Data Mining Project

Location type
Logo Coursera
Provider rating: starstarstarstar_borderstar_border 6.3 Coursera has an average rating of 6.3 (out of 4 reviews)

Need more information? Get more details on the site of the provider.

Description

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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 P…

Read the complete description

Frequently asked questions

There are no frequently asked questions yet. Send an Email to info@springest.com

Didn't find what you were looking for? See also: Data Mining, Visualization, Algorithms, Hour of Code, and Full time MBA.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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.

Created by:  University of Illinois at Urbana-Champaign
  • Taught by:  Jiawei Han, Abel Bliss Professor

    Department of Computer Science
  • Taught by:  ChengXiang Zhai, Professor

    Department of Computer Science
  • Taught by:  John C. Hart, Professor of Computer Science

    Department of Computer Science
Basic Info Course 6 of 6 in the Data Mining Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.4 stars Average User Rating 4.4See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from your peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

University of Illinois at Urbana-Champaign The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.

Syllabus


WEEK 1


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


1 video, 6 readings expand


  1. Video: Welcome to the Data Mining Project!
  2. Reading: Orientation Overview
  3. Reading: Syllabus
  4. Reading: About the Discussion Forums
  5. Reading: Updating Your Profile
  6. Reading: MeTA Installation and Overview
  7. Reading: Data Set and Toolkit Acquisition
  8. Discussion Prompt: Getting to Know Your Classmates


Task 1 - Exploration of a Data Set



2 readings expand


  1. Reading: Task 1 Overview
  2. Reading: Task 1 Rubric

Graded: Task 1 Submission

WEEK 2


Task 2 - Cuisine Clustering and Map Construction



2 readings expand


  1. Reading: Task 2 Overview
  2. Reading: Task 2 Rubric

Graded: Task 2 Submission

WEEK 3


Task 3 - Dish Recognition



2 readings expand


  1. Reading: Task 3 Overview
  2. Reading: Task 3 Rubric

Graded: Task 3 Report Submission

WEEK 4


Task 4 & 5 - Popular Dishes and Restaurant Recommendation



2 readings expand


  1. Reading: Task 4 and 5 Overview
  2. Reading: Task 4 and 5 Rubric

Graded: Task 4 and 5 Submission

WEEK 5


Task 6



2 readings expand


  1. Reading: Task 6 Overview
  2. Reading: Task 6 Rubric

Graded: Task 6 Report Submission

WEEK 6


Final Report



2 readings expand


  1. Reading: Final Report Instructions
  2. Reading: Final Report Rubric

Graded: Final Report Submission
There are no reviews yet.

Share your review

Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate $1.- to Stichting Edukans.

There are no frequently asked questions yet. Send an Email to info@springest.com