources into a structured digital or physical format
Categorize and label the resources based on their content and utility
Share the compilation with other students or online communities as a supplementary resource

Two other activities

Expand to see all activities and additional details

Show all five activities

Contribute to an open-source data mining project

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Gain practical experience and contribute to the data mining community by working on real-world projects.

Browse courses on Big Data Analytics

Show steps

Identify open-source data mining projects on platforms like GitHub or Apache
Review the project's description and select a task or issue to work on
Join the project's online community, ask questions, and discuss your contributions
Fork the project, make your changes, and submit a pull request for review
Collaborate with other developers to refine and improve your contributions

Develop a data mining project based on a real-world dataset

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Apply data mining techniques to solve a real-world problem, developing a data mining pipeline from scratch.

Browse courses on Data Analysis

Show steps

Identify a real-world problem or dataset that can benefit from data mining techniques
Define the project scope, objectives, and expected outcomes
Collect and prepare the necessary data, ensuring data quality and relevance
Apply data mining techniques to extract insights and patterns from the data
Interpret and analyze the results, developing actionable recommendations
Present the project findings and communicate the insights effectively

Career center

Learners who complete Data Mining Pipeline will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist uses advanced data analysis techniques to extract meaningful insights from data, solving complex business problems. This course can help aspiring Data Scientists develop a deep understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course provides hands-on experience with real-world datasets, enabling Data Scientists to gain the skills to extract insights from complex data and make informed decisions.

See salaries and explore the career path for Data Scientist

Data Analyst

A Data Analyst prepares, processes, and analyzes data to identify trends and patterns, supporting decision-making within organizations. This course helps aspiring Data Analysts build a solid foundation in the data mining pipeline. It covers essential topics such as data understanding, data preprocessing, data warehousing, and data modeling, providing Data Analysts with the knowledge and skills to manage and analyze data effectively.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines and systems to ensure the availability, reliability, and performance of data. This course introduces aspiring Data Engineers to the key steps involved in the data mining pipeline. It provides hands-on experience with data understanding, data preprocessing, data warehousing, and data modeling, equipping Data Engineers with the skills to build and maintain data infrastructure that supports data-driven decision-making.

See salaries and explore the career path for Data Engineer

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models to solve complex problems, leveraging data to automate tasks and improve decision-making. This course helps aspiring Machine Learning Engineers understand the data mining pipeline, providing a foundation for data preparation, feature engineering, and model building. The course's coverage of data warehousing and data modeling is particularly relevant, as Machine Learning Engineers need to understand how data is stored and managed to effectively build and deploy machine learning solutions.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

A Business Analyst uses data to understand business needs and develop solutions to optimize processes and improve performance. This course provides aspiring Business Analysts with an understanding of the data mining pipeline and its role in data-driven decision-making. The course covers techniques for data understanding, data preprocessing, data warehousing, and data modeling, equipping Business Analysts with the skills to analyze data and make recommendations that support business objectives.

See salaries and explore the career path for Business Analyst

Data Warehouse Analyst

A Data Warehouse Analyst designs, builds, and manages data warehouses, ensuring that data is organized, accessible, and reliable for analysis and reporting. This course provides aspiring Data Warehouse Analysts with a deep understanding of the data mining pipeline, focusing on data warehousing techniques and technologies. The course covers data understanding, data preprocessing, and data modeling, providing Data Warehouse Analysts with the skills to build and maintain data warehouses that support data-driven decision-making.

See salaries and explore the career path for Data Warehouse Analyst

Data Mining Analyst

A Data Mining Analyst uses data mining techniques to extract meaningful insights from data, supporting decision-making and problem-solving within organizations. This course is specifically tailored to the needs of aspiring Data Mining Analysts, providing a comprehensive overview of the data mining pipeline. The course covers data understanding, data preprocessing, data warehousing, data modeling, and advanced data mining techniques, equipping Data Mining Analysts with the skills to uncover hidden insights and patterns in data.

See salaries and explore the career path for Data Mining Analyst

Data Architect

A Data Architect designs and implements data management solutions, ensuring that data is accessible, reliable, and secure. This course may be helpful for aspiring Data Architects by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Data Architects build a foundation for designing and implementing data management solutions that support data-driven decision-making.

See salaries and explore the career path for Data Architect

Database Administrator

A Database Administrator manages and maintains databases, ensuring that they are available, reliable, and secure. This course may be helpful for aspiring Database Administrators by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Database Administrators build a foundation for managing and maintaining databases that support data-driven decision-making.

See salaries and explore the career path for Database Administrator

Statistician

A Statistician uses statistical methods to analyze data and draw conclusions, supporting decision-making and problem-solving. This course may be helpful for aspiring Statisticians by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Statisticians build a foundation for applying statistical methods to data analysis and interpretation.

See salaries and explore the career path for Statistician

Software Engineer

A Software Engineer designs, develops, and maintains software applications and systems. This course may be helpful for aspiring Software Engineers by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Software Engineers build a foundation for developing software solutions that leverage data and support data-driven decision-making.

See salaries and explore the career path for Software Engineer

Information Architect

An Information Architect designs and manages information systems, ensuring that they are organized, accessible, and usable. This course may be helpful for aspiring Information Architects by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Information Architects build a foundation for designing and managing information systems that support data-driven decision-making.

See salaries and explore the career path for Information Architect

Data Quality Analyst

A Data Quality Analyst ensures that data is accurate, consistent, and complete, supporting data-driven decision-making and problem-solving. This course may be helpful for aspiring Data Quality Analysts by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Data Quality Analysts build a foundation for assessing and improving data quality.

See salaries and explore the career path for Data Quality Analyst

Data Governance Specialist

A Data Governance Specialist develops and implements data governance policies and procedures, ensuring that data is used ethically and responsibly. This course may be helpful for aspiring Data Governance Specialists by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Data Governance Specialists build a foundation for developing and implementing data governance frameworks that support data-driven decision-making.

See salaries and explore the career path for Data Governance Specialist

Data Privacy Analyst

A Data Privacy Analyst ensures that data is collected, used, and disclosed in compliance with privacy laws and regulations. This course may be helpful for aspiring Data Privacy Analysts by providing an understanding of the data mining pipeline, including data understanding, data preprocessing, data warehousing, and data modeling. The course helps Data Privacy Analysts build a foundation for developing and implementing data privacy policies and procedures.

See salaries and explore the career path for Data Privacy Analyst

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Data Mining Pipeline.

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Provides a comprehensive overview of data mining concepts and techniques. It covers all the key topics that are relevant to this course, including data preprocessing, data warehousing, data mining models, and data mining applications. It valuable resource for both students and practitioners who want to learn more about data mining.

Data Mining: Concepts and Techniques (The Morgan...

Data Mining: Concepts and Techniques (The Morgan...

Data Mining: Concepts and Techniques, Second...

Data Mining: Know It All

Cover image

Cover image

ADVANCES IN EMERGING COMPUTING TECHNOLOGIES

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Provides a comprehensive overview of statistical learning methods. It valuable resource for students and practitioners who want to learn more about how statistical learning methods can be used to solve real-world problems.

ADVANCES IN EMERGING COMPUTING TECHNOLOGIES

ADVANCES IN EMERGING COMPUTING TECHNOLOGIES

Cover image

Cover image

Save

Deep Learning is the current standard text on deep learning.

Deep Learning (Adaptive Computation and Machine...

Deep Learning (Adaptive Computation and Machine...

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Cover image

Hands-On Machine Learning with Scikit-Learn, Keras,...

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow provides a practical introduction to machine learning using Python.

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Hands-On Machine Learning with Scikit-Learn, Keras,...

Cover image

Cover image

Reinforcement Learning, second edition

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Reinforcement Learning: An Introduction provides a comprehensive overview of reinforcement learning.

Reinforcement Learning, second edition: An...

Reinforcement Learning, second edition: An...

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Cover image

Data Mining with R

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Data Mining with R comprehensive guide to data mining using R.

Data Mining with R: Learning with Case Studies,...

Data Mining with R: Learning with Case Studies...

Data Mining with R: Learning with Case Studies,...

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Cover image

Data Science for Business

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Data Science for Business provides a practical introduction to data science for business applications.

Data Science for Business: What You Need to Know...

Data Science for Business: What You Need to Know...

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Cover image

Handbook of Systems Engineering and Risk Management...

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Provides a collection of case studies that demonstrate how data mining techniques have been used to solve real-world problems. It valuable resource for students and practitioners who want to learn how data mining can be used to improve business outcomes.

Handbook of Systems Engineering and Risk Management...

Handbook of Systems Engineering and Risk Management...

Cover image

Cover image

The Master Algorithm

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The Master Algorithm provides a grand overview of the history and future of machine learning.

The Master Algorithm: How the Quest for the...

The Master Algorithm: How the Quest for the...

(Türkçe) Master algoritma : yapay ögrenme hayatimizi nasil...

Master algorithm (Korean Edition)

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Cover image

Weapons of Math Destruction

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Weapons of Math Destruction provides a critical look at the role of data science in society.

Weapons of Math Destruction

Weapons of Math Destruction

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Rating

3.7

Effort

Approx. 21 hours

Level

Intermediate

Via

Coursera

Institution

University of Colorado Boulder

Instructor

Qin (Christine) Lv

Language

English

Transcripts

Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова Bahasa Indonesia ไทย қазақша

This course belongs to a collection called Data Mining Foundations and Practice. It's comprised of two courses:

Data Mining Pipeline (viewing)
Data Mining Methods

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores multiple facets and perspectives on relevant topics in data mining, including the methodology, interpretability, and real-world impact of data mining

Provides hands-on labs, interactive materials, demonstrations, and exercises to reinforce learning

Provides an accessible entry point for beginners interested in learning about data mining, as it covers the fundamentals and key concepts of the field

Ensures understanding of each step in the data mining pipeline through well-structured and organized content

Provides opportunities for learners to apply their knowledge through hands-on exercises and projects, fostering practical skills development

Equips learners with the ability to identify and address real-world data mining problems and challenges

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