Ever wonder how Netflix decides what movies to recommend for you? Or how Amazon recommends books? We can get a feel for how it works by building a simplified recommender of our own! For that purpose, you can enroll in Duke University Java Programming: Build a Recommendation System Course.

Duke University Online Course Highlights

4 weeks long
3-6 hours per week
Learn for FREE, Upgradable
Self-Paced
Taught by: Robert Duvall, Andrew D. Hilton, Owen Astrachan, Susan H. Rodger
View Course Syllabus

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TTC Course Analysis:

Following are the results of comprehensive analysis of “Java Programming: Build a Recommendation System” online course by our team of experts.

TTC Rating
110 Reviews

4.3

TakeThisCourse Sentiment Analysis Results:

In order to facilitate our learners with real user experience, we performed sentiment analysis and text mining techniques that generates following results:

TTC analyzed a total of 110 reviews for this online course.
The analysis indicates that around 85.5% reviews were positive while around 14.5% of reviews had negative sentiment.
Duke University online course received a total score of 4.3 out of 5, based on user opinions related to 4 effectiveness factors including content, engagement, quality practice and career benefits.

Java Programming: Build a Recommendation System Review

TTC Sentiment Analysis based on Learner Reviews

TTC Course Effectiveness:

Online Course Effectiveness Score (Learn More)
Content	Engagement	Practice	Career Benefit
4.5/5.0 ★★★★★ 22 Reviews	4.2/5.0 ★★★★☆ 64 Reviews	4.4/5.0 ★★★★☆ 24 Reviews	4.8/5.0 ★★★★★ 21 Reviews

Java Programming Build a Recommendation System Effectiveness Review

Online Course Details:

I n this capstone Java Programming: Build a Recommendation System, you will show off your problem solving and Java programming skills by creating recommender systems. You will work with data for movies, including ratings, but the principles involved can easily be adapted to books, restaurants, and more. You will write a program to answer questions about the data, including which items should be recommended to a user based on their ratings of several movies.

Given input files on users ratings and movie titles, you will be able to:

1. Read in and parse data into lists and maps;
2. Calculate average ratings;
3. Calculate how similar a given rater is to another user based on ratings; and
4. Recommend movies to a given user based on ratings.
5. Display recommended movies for a given user on a webpage.

SKILLS YOU WILL GAIN:

Data Structure
Interfaces
Software Design
Java Programming

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Java Programming: Build a Recommendation System Review