This six-week long Capstone 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 Capstone 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 Capstone 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:
Opinion visualization: explore and visualize the review content to understand what people have said in those reviews. Cuisine map construction: mine the data set to understand the landscape of different types of cuisines and their similarities. Discovery of popular dishes for a cuisine: mine the data set to discover the common/popular dishes of a particular cuisine. 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 Capstone.
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, and the top 20 reports, as evaluated by peer review, will also be judged by an Industry Expert Committee who will select 10 of the reports as the Best Capstone Projects. The Industry Expert Committee is composed of multiple relevant industry experts representing different companies. For further information, see the Best Capstone Project Award page.
###Course Goals and Objectives By the end the course, you will be able to:
Explain the basic process of generating useful knowledge from a raw data set. Analyze a large data set with both structured attributes and unstructured text. Combine different data mining algorithms to accomplish a complex data mining task or improve the effectiveness of data mining. Evaluate data mining algorithms quantitatively with a test collection. Choose appropriate techniques to effectively visualize results from data mining algorithms. Write a report to summarize a data mining task and results, including explanation of the techniques used and discussion of the results.