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Data Mining Project

Approx. 11 hours to complete

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Course Summary

This course teaches you how to apply data mining techniques to real-world problems. You'll learn how to use popular data mining tools and software, and gain hands-on experience in analyzing large datasets and creating predictive models.

Key Learning Points

Learn how to apply data mining techniques to real-world problems
Gain hands-on experience in analyzing large datasets
Create predictive models using popular data mining tools and software

Learning Outcomes

Apply data mining techniques to real-world problems
Create predictive models using popular data mining tools and software
Analyze large datasets and gain hands-on experience

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics and programming
Access to popular data mining tools and software

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Hands-on exercises

Similar Courses

Introduction to Data Mining
Data Mining with Python
Advanced Data Mining with Weka

Related Education Paths

Related Books

Description

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.

Outline

Orientation
Welcome to the Data Mining Project!
Orientation Overview
Syllabus
About the Discussion Forums
Updating Your Profile
MeTA Installation and Overview
Data Set and Toolkit Acquisition

Task 1 - Exploration of a Data Set
Task 1 Overview
Task 1 Rubric

Task 2 - Cuisine Clustering and Map Construction
Task 2 Overview
Task 2 Rubric

Task 3 - Dish Recognition
Task 3 Overview
Task 3 Rubric

Task 4 & 5 - Popular Dishes and Restaurant Recommendation
Task 4 and 5 Overview
Task 4 and 5 Rubric

Task 6
Task 6 Overview
Task 6 Rubric

Final Report
Final Report Instructions
Final Report Rubric

Summary of User Reviews

The Data Mining Project course on Coursera has received positive reviews for its comprehensive coverage of data mining and machine learning techniques. Many users have praised the course for its practical approach to solving real-world problems using data analysis. Overall, the course has been highly recommended by users.

Key Aspect Users Liked About This Course

Practical approach to solving real-world problems using data analysis

Pros from User Reviews

Comprehensive coverage of data mining and machine learning techniques
Hands-on projects that provide practical experience with data analysis
Excellent instructors who are knowledgeable and responsive to questions
Flexible schedule allows learners to work at their own pace
Useful feedback on assignments to help improve understanding of course material

Cons from User Reviews

Some users have reported technical difficulties with the platform
The course can be challenging for those without a strong background in math or statistics
The workload can be heavy at times, especially for those with other commitments
Some users have found the course material to be outdated or not applicable to their specific field
The cost of the course can be prohibitive for some learners

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