Data Mining Project from Coursera

Note: You should complete all the other courses in this Specialization before beginning this course.

This six-week long Project course of the Data Mining Specialization will allow you to apply the learned algorithms and techniques for data mining from the previous courses in the Specialization, including Pattern Discovery, Clustering, Text Retrieval, Text Mining, and Visualization, to solve interesting real-world data mining challenges. Specifically, you will work on a restaurant review data set from Yelp and use all the knowledge and skills you’ve learned from the previous courses to mine this data set to discover interesting and useful knowledge. The design of the Project emphasizes: 1) simulating the workflow of a data miner in a real job setting; 2) integrating different mining techniques covered in multiple individual courses; 3) experimenting with different ways to solve a problem to deepen your understanding of techniques; and 4) allowing you to propose and explore your own ideas creatively.

The goal of the Project is to analyze and mine a large Yelp review data set to discover useful knowledge to help people make decisions in dining. The project will include the following outputs:

1. Opinion visualization: explore and visualize the review content to understand what people have said in those reviews.

2. Cuisine map construction: mine the data set to understand the landscape of different types of cuisines and their similarities.

3. Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular dishes of a particular cuisine.

4. Recommendation of restaurants to help people decide where to dine: mine the data set to rank restaurants for a specific dish and predict the hygiene condition of a restaurant.

From the perspective of users, a cuisine map can help them understand what cuisines are there and see the big picture of all kinds of cuisines and their relations. Once they decide what cuisine to try, they would be interested in knowing what the popular dishes of that cuisine are and decide what dishes to have. Finally, they will need to choose a restaurant. Thus, recommending restaurants based on a particular dish would be useful. Moreover, predicting the hygiene condition of a restaurant would also be helpful.

By working on these tasks, you will gain experience with a typical workflow in data mining that includes data preprocessing, data exploration, data analysis, improvement of analysis methods, and presentation of results. You will have an opportunity to combine multiple algorithms from different courses to complete a relatively complicated mining task and experiment with different ways to solve a problem to understand the best way to solve it. We will suggest specific approaches, but you are highly encouraged to explore your own ideas since open exploration is, by design, a goal of the Project.

You are required to submit a brief report for each of the tasks for peer grading. A final consolidated report is also required, which will be peer-graded.

What's inside

Syllabus

Orientation

In this module, you will become familiar with the course, your instructor, your classmates, and our learning environment.

Task 1 - Exploration of a Data Set

Task 2 - Cuisine Clustering and Map Construction

Task 3 - Dish Recognition

Task 4 & 5 - Popular Dishes and Restaurant Recommendation

Task 6

Final Report

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches data mining algorithms and techniques used in practice

Helps learners build a portfolio of data mining work

Taught by instructors recognized for their work in data mining

Strong for learners with prior experience in data mining

Course requires learners to complete other courses in the specialization first

Reviews summary

Course with good content, limited support

Learners say this data mining project's course is moderately well received. Positive reviews often mention good content and interesting ideas. However, negative reviews show concern about lack of support, outdated code, irresponsive instructors, and course inactivity.

Content is generally good

"Course content is excellent"

"Good ideas"

Code is outdated

"code somewhat out of date"

"Some of the links and recommended packages are not working anymore"

Support is limited

"lack of support"

"irresponsive people in charge"

"Very inactive course and staff"

"Lack of support"

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 Python programming

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Reinforce your Python programming skills to ensure you have a strong foundation for data mining tasks and assignments.

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Review basic Python syntax, data structures, and functions.
Practice writing simple Python scripts.

Review data mining techniques

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Refresh your understanding of data mining techniques to reinforce your foundation and strengthen your comprehension of the course materials.

Browse courses on Data Mining

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Revisit foundational concepts of data mining, including data preprocessing, data exploration, and data analysis.
Review specific data mining algorithms and techniques, such as clustering, classification, and association rule mining.

Data Preprocessing Practice

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Practice data preprocessing techniques to improve data quality and prepare it for analysis.

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Load and explore the data set.
Identify and handle missing values.
Clean and standardize data.

Seven other activities

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Peer Review of Data Exploration Methods

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Engage with peers to discuss and compare different data exploration methods for the data set.

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Present your data exploration findings.
Review and provide feedback on others' findings.
Compare and contrast different approaches.
Identify areas for improvement.
Refine your own data exploration methods.

Follow online data mining tutorials

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Explore additional resources and tutorials to supplement your learning, expanding your knowledge and understanding of data mining techniques.

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Identify reputable online courses or tutorials that cover specific data mining topics or techniques.
Follow the tutorials, complete exercises, and engage in discussion forums to enhance your understanding.

Participate in data mining study groups

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Engage with peers in study groups to discuss complex concepts, share knowledge, and provide support throughout your learning journey.

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Join or form a study group with other data mining learners.
Collaborate on projects, discuss course materials, and share insights.

Develop a Cuisine Map

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Create a visual representation of the different cuisines in the data set, highlighting similarities and relationships.

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Cluster cuisines based on similar dishes.
Develop a map layout.
Visualize the cuisine clusters on the map.
Include interactive features for user exploration.

Build a data mining portfolio

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Create a portfolio of data mining projects to demonstrate your skills, showcase your knowledge, and enhance your employability.

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Identify real-world data sets and develop data mining projects that address specific business or research problems.
Apply data mining techniques to analyze data, extract insights, and create effective visualizations.
Document your projects, including project goals, methods, results, and discussion.

Contribute to open-source data mining projects

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Engage with the open-source community by contributing to data mining projects, expanding your technical skills and collaborating with other developers.

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Identify open-source data mining projects that align with your interests and skills.
Contribute code, documentation, or bug fixes to the project.

Mentor junior data miners

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Share your knowledge and expertise by mentoring junior data miners, reinforcing your understanding and fostering a supportive learning community.

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Identify opportunities to mentor junior data miners through online forums, university programs, or personal connections.
Provide guidance, answer questions, and share resources to support their learning.

Career center

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

Data Scientist

Data Scientists are responsible for developing and applying analytical models to solve business problems. They use their knowledge of statistics, machine learning, and data mining to extract insights from data. The Data Mining Project course can help students develop the skills and knowledge necessary to become a successful Data Scientist. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts collect, process, and analyze large data sets to extract meaningful insights and trends. They use statistical techniques, programming skills, and data visualization tools to identify patterns and relationships in data. The Data Mining Project course can help build a foundation for success as a Data Analyst by teaching students how to mine data, discover patterns, and present results. Students will gain experience with data preprocessing, exploration, analysis, and visualization, which are all essential skills for Data Analysts.

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Machine Learning Engineer

Machine Learning Engineers develop, deploy, and maintain machine learning models. They use their knowledge of machine learning algorithms, software engineering, and data engineering to build and implement machine learning solutions. The Data Mining Project course can help students develop the skills and knowledge necessary to become a successful Machine Learning Engineer. Students will learn how to apply machine learning algorithms to real-world problems and gain experience with data preprocessing, exploration, and visualization.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make better decisions. They collect, analyze, and interpret data to identify trends, patterns, and opportunities. The Data Mining Project course can help Business Analysts develop the skills and knowledge necessary to be successful in their roles. Students will learn how to mine data, discover patterns, and present results, which are all essential skills for Business Analysts.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. They use their knowledge of optimization, statistics, and data mining to develop and implement solutions to improve efficiency and productivity. The Data Mining Project course can help Operations Research Analysts develop the skills and knowledge necessary to be successful in their roles. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Software Engineer

Software Engineers design, develop, and maintain software applications. They use their knowledge of programming languages, software development tools, and data structures to build and implement software solutions. The Data Mining Project course may be useful for Software Engineers who want to develop data mining applications. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Statistician

Statisticians collect, analyze, and interpret data to provide insights and make predictions. They use their knowledge of probability, statistics, and data analysis techniques to solve problems and make informed decisions. The Data Mining Project course may be useful for Statisticians who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Data Visualization Specialist

Data Visualization Specialists design and develop visual representations of data to communicate insights and trends. They use their knowledge of data visualization tools and techniques to create clear and effective data visualizations. The Data Mining Project course may be useful for Data Visualization Specialists who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

See salaries and explore the career path for Data Visualization Specialist

Fraud Analyst

Fraud Analysts investigate and prevent fraud. They use their knowledge of fraud detection techniques and data analysis to identify and mitigate fraudulent activities. The Data Mining Project course may be useful for Fraud Analysts who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Database Administrator

Database Administrators manage and maintain databases. They use their knowledge of database management systems and data structures to ensure that databases are running smoothly and efficiently. The Data Mining Project course may be useful for Database Administrators who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Risk Analyst

Risk Analysts identify, assess, and manage risks. They use their knowledge of risk management techniques and data analysis to develop and implement risk management strategies. The Data Mining Project course may be useful for Risk Analysts who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Data Engineer

Data Engineers design, build, and maintain data pipelines and data infrastructure. They use their knowledge of data engineering tools and techniques to ensure that data is available for analysis and use. The Data Mining Project course may be useful for Data Engineers who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They use their knowledge of probability, statistics, and data analysis techniques to develop and implement trading strategies. The Data Mining Project course may be useful for Quantitative Analysts who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Actuary

Actuaries use mathematical and statistical models to assess risk and uncertainty. They use their knowledge of probability, statistics, and data analysis techniques to develop and implement risk management strategies. The Data Mining Project course may be useful for Actuaries who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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Compliance Analyst

Compliance Analysts ensure that organizations comply with laws and regulations. They use their knowledge of compliance regulations and data analysis techniques to identify and mitigate risks. The Data Mining Project course may be useful for Compliance Analysts who want to develop data mining skills. Students will learn how to apply data mining algorithms and techniques to real-world problems and gain experience with data exploration, analysis, and visualization.

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