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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.
Read more
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.
Read more
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
Course logo image courtesy of Francesco Ungaro, available here on Unsplash: https://unsplash.com/photos/C89G61oKDDA
Register for this course and see more details by visiting:
OpenCourser.com/course/5kgzpb/data
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.
Read more
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.
Read more
Data Understanding
This week covers data understanding by identifying key data properties and applying techniques to characterize different datasets.
Data Preprocessing
This week explains why data preprocessing is needed and what techniques can be used to preprocess data.
Data Warehousing
This week covers the key characteristics of data warehousing and the techniques to support data warehousing.
Good to know
Good to know
Know what's good ,
what to watch for , 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
Unengaging course with poor instruction
Learners say this course is difficult to complete due to unclear instructions, an unhelpful grading system, and a lack of feedback from the instructor. The programming assignments, which hold a significant proportion of the grade, are described as unclear and difficult to understand. There is a lack of support for students, with no forums or discussion boards to facilitate questions and no timely responses to student concerns. The course lectures are considered poorly structured and disjointed, and some learners believe they fail to provide adequate coverage of the material. Overall, the course has been met with largely negative feedback due to its poor quality, limited support, and unhelpful assignments.
Assignments criticized for being difficult to understand.
"The instructions for the assignments were unclear and often left me confused about what was expected of me."
"These are just two small examples of many - your method for creating lists, how you treat variables, etc. can all cause errors that will result in losing points even if your code does exactly what's expected."
Course material is considered incomplete and insufficient.
"the coverage of material in the course also seems rather superficial."
Grading system is described as frequently malfunctioning.
"The grader system was broken and often did not function correctly."
"These automated grading rubrics are ABSOLUTELY HORRIBLE they are nowhere near robust enough for what they're designed, especially when you consider the terrible instructions provided for the assignment."
Course offers no forums or discussion boards for student support.
"There were no forums or discussion boards where students could ask questions or seek guidance from the instructor or peers."
"If you have to take this course as part of the MS-DS offered by CU I might try to wait and see if someone new teaches it in the future."
Lectures are seen as poorly structured and lacking depth.
"the lectures need to be more in depth and assignments are not explained well the course need another instructor because she is not well spoken and not fluent enough to teach in english"
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
Show steps
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
Show steps
Supplement course materials with practical examples of data mining in real-world settings.
Show steps
-
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
Show steps
Build a comprehensive reference list of tools and techniques used in data mining.
Browse courses on
Data Analytics Tools
Show steps
-
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
Show steps
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
Show steps
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
Practice data mining techniques on Kaggle
Show steps
Apply and reinforce data mining concepts and techniques.
Browse courses on
Data Mining
Show steps
Show steps
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
Show steps
Supplement course materials with practical examples of data mining in real-world settings.
Show steps
Show steps
Show steps
- 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
Show steps
Build a comprehensive reference list of tools and techniques used in data mining.
Browse courses on
Data Analytics Tools
Show steps
Show steps
Show steps
- 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
Show steps
Gain practical experience and contribute to the data mining community by working on real-world projects.
Browse courses on
Big Data Analytics
Show steps
Show steps
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
Show steps
Apply data mining techniques to solve a real-world problem, developing a data mining pipeline from scratch.
Browse courses on
Data Analysis
Show steps
Show steps
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
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.
Data Analyst
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.
Data Engineer
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.
Machine Learning 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.
Business Analyst
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.
Data Warehouse 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.
Data Mining 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.
Data Architect
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.
Database Administrator
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.
Statistician
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.
Software Engineer
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.
Information Architect
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.
Data Quality Analyst
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.
Data Governance Specialist
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.
Data Privacy Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/5kgzpb/data
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.
Save
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.
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.
Save
Deep Learning is the current standard text on deep learning.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow provides a practical introduction to machine learning using Python.
Reinforcement Learning: An Introduction provides a comprehensive overview of reinforcement learning.
Save
Data Mining with R comprehensive guide to data mining using R.
Data Science for Business provides a practical introduction to data science for business applications.
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.
Save
The Master Algorithm provides a grand overview of the history and future of machine learning.
Weapons of Math Destruction provides a critical look at the role of data science in society.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/5kgzpb/data
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Effort
about five hours a week for four weeks
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
Good to know
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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