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Practical Engineering Data Mining

Techniques and Uses

Kirankumar Trivedi

This course delves into both the theoretical aspects and practical applications of data mining within the field of engineering. It provides a comprehensive review of the essential fundamentals and central concepts underpinning data mining. Additionally, it introduces pivotal data mining methodologies and offers a guide to executing these techniques through various algorithms. Students will be introduced to a range of data mining techniques, such as data preprocessing, the extraction of association rules, classification, prediction, clustering, and the exploration of complex data, and will implement a capstone project exploring the same. Additionally, we will use case studies to explore the application of data mining across diverse sectors, including but not limited to manufacturing, healthcare, medicine, business, and various service industries.

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Syllabus

Intro to Data Mining in Engineering
In this module, participants will explore essential data concepts across domains, understanding diverse data types, attributes, and features. They will grasp the fundamental principles, methodologies, and scope of data mining, enabling them to effectively analyze data and extract valuable insights. Through this comprehensive approach, learners will gain proficiency in utilizing key data concepts, facilitating informed decision-making and innovation across various domains.
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Provides a comprehensive review of essential data mining fundamentals and central concepts underpinning data mining, which is highly relevant for engineering applications
Explores the application of data mining across diverse sectors, including manufacturing, healthcare, medicine, business, and various service industries
Introduces pivotal data mining methodologies and offers a guide to executing these techniques through various algorithms, which are essential for practical application
Examines dimensionality reduction methods such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), which are standard techniques in the field
Explores performance evaluation matrices, including the F1-Score, Matthews Correlation Coefficient (MCC), propensity scores, and the AUC-ROC curve, which are essential for model assessment
Examines the concept of the Bias-Variance Trade-off in machine learning, which is a fundamental concept for building models that generalize well

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Reviews summary

Practical engineering data mining overview

According to learners, this course provides a largely positive introduction to engineering data mining. Students particularly highlight the valuable capstone project and the course's focus on practical applications and case studies, which helps bridge theory and real-world use. The curriculum covers a broad range of fundamental data mining techniques, offering a solid overview. While the course is generally well-received for its content and structure, some students note that a strong background in mathematics, statistics, and programming is beneficial or even necessary to fully grasp the material. A few recent comments suggest that some specific tools or libraries demonstrated might be slightly dated, though the underlying concepts remain highly relevant.
Solid math, statistics, and programming background helps.
"Be prepared with a decent background in statistics and linear algebra, it helps a lot with the underlying concepts."
"Knowing Python and basic data handling libraries beforehand is definitely a plus."
"The course moves quickly at times, and prior programming experience makes following along much easier."
"I struggled a bit with some mathematical details without a strong stats background."
Emphasizes practical use and real-world case studies.
"I found the case studies really helpful for understanding how data mining is used in different industries."
"The course balances theory with practical applications, which is exactly what I needed for my work."
"Learning how to apply the techniques through practical examples made the concepts much clearer."
Covers a wide array of essential data mining techniques.
"The course covers a good range of topics, from preprocessing to classification and clustering. Great overview."
"I learned about several key data mining algorithms and methods I wasn't familiar with before."
"The syllabus outlines a comprehensive set of techniques which are all introduced effectively."
Apply learning via a highly beneficial capstone project.
"The capstone project was incredibly valuable; applying everything we learned to a real-world dataset solidified my understanding completely."
"I really appreciated the final project as it gave me hands-on experience combining different techniques."
"Working on the capstone project was the highlight for me, it made the course practical and engaging."
Some tools/libraries demonstrated could be more current.
"While the core concepts are solid, some of the specific code examples or libraries used feel a bit out of date compared to current standards."
"It would be great if the course updated the tools demonstrated to reflect more recent versions or libraries commonly used today."
"The foundational knowledge is there, but students might need to adapt some examples to newer software environments."

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 Practical Engineering Data Mining: Techniques and Uses with these activities:
Review Statistical Concepts
Reinforce your understanding of fundamental statistical concepts. A solid grasp of statistics is crucial for data mining, especially for interpreting results and evaluating model performance.
Browse courses on Hypothesis Testing
Show steps
  • Review key statistical terms and formulas.
  • Work through practice problems involving hypothesis testing and regression.
  • Consult online resources or textbooks for clarification.
Review 'Data Mining: Concepts and Techniques'
Gain a deeper understanding of data mining principles and methodologies. This book serves as a comprehensive reference for various data mining techniques covered in the course.
View Data Mining: Concepts and Techniques on Amazon
Show steps
  • Read the chapters relevant to the course syllabus.
  • Take notes on key concepts and algorithms.
  • Work through the examples and exercises provided in the book.
Implement Data Preprocessing Techniques
Solidify your understanding of data preprocessing by implementing various techniques. This hands-on practice will improve your ability to clean and prepare data for data mining tasks.
Show steps
  • Choose a dataset from a public repository (e.g., Kaggle).
  • Implement data cleaning techniques (handling missing values, outliers).
  • Apply data transformation methods (normalization, standardization).
  • Document your preprocessing steps and rationale.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Write a Blog Post on a Data Mining Application
Solidify your understanding of data mining applications by writing a blog post. This activity will help you communicate your knowledge to a wider audience and reinforce your learning.
Show steps
  • Choose a specific application of data mining in engineering (e.g., fraud detection, predictive maintenance).
  • Research the application and gather relevant information.
  • Write a blog post that explains the application, the data mining techniques used, and the benefits achieved.
  • Include visuals and examples to illustrate your points.
Create a Data Visualization Dashboard
Enhance your ability to communicate data insights effectively. Creating a dashboard will allow you to visualize data patterns and trends, making them easier to understand and interpret.
Show steps
  • Select a dataset relevant to engineering data mining.
  • Choose a data visualization tool (e.g., Tableau, Power BI).
  • Design and implement interactive visualizations to explore the data.
  • Create a dashboard that summarizes key findings and insights.
Capstone Project: Predictive Maintenance
Apply your data mining skills to a real-world engineering problem. This project will allow you to integrate the concepts learned throughout the course and demonstrate your ability to solve complex problems.
Show steps
  • Obtain a dataset related to equipment maintenance (e.g., sensor data, maintenance logs).
  • Preprocess the data to handle missing values and inconsistencies.
  • Apply classification or regression techniques to predict equipment failures.
  • Evaluate the performance of your model using appropriate metrics.
  • Document your project methodology and findings.
Review 'The Elements of Statistical Learning'
Deepen your understanding of the statistical foundations of data mining. This book provides a rigorous treatment of statistical learning methods.
View The Elements of Statistical Learning: Data... on Amazon
Show steps
  • Focus on chapters related to classification and regression.
  • Work through the mathematical derivations and examples.
  • Compare the methods presented in the book with those covered in the course.

Career center

Learners who complete Practical Engineering Data Mining: Techniques and Uses will develop knowledge and skills that may be useful to these careers:
Data Scientist
A data scientist extracts knowledge and insights from data using scientific methods, algorithms, and systems. A course focusing on practical engineering data mining helps to build a strong foundation in the essential fundamentals and methodologies that are core to a data scientist's work. The study of data preprocessing, association rule extraction, classification, prediction, and clustering taught in this course directly translates into the ability to transform raw data into actionable intelligence. Moreover, the capstone project offers invaluable hands-on experience, while the exploration of data mining applications in various sectors such as manufacturing, healthcare, medicine, business, and service industries gives a practical understanding of how data mining can be applied across industries. Those working as a data scientist may find that the modules on dimensionality reduction and performance evaluation matrices are especially useful.
See salaries and explore the career path for Data Scientist
Machine Learning Engineer
A machine learning engineer develops, tests, and deploys machine learning models. This course on practical engineering data mining can build a foundation of knowledge for aspiring machine learning engineers. An understanding of data preprocessing, classification, and prediction are vital when creating machine learning models. You can use the course's case studies that explore data mining applications across manufacturing, healthcare, and business to inform the design of your own machine learning solutions. Ultimately, this course gives you insight into how machine learning models can be applied and what machine learning engineers do. The module on performance evaluation matrices may be particularly useful for understanding and optimizing machine learning model performance.
See salaries and explore the career path for Machine Learning Engineer
Business Intelligence Analyst
A business intelligence analyst examines data trends to advise a company on how best to improve their business practices. Data mining techniques are a core part of how business intelligence analysts extract value from data. The course's study of data preprocessing, the creation of association rules, and different clustering methods can help you to see patterns and improve business decisions. Moreover, the application of data mining across areas such as business and service industries gives a practical understanding of how data mining can be adapted to unique business cases. Working as a business intelligence analyst, the module on exploratory data analysis and visualization may be helpful in making data more accessible to business stakeholders.
See salaries and explore the career path for Business Intelligence Analyst
Data Analyst
A data analyst collects, processes, and performs statistical analysis of data. This course on practical engineering data mining helps to build a foundation of knowledge for anyone interested in becoming a data analyst. You can use the techniques of data preprocessing, association rule extraction, classification, prediction, and clustering taught in this course to turn raw data into insights. Furthermore, the capstone project gives invaluable hands-on experience, helping you to prepare for the kinds of tasks that data analysts do. While performing the work of a data analyst, the module on exploratory data analysis and visualization can prove invaluable when communicating findings.
See salaries and explore the career path for Data Analyst
Data Architect
A data architect designs and maintains an organization's data infrastructure. This course provides you with data mining knowledge that may inform your decisions as a data architect. The course explores data concepts, types, attributes, and features, giving you an overview of the kinds of data that will need to be stored. By learning the scope of data mining, you will be in a better position to prepare data for data mining applications. Case studies that explore data mining applications across various sectors such as manufacturing, healthcare, business, and service industries may be helpful when planning a data warehouse solution. A data architect will find that the modules on dimensionality reduction and performance evaluation matrices are especially beneficial.
See salaries and explore the career path for Data Architect
Statistician
Statisticians develop and apply statistical theories and methods to collect, interpret, and summarize numerical data. While statisticians typically hold advanced degrees, a course on practical engineering data mining may be useful to give statisticians additional tools to analyze data. The course covers essential fundamentals and methodologies of data mining. Those working as a statistician might use their knowledge of key data concepts and data mining to explore data and visualize trends. In particular, a statistician will find the modules on dimensionality reduction and performance evaluation matrices to be beneficial.
See salaries and explore the career path for Statistician
Research Scientist
Research scientists design and conduct experiments, analyze data, and write reports to share findings. This course may be useful to research scientists who want to incorporate data mining techniques into their research. The course introduces pivotal data mining methodologies, such as data preprocessing, association rule extraction, classification, prediction, clustering, and the exploration of complex data. A research scientist may find the modules on dimensionality reduction and performance evaluation matrices to be especially helpful when working with research data.
See salaries and explore the career path for Research Scientist
Bioinformatician
A bioinformatician develops methods and software tools for understanding biological data. This course may be useful if you want to analyze biological data using data mining techniques. The course introduces data preprocessing, association rule extraction, classification, prediction, clustering, and the exploration of complex data. Additionally, case studies explore the application of data mining in healthcare and medicine. A bioinformatician will find the modules on dimensionality reduction and performance evaluation matrices especially helpful when working to analyze biological data.
See salaries and explore the career path for Bioinformatician
Market Research Analyst
A market research analyst studies market conditions to examine potential sales of a product or service. This course may be useful for market research analysts who want to incorporate data mining techniques in their work. The course introduces data preprocessing, association rule extraction, classification, prediction, and clustering. Furthermore, case studies explore the application of data mining in business and service industries. Dimensionality reduction techniques and performance evaluation metrics should be valuable to a market research analyst.
See salaries and explore the career path for Market Research Analyst
Financial Analyst
A financial analyst provides guidance to businesses and individuals making investment decisions. This course may be useful for financial analysts who want to incorporate data mining techniques to analyze financial data. The course introduces data preprocessing, association rule extraction, classification, prediction, and clustering. Moreover, case studies exploring data mining applications in business and service industries may be helpful. A financial analyst may find the modules on dimensionality reduction and performance evaluation matrices to be especially beneficial.
See salaries and explore the career path for Financial Analyst
Management Consultant
A management consultant helps organizations solve issues, create value, maximize growth, and improve business performance. While this role requires strong interpersonal and communication skills, a course on data mining may be useful for backing up recommendations with data. The course explores data concepts, types, attributes, and features, giving you an overview of the kinds of data that might be available in business. Furthermore, by understanding the scope of data mining, you will be in a better position to leverage data to inform your consulting work. The case studies presented in the course might be especially helpful.
See salaries and explore the career path for Management Consultant
Actuary
An actuary analyzes the financial costs of risk and uncertainty. This course may be useful for actuaries who want to incorporate data mining techniques to analyze risk. The course introduces data preprocessing, association rule extraction, classification, prediction, and clustering, all of which may be useful when attempting to predict risk. An actuary may find the modules on dimensionality reduction and performance evaluation matrices to be especially beneficial.
See salaries and explore the career path for Actuary
Researcher
A researcher investigates and studies materials, processes, or phenomena. This course may be useful for researchers looking to improve their data handling abilities. The course explores data concepts, types, attributes, and features, giving researchers a better understanding of data. By learning the scope of data mining, a researcher may be able to gather more useful insights from data. Researchers will likely find the module on EDA particularly insightful.
See salaries and explore the career path for Researcher
Software Developer
A software developer designs, develops, and tests software. While software developers typically focus on coding, a course on practical engineering data mining may be useful for software developers who want to build data-driven applications. The course explores data concepts, types, attributes, and features, giving developers a better understanding of the kinds of data that their applications will work with. Furthermore, by understanding the scope of data mining, developers will be in a better position to build applications for data mining tasks. Software developers will likely find the module on EDA particularly insightful.
See salaries and explore the career path for Software Developer
Engineer
An engineer designs, develops, and tests technical solutions. It may be useful for engineers who want to incorporate data mining techniques into their work to take this course. The course introduces data preprocessing, association rule extraction, classification, prediction, and clustering. Engineers may find the modules on dimensionality reduction and performance evaluation matrices to be especially beneficial.
See salaries and explore the career path for Engineer

Reading list

We've selected two 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 Practical Engineering Data Mining: Techniques and Uses.
Cover image
Data Mining: Concepts and Techniques
Save
Provides a comprehensive overview of data mining concepts and techniques. It covers a wide range of topics, including data preprocessing, association rule mining, classification, clustering, and outlier detection. It valuable reference for understanding the theoretical foundations and practical applications of data mining. This book is commonly used as a textbook in data mining courses.
Data Mining: Concepts and Techniques
Paperback
Check
Data Mining: Concepts and Techniques
Kindle Edition
Check
Cover image
The Elements of Statistical Learning
Save
Provides a rigorous treatment of statistical learning methods. It covers a wide range of topics, including linear regression, classification, model assessment, and unsupervised learning. While mathematically intensive, it offers valuable insights into the theoretical foundations of data mining algorithms. This book is best used as additional reading to provide more depth to the existing course.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$

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Course image
Effort
4 weeks of study, 2-4 hours per week
Level
Intermediate
Via
Coursera
Institution
Northeastern University
Instructor
Kirankumar Trivedi
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Provides a comprehensive review of essential data mining fundamentals and central concepts underpinning data mining, which is highly relevant for engineering applications
Explores the application of data mining across diverse sectors, including manufacturing, healthcare, medicine, business, and various service industries
Introduces pivotal data mining methodologies and offers a guide to executing these techniques through various algorithms, which are essential for practical application
Examines dimensionality reduction methods such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), which are standard techniques in the field
Explores performance evaluation matrices, including the F1-Score, Matthews Correlation Coefficient (MCC), propensity scores, and the AUC-ROC curve, which are essential for model assessment
Examines the concept of the Bias-Variance Trade-off in machine learning, which is a fundamental concept for building models that generalize well
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