Pattern Discovery in Data Mining from Coursera

What's inside

Syllabus

Course Orientation

The course orientation will get you familiar with the course, your instructor, your classmates, and our learning environment.

Module 1

Module 1 consists of two lessons. Lesson 1 covers the general concepts of pattern discovery. This includes the basic concepts of frequent patterns, closed patterns, max-patterns, and association rules. Lesson 2 covers three major approaches for mining frequent patterns. We will learn the downward closure (or Apriori) property of frequent patterns and three major categories of methods for mining frequent patterns: the Apriori algorithm, the method that explores vertical data format, and the pattern-growth approach. We will also discuss how to directly mine the set of closed patterns.

Module 2

Module 2 covers two lessons: Lessons 3 and 4. In Lesson 3, we discuss pattern evaluation and learn what kind of interesting measures should be used in pattern analysis. We show that the support-confidence framework is inadequate for pattern evaluation, and even the popularly used lift and chi-square measures may not be good under certain situations. We introduce the concept of null-invariance and introduce a new null-invariant measure for pattern evaluation. In Lesson 4, we examine the issues on mining a diverse spectrum of patterns. We learn the concepts of and mining methods for multiple-level associations, multi-dimensional associations, quantitative associations, negative correlations, compressed patterns, and redundancy-aware patterns.

Module 3

Module 3 consists of two lessons: Lessons 5 and 6. In Lesson 5, we discuss mining sequential patterns. We will learn several popular and efficient sequential pattern mining methods, including an Apriori-based sequential pattern mining method, GSP; a vertical data format-based sequential pattern method, SPADE; and a pattern-growth-based sequential pattern mining method, PrefixSpan. We will also learn how to directly mine closed sequential patterns. In Lesson 6, we will study concepts and methods for mining spatiotemporal and trajectory patterns as one kind of pattern mining applications. We will introduce a few popular kinds of patterns and their mining methods, including mining spatial associations, mining spatial colocation patterns, mining and aggregating patterns over multiple trajectories, mining semantics-rich movement patterns, and mining periodic movement patterns.

Week 4

Module 4 consists of two lessons: Lessons 7 and 8. In Lesson 7, we study mining quality phrases from text data as the second kind of pattern mining application. We will mainly introduce two newer methods for phrase mining: ToPMine and SegPhrase, and show frequent pattern mining may be an important role for mining quality phrases in massive text data. In Lesson 8, we will learn several advanced topics on pattern discovery, including mining frequent patterns in data streams, pattern discovery for software bug mining, pattern discovery for image analysis, and pattern discovery and society: privacy-preserving pattern mining. Finally, we look forward to the future of pattern mining research and application exploration.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops foundational knowledge in data mining including general concepts, pattern discovery, phrase mining, and various applications

Focuses on one subfield pattern discovery with in-depth concepts, methods, and various applications

Taught by Jiawei Han, recognized as one of the leading researchers in data mining

Provides opportunities to practice methods on massive transactional data found in industry

Covers diverse patterns and evaluation measures, which enhances learners' ability to analyze patterns

Examines advanced topics in pattern discovery, including data stream mining and privacy-preserving techniques

Reviews summary

Practice with data mining patterns

Learners say that Pattern Discovery in Data Mining is a well-structured course that covers all fundamentals of data mining patterns. It effectively points out directions for further study and is relatively easy to comprehend. Although learners agree that the course lacks programming assignments, they highlight the engaging assignments in the course. The quizzes are perceived as thought-provoking and effectively test learners' understanding of the material. Although the lectures have been described as unclear, the assignments are seen as helpful in applying the concepts learned. Overall, learners recommend this course, especially for those interested in theoretical and algorithmic aspects of pattern discovery.

Assignments are helpful in applying concepts.

"assignments: programming assignments were helpful to apply the course"

Thought-provoking quizzes effectively test understanding.

"The quizzes aren’t bad if you pay attention and read the forums."

Course focuses on theoretical and algorithmic aspects of pattern discovery.

"This is a very good theoretical and algorithmic course on pattern discovery."

Lectures can be unclear at times.

"Way too hard to find out whta the teacher was talking abut, had to make too much research on texts."

Course lacks practical programming assignments.

"I too think it lacked a practical, programming part."

"No real example of data or programming application."

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 Pattern Discovery in Data Mining with these activities:

Connect with Experienced Data Miners

Show steps

Accelerate your learning journey by seeking guidance from experienced professionals in the field of pattern discovery.

Browse courses on Mentorship

Show steps

Identify potential mentors through online platforms, industry events, or personal connections.
Reach out to mentors, briefly introduce yourself, and express your interest in their mentorship.
Attend meetings or schedule calls to discuss your progress, seek advice, and gain valuable insights.

Organize Course Materials

Show steps

Enhance your learning experience by organizing and reviewing course materials, improving retention and facilitating easy access to information.

Browse courses on Organization

Show steps

Set up a dedicated folder or notebook for course materials.
Regularly file lecture notes, assignments, and other course-related documents.
Categorize and label materials for efficient retrieval.
Review and summarize key concepts to reinforce understanding.

Review Frequent Patterns

Show steps

Become more comfortable with the foundational concepts of pattern discovery before you start Module 1 of the course.

Show steps

Review the Definition and Examples of Frequent Patterns
Go Over Properties of Frequent Patterns
Explore Applications of Frequent Patterns

Five other activities

Expand to see all activities and additional details

Show all eight activities

Compile a Glossary of Data Mining Terms

Show steps

Expand your vocabulary and enhance your understanding of key terms used in the field of data mining.

Browse courses on Data Mining

Show steps

Collect definitions of important data mining terms from various sources.
Create a comprehensive glossary with clear and concise definitions.
Organize the glossary alphabetically or by category for easy reference.

Complete Problems on Frequent Pattern Mining Algorithms

Show steps

Solidify your knowledge of frequent pattern mining algorithms by solving example problems.

Show steps

Select a set of example problems focusing on Apriori, Pattern Growth, and FP-growth.
Go through algorithms, step-by-step, mining frequent patterns for each.
Review and analyze results to validate understanding.

Explore Applications of Pattern Discovery

Show steps

Expand your knowledge by familiarizing yourself with the diverse ways pattern discovery is utilized across industries.

Browse courses on Pattern Discovery

Show steps

Identify different industries that leverage pattern discovery for decision-making.
Research and explore real-world examples of how pattern discovery is applied in these industries.
Analyze the impact and significance of pattern discovery in improving outcomes.

Volunteer as a Mentor for Beginners

Show steps

Deepen your understanding of pattern discovery concepts and make a positive impact by mentoring students new to the field.

Browse courses on Mentoring

Show steps

Join online forums or discussion groups related to pattern discovery.
Identify beginners seeking guidance and offer your support.
Provide clear explanations, answer questions, and share your insights.
Encourage students to engage in active learning and foster a positive learning environment.

Develop a Visualization for a Data Mining Case Study

Show steps

Demonstrate your understanding of data mining techniques by creating a visual representation of insights gained from a case study.

Browse courses on Data Visualization

Show steps

Select a data mining case study that aligns with your interests.
Extract key patterns and insights from the case study.
Choose an appropriate visualization method to effectively convey the insights.
Create a visually appealing and informative visualization.
Write a brief report summarizing your findings and the significance of the visualization.

Career center

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

Data Engineer

Data Engineers use their knowledge of programming and data to design and develop data pipelines. These pipelines automate the processes of collecting, cleaning, and organizing data for analysis. Often, Data Engineers are responsible for building and maintaining data warehouses and data lakes in support of an organization's business intelligence and analytics teams. The course introduces learners to the fundamentals of data mining, including approaches like the Apriori algorithm, vertical data format exploration, and the pattern-growth approach. This foundation prepares learners to build a career as a Data Engineer where they may work with data mining tools and techniques on a daily basis.

See salaries and explore the career path for Data Engineer

Data Analyst

Data Analysts provide organizations with actionable insights by analyzing data. They combine their knowledge of statistics, data mining, and programming to identify trends, extract meaningful patterns, and help organizations make data-driven decisions. Learners will be exposed to evaluation methods for patterns, such as lift and chi-square, and learn about better alternatives, such as null invariance. This skill directly supports the work of a Data Analyst by providing them with additional methods for extracting insights from data and communicating them to stakeholders.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists combine their knowledge of programming, statistics, and data mining to build machine learning models. These models analyze massive datasets and perform tasks such as prediction, classification, and clustering. Learners will study methods for mining sequential patterns, such as GSP, SPADE, and PrefixSpan. This skill supports the work of a Data Scientist by providing them with the techniques needed to identify sequential patterns in their data, potentially improving the accuracy and performance of their models.

See salaries and explore the career path for Data Scientist

Research Scientist

Research Scientists conduct scientific research with the goal of advancing knowledge and developing new technologies. They may specialize in a particular field, such as data mining, and work in academic institutions, government labs, or private companies. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining diverse kinds of patterns, and mining sequential patterns. It provides a solid foundation for a career as a Research Scientist in the field of data mining.

See salaries and explore the career path for Research Scientist

Machine Learning Engineer

Machine Learning Engineers develop, deploy, and maintain machine learning models. They work on projects such as image recognition, natural language processing, and speech recognition. Learners who take this course will be introduced to pattern mining, which is a fundamental technique used in machine learning for tasks such as feature extraction and model selection. Additionally, this course provides insights into evaluating and interpreting patterns, which is crucial for the success of machine learning models.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to identify and solve business problems. They work with stakeholders to understand their needs and develop data-driven solutions. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Business Analysts who work with large datasets and need to be able to extract insights from data in order to make informed recommendations.

See salaries and explore the career path for Business Analyst

Software Engineer

Software Engineers design, develop, and maintain software systems. They work on projects such as operating systems, web applications, and mobile apps. Learners who take this course will gain a foundation in pattern discovery, which is a fundamental technique used in software engineering for tasks such as code optimization and bug detection. Additionally, this course provides insights into evaluating and interpreting patterns, which is crucial for the success of software systems.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data. They work in the financial industry, helping to make investment decisions. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining sequential patterns, and mining spatiotemporal and trajectory patterns. It provides a solid foundation for a career as a Quantitative Analyst, who needs to be able to identify and interpret patterns in financial data in order to make informed investment recommendations.

See salaries and explore the career path for Quantitative Analyst

Product Manager

Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring products to market that meet the needs of customers. Learners who take this course will gain a foundation in pattern discovery, which is a fundamental technique used in product management for tasks such as market research and customer segmentation. Additionally, this course provides insights into evaluating and interpreting patterns, which is crucial for the success of new products.

See salaries and explore the career path for Product Manager

Statistician

Statisticians collect, analyze, and interpret data. They work in a variety of fields, such as healthcare, education, and government. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Statisticians who work with large datasets and need to be able to extract insights from data in order to make informed recommendations.

See salaries and explore the career path for Statistician

Financial Analyst

Financial Analysts provide organizations with financial advice. They analyze financial data, make recommendations, and help organizations make investment decisions. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining sequential patterns, and mining spatiotemporal and trajectory patterns. It provides a solid foundation for a career as a Financial Analyst, who needs to be able to identify and interpret patterns in financial data in order to make informed investment recommendations.

See salaries and explore the career path for Financial Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex business problems. They work in a variety of industries, such as manufacturing, healthcare, and transportation. This course covers the theory and practice of pattern discovery in data mining, including topics such as pattern evaluation, mining sequential patterns, and mining spatiotemporal and trajectory patterns. It provides a solid foundation for a career as an Operations Research Analyst, who needs to be able to identify and interpret patterns in data in order to make informed recommendations.

See salaries and explore the career path for Operations Research Analyst

Data Architect

Data Architects design and build data architectures for organizations. They work with stakeholders to understand their data needs and develop data solutions that meet those needs. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Data Architects who need to be able to extract insights from data in order to design and build effective data architectures.

See salaries and explore the career path for Data Architect

Database Administrator

Database Administrators manage and maintain databases. They work with users to ensure that databases are running smoothly and that data is secure. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Database Administrators who need to be able to extract insights from data in order to optimize database performance and security.

See salaries and explore the career path for Database Administrator

Business Intelligence Analyst

Business Intelligence Analysts use data to help organizations make better decisions. They work with stakeholders to understand their data needs and develop data-driven solutions. This course provides an introduction to pattern discovery in data mining. Learners will learn how to identify patterns and trends in data, and how to use this information to make better decisions. This course is particularly relevant for Business Intelligence Analysts who need to be able to extract insights from data in order to make informed recommendations.

See salaries and explore the career path for Business Intelligence Analyst