Big Data Capstone: Applied Data Science Project
Apply advanced data science techniques to real-world projects while addressing ethical concerns and developing professional communication skills.
Apply advanced data science techniques to real-world projects while addressing ethical concerns and developing professional communication skills.
In this advanced capstone project, students apply their comprehensive big data knowledge to a medium-scale data science initiative. Working with real-world datasets, participants plan and execute substantial projects independently, demonstrating initiative and accountability. The course emphasizes ethical considerations in data science, requiring analysis of ethical frameworks for data selection and management. Students enhance their professional skills through online collaboration and deliver detailed written presentations of their project design, methodologies, and outcomes. The program integrates practical application with theoretical understanding, preparing students for real-world data science challenges.
Instructors:
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What you'll learn
Plan and execute complex data science projects independently
Apply advanced data analysis techniques to real-world datasets
Evaluate and address ethical concerns in data science projects
Develop professional communication and presentation skills
Master data cleaning and preprocessing methodologies
Implement regression and classification models effectively
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams, Timed proctored exam
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 4 modules in this course
This capstone project integrates advanced big data concepts with practical application. Students work on real-world datasets, applying data science techniques while considering ethical implications. The course covers data cleaning, regression analysis, classification models, and feature selection. Emphasis is placed on independent project planning, execution, and professional communication. Students learn to evaluate and select appropriate data science methodologies, manage ethical concerns, and present their findings effectively using collaborative technologies.
Dataset overview, data selection and ethics
Module 1
Exam
Module 2
Project Task 1: Data cleaning and Regression
Module 3
Project Task 2: Classification
Module 4
Fee Structure
Instructors
2 Courses
A Distinguished Leader in Computer Science Education and Network Systems
Nick Falkner serves as Associate Professor in the School of Computer Science at the University of Adelaide, where he has established himself as an award-winning educator and researcher in computer science education and network systems. After earning his PhD, he has built an impressive career combining educational innovation with technical research in network topology, blockchain technologies, and the Internet of Things. His research spans multiple areas including information management, network security, privacy preservation, and educational strategies focused on student motivation and retention. As a recognized leader in learning and teaching, he pioneered new approaches to course delivery and assessment while conducting groundbreaking research into social networks' role in forming strong learning communities. His work has garnered over 4,500 citations, with significant contributions to the Internet Topology Zoo and puzzle-based learning approaches. Through his leadership in computer science education, he continues to develop innovative teaching methods that emphasize time management, motivation, and supportive learning environments while maintaining active research in network systems and information management.
2 Courses
A Distinguished Expert in Computer Science Education and Big Data Analytics
Gavin Meredith serves as Research Associate in the School of Computer Science at the University of Adelaide, where he has established himself as a key contributor to the Big Data MicroMasters program. His teaching portfolio spans foundational computer science courses including Introduction to Programming, Object Oriented Programming, Algorithm Design and Data Structures, and Problem Solving and Software Development. As an instructor in the Big Data MicroMasters program, he helps students develop skills in data analysis, programming, and computational thinking. Through his role as both researcher and educator, he continues to advance computer science education while maintaining active involvement in course development and student mentoring in the School of Computer Science.
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