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Qin (Christine) Lv
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.
Read more
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.
Read more
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
Register for this course and see more details by visiting:
OpenCourser.com/course/1mbx5y/data
What's inside
Syllabus
Data Mining Project
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.
Read more
Syllabus
Data Mining Project
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.
Read more
Project Proposal
This week discusses in detail what should be included in your project proposal and ends with an opportunity to craft your own.
Project Checkpoint
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.
Final Project Report
This week discusses in detail the final project report, highlighting the importance of summarizing the key findings and analyzing the overall project process.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers the fundamentals of data mining, including techniques, tools, and applications
Emphasizes hands-on experience, with guidance in designing and implementing a real-world data mining project
Suitable for individuals with a background in computer science, statistics, or a related field
Can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees
Instructors Qin (Christine) Lv have expertise in data mining and machine learning
Save this course
Save Data Mining Project to your list so you can find it easily later:
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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 Project with these
activities:
Review the Basics of Data Mining and Machine Learning
Show steps
Refresh your memory on the concepts of data mining and machine learning before embarking on the project.
Browse courses on
Data Mining
Show steps
-
Read the course syllabus and review the readings on data mining and machine learning.
-
Revisit any online tutorials or resources that you have used in the past to learn data mining and machine learning.
-
Complete a few practice problems or exercises on data mining and machine learning.
Attend the Course Office Hours
Show steps
Attend the course office hours to get personalized help from the instructors and TAs.
Show steps
-
Attend the office hours regularly.
-
Bring specific questions or materials that you want to discuss with the instructors or TAs.
Identify a Real-World Data Mining Problem
Show steps
Get started on the Data Mining Project by identifying a real-world data mining problem that you are interested in solving.
Browse courses on
Problem Formulation
Show steps
-
Read the case studies in the course materials for inspiration.
-
Brainstorm with classmates or colleagues to identify potential data mining problems.
-
Research different industries and domains to find a problem that is relevant to your interests and skillset.
-
Define the problem scope and objectives clearly.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Form a Study Group
Show steps
Connect with classmates and form a study group to support each other and stay motivated throughout the course.
Show steps
-
Identify a few classmates who are interested in collaborating.
-
Set regular meeting times and locations.
-
Establish clear roles and responsibilities for each member.
-
Use the study group to review material, work through problems, and prepare for assessments.
Complete the Data Mining Project Proposal
Show steps
Practice your data mining and project planning skills by completing a proposal for your Data Mining Project.
Show steps
-
Write a clear and concise statement of the problem that you will be solving.
-
Describe the dataset that you will be using.
-
Outline the data mining techniques that you will use.
-
Describe the evaluation metrics that you will use to assess the performance of your model.
-
Identify the resources that you will need to complete the project.
Write a Blog Post on Your Data Mining Project
Show steps
Share your experience working on the Data Mining Project with a wider audience by writing a blog post.
Show steps
-
Come up with a catchy title for your blog post.
-
Write an introduction that explains the purpose of your data mining project.
-
Describe the methods and techniques that you used.
-
Discuss the results of your project and what you learned.
-
Proofread your blog post carefully before publishing it.
Participate in a Data Mining Competition
Show steps
Challenge yourself by participating in a data mining competition to apply your skills and knowledge.
Show steps
-
Research different data mining competitions.
-
Choose a competition that is aligned with your interests and skillset.
-
Form a team or work independently.
-
Study the data and develop a model.
-
Submit your solution and track your progress.
Review the Basics of Data Mining and Machine Learning
Show steps
Refresh your memory on the concepts of data mining and machine learning before embarking on the project.
Browse courses on
Data Mining
Show steps
Show steps
Show steps
- Read the course syllabus and review the readings on data mining and machine learning.
- Revisit any online tutorials or resources that you have used in the past to learn data mining and machine learning.
- Complete a few practice problems or exercises on data mining and machine learning.
Attend the Course Office Hours
Show steps
Attend the course office hours to get personalized help from the instructors and TAs.
Show steps
Show steps
Show steps
- Attend the office hours regularly.
- Bring specific questions or materials that you want to discuss with the instructors or TAs.
Identify a Real-World Data Mining Problem
Show steps
Get started on the Data Mining Project by identifying a real-world data mining problem that you are interested in solving.
Browse courses on
Problem Formulation
Show steps
Show steps
Show steps
- Read the case studies in the course materials for inspiration.
- Brainstorm with classmates or colleagues to identify potential data mining problems.
- Research different industries and domains to find a problem that is relevant to your interests and skillset.
- Define the problem scope and objectives clearly.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Form a Study Group
Show steps
Connect with classmates and form a study group to support each other and stay motivated throughout the course.
Show steps
Show steps
Show steps
- Identify a few classmates who are interested in collaborating.
- Set regular meeting times and locations.
- Establish clear roles and responsibilities for each member.
- Use the study group to review material, work through problems, and prepare for assessments.
Complete the Data Mining Project Proposal
Show steps
Practice your data mining and project planning skills by completing a proposal for your Data Mining Project.
Show steps
Show steps
Show steps
- Write a clear and concise statement of the problem that you will be solving.
- Describe the dataset that you will be using.
- Outline the data mining techniques that you will use.
- Describe the evaluation metrics that you will use to assess the performance of your model.
- Identify the resources that you will need to complete the project.
Write a Blog Post on Your Data Mining Project
Show steps
Share your experience working on the Data Mining Project with a wider audience by writing a blog post.
Show steps
Show steps
Show steps
- Come up with a catchy title for your blog post.
- Write an introduction that explains the purpose of your data mining project.
- Describe the methods and techniques that you used.
- Discuss the results of your project and what you learned.
- Proofread your blog post carefully before publishing it.
Participate in a Data Mining Competition
Show steps
Challenge yourself by participating in a data mining competition to apply your skills and knowledge.
Show steps
Show steps
Show steps
- Research different data mining competitions.
- Choose a competition that is aligned with your interests and skillset.
- Form a team or work independently.
- Study the data and develop a model.
- Submit your solution and track your progress.
Career center
Learners who complete Data Mining Project will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist can expect to collect, interpret, and sort through large amounts of data, as they work to find meaningful correlations and insights. This Data Mining Project course is a great fit for someone working or looking to work as a Data Scientist. The course teaches students how to design and implement a real-world data mining project. This includes problem formulation, literature survey, proposed work, evaluation, discussion and future work. These skills are essential for any Data Scientist.
Data Analyst
Data Analyst
A Data Analyst works with large volumes of data to extract meaningful insights and trends. They play a key role in making data-driven decisions and providing valuable information to stakeholders. This Data Mining Project course can be highly valuable to Data Analysts as it provides them with the opportunity to gain practical knowledge and hands-on experience in designing and implementing a real-world data mining project.
Business Analyst
Business Analyst
Business Analysts use data and analytical techniques to solve business problems and improve decision-making. They work closely with stakeholders to understand their needs and develop solutions that meet the business requirements. This Data Mining Project course can be valuable for Business Analysts as it equips them with the skills to collect, analyze, and interpret data, and to develop recommendations and solutions based on their findings.
Market Research Analyst
Market Research Analyst
Market Research Analysts study market trends and customer behavior to help businesses make informed decisions. They use data analysis techniques to understand consumer preferences and identify opportunities for growth. This Data Mining Project course can be beneficial for Market Research Analysts as it provides them with the skills to collect, analyze, and interpret data, and to develop insights and recommendations that can inform business decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems and improve operational efficiency. They develop and implement models to optimize processes, reduce costs, and improve decision-making. This Data Mining Project course can be valuable for Operations Research Analysts as it provides them with the skills to collect, analyze, and interpret data, and to develop and implement models that can solve real-world business problems.
Financial Analyst
Financial Analyst
Financial Analysts use data analysis techniques to evaluate financial performance, make investment recommendations, and manage risk. They help businesses and individuals make sound financial decisions. This Data Mining Project course can be beneficial for Financial Analysts as it provides them with the skills to collect, analyze, and interpret financial data, and to develop models and recommendations that can support investment and risk management decisions.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. They develop and implement models to predict future events and to price insurance products. This Data Mining Project course can be valuable for Actuaries as it provides them with the skills to collect, analyze, and interpret data, and to develop and implement models that can assess risk and uncertainty.
Statistician
Statistician
Statisticians use mathematical and statistical techniques to collect, analyze, and interpret data. They design and conduct studies, and develop models to predict future events and to draw conclusions from data. This Data Mining Project course can be valuable for Statisticians as it provides them with the skills to collect, analyze, and interpret data, and to develop and implement models that can solve real-world problems.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. They work with data scientists and analysts to ensure that data is available, reliable, and secure. This Data Mining Project course can be beneficial for Data Engineers as it provides them with the skills to design and implement data mining projects, and to work with data scientists and analysts to develop and implement data pipelines and infrastructure.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with users and stakeholders to understand their needs and develop solutions that meet those needs. This Data Mining Project course may be helpful for Software Engineers as it provides them with the skills to design and implement data mining projects, and to work with data scientists and analysts to develop and implement software applications that use data mining techniques.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They work with database users and stakeholders to ensure that data is available, reliable, and secure. This Data Mining Project course may be helpful for Database Administrators as it provides them with the skills to design and implement data mining projects, and to work with data scientists and analysts to develop and implement databases that support data mining applications.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. They work with data scientists and analysts to develop and implement machine learning solutions. This Data Mining Project course may be helpful for Machine Learning Engineers as it provides them with the skills to design and implement data mining projects, and to work with data scientists and analysts to develop and implement machine learning models.
Data Architect
Data Architect
Data Architects design and implement data architectures. They work with stakeholders to understand their data needs and to develop solutions that meet those needs. This Data Mining Project course may be helpful for Data Architects as it provides them with the skills to design and implement data mining projects, and to work with stakeholders to develop and implement data architectures that support data mining applications.
Information Security Analyst
Information Security Analyst
Information Security Analysts design, implement, and maintain security systems to protect data and information. They work with stakeholders to identify and mitigate security risks. This Data Mining Project course may be helpful for Information Security Analysts as it provides them with the skills to design and implement data mining projects, and to work with stakeholders to develop and implement security systems that protect data and information from data mining attacks.
Forensic Analyst
Forensic Analyst
Forensic Analysts investigate computer crimes and security breaches. They collect and analyze digital evidence to identify and prosecute criminals. This Data Mining Project course may be helpful for Forensic Analysts as it provides them with the skills to design and implement data mining projects, and to collect and analyze digital evidence to identify and prosecute criminals.
For more career information including salaries, visit:
OpenCourser.com/course/1mbx5y/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 Project.
Save
An approachable yet thorough and wide-ranging introduction to data mining, this third edition covers the latest advancements in the field. It is an indispensable reference for students, researchers, and professionals who want to master the practical and theoretical aspects of data mining.
Save
A hands-on guide to data mining with R, this book presents a step-by-step approach to data mining, covering data preparation, model building, and result evaluation. It is an excellent resource for anyone who wants to learn how to use R for data mining.
A comprehensive yet easy-to-understand guide to data mining techniques, this book provides a thorough overview of the field and its applications. It is an excellent resource for anyone who wants to gain a broad understanding of data mining.
A practical guide to data science project design, this book provides a step-by-step approach to designing and implementing data mining projects. It is an excellent resource for anyone who wants to learn how to design and conduct successful data mining projects.
Save
A comprehensive look at big data analytics, this book provides a solid understanding of the technologies and techniques used in the field. It is an excellent resource for anyone who wants to learn more about big data analytics.
A business-oriented introduction to data mining, this book provides a comprehensive overview of the field and its applications. It is an excellent resource for anyone who wants to learn how to use data mining to improve business performance.
Save
A comprehensive introduction to R for data science, this book covers a wide range of topics, including data import, cleaning, transformation, visualization, and modeling. It is an excellent resource for anyone who wants to learn how to use R for data science.
A comprehensive introduction to Python for data analysis, this book covers a wide range of topics, including data import, cleaning, transformation, visualization, and modeling. It is an excellent resource for anyone who wants to learn how to use Python for data analysis.
A comprehensive guide to data mining with SAS, this book provides a step-by-step approach to using SAS for data mining. It is an excellent resource for anyone who wants to learn how to use SAS for data mining.
A comprehensive guide to data mining with Oracle Data Mining, this book provides a step-by-step approach to using Oracle Data Mining for data mining. It is an excellent resource for anyone who wants to learn how to use Oracle Data Mining for data mining.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1mbx5y/data
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Effort
about ten hours a week for four weeks
Level
Intermediate
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Qin (Christine) Lv
Language
English
Transcripts
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Good to know
Covers the fundamentals of data mining, including techniques, tools, and applications
Emphasizes hands-on experience, with guidance in designing and implementing a real-world data mining project
Suitable for individuals with a background in computer science, statistics, or a related field
Can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees
Instructors Qin (Christine) Lv have expertise in data mining and machine learning
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