Data Science Capstone: NYC Housing Analysis
Apply advanced data science and machine learning techniques to analyze New York City's 311 housing complaints data and build predictive models.
Apply advanced data science and machine learning techniques to analyze New York City's 311 housing complaints data and build predictive models.
This comprehensive capstone project provides hands-on experience in applying data science and machine learning concepts to real-world problems. Students work with New York City's 311 complaint system data, focusing on housing-related issues. The project encompasses complete data science workflow including data ingestion, exploration, visualization, feature engineering, and predictive modeling. Participants develop a portfolio-worthy project demonstrating their ability to derive actionable insights from complex datasets. This practical application of skills helps showcase professional capabilities to potential employers.
4.4
(131 ratings)
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What you'll learn
Apply comprehensive data science techniques to real-world housing data
Develop advanced data visualization and analysis skills using Python
Master feature engineering for predictive modeling
Build and validate machine learning models for practical applications
Create actionable insights from complex datasets
Develop a portfolio-ready project showcasing data science expertise
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
Shareable certificate
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Module Description
This capstone project provides a comprehensive, real-world application of data science and machine learning skills. Students work with actual New York City 311 complaint data to analyze housing-related issues. The project covers the entire data science workflow, from initial data exploration to final model deployment. Participants learn to handle real-world data challenges, perform advanced analytics, and create meaningful visualizations. The course emphasizes practical application and portfolio development, helping students demonstrate their expertise to potential employers.
Fee Structure
Instructors
AI and Machine Learning Expert at IBM Canada
Yan Luo serves as a Data Scientist and Developer at IBM Canada, where he applies his expertise in machine learning and artificial intelligence to develop innovative cognitive applications across diverse domains including software repository mining, personalized health management, wireless networks, and digital banking. After earning his Ph.D. in Machine Learning from the University of Western Ontario, he has contributed significantly to technical education through developing and teaching multiple data science courses, including Applied Data Science Capstone, Machine Learning Capstone, and Introduction to R Programming for Data Science. His work focuses on practical applications of AI and cognitive computing, bridging the gap between theoretical machine learning concepts and real-world business solutions.
Pioneering Data Scientist Bridging AI Research and Education
Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.
Testimonials
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4.4 course rating
131 ratings
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