Big Data Fundamentals
Learn essential big data concepts, including data mining, MapReduce, stream processing, and PageRank algorithms for organizational data analysis.
Learn essential big data concepts, including data mining, MapReduce, stream processing, and PageRank algorithms for organizational data analysis.
This comprehensive course explores how organizations leverage big data for effective decision-making. Students learn fundamental techniques in data mining, stream processing, and algorithm design using MapReduce for scalable data processing across Hadoop clusters. The curriculum covers key topics including web search optimization, online advertising systems, and the challenges of analyzing massive datasets. Through practical applications and real-world examples, participants gain expertise in implementing PageRank algorithms, understanding data stream analysis, and applying big data methods in industry contexts.
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What you'll learn
Master MapReduce programming for large-scale data processing
Implement PageRank algorithms for web search optimization
Design algorithms for stream processing and data mining
Analyze social networks and implement clustering techniques
Develop recommendation systems and online advertising solutions
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, Exams
Access on Mobile, Tablet, Desktop
Limited Access access
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Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 10 modules in this course
This course provides a thorough understanding of big data fundamentals and their organizational applications. Topics include the four V's of Big Data, web and social network analysis, clustering techniques, Google PageRank, MapReduce computing, document similarity analysis, recommendation systems, and data stream mining. Students learn both theoretical concepts and practical implementations through various real-world applications.
The basics of working with big data
Module 1
Web and social networks
Module 2
Clustering big data
Module 3
Google web search
Module 4
Computing similar documents in big data
Module 6
Products frequently bought together in stores
Module 7
Movie and music recommendations
Module 8
Google's AdWords System
Module 9
Mining rapidly arriving data streams
Module 10
Fee Structure
Instructors
1 Course
A Distinguished Leader in Evolutionary Computation and Optimization
Frank Neumann serves as Professor and leader of the Optimisation and Logistics group at the University of Adelaide, where he has established himself as a pioneering researcher in evolutionary computation and AI-based optimization. His position is funded by the Australian Research Council through a Future Fellowship, focusing on optimization methods for problems with stochastic constraints. As an internationally recognized scholar with over 11,000 citations, his research spans theoretical aspects of evolutionary computation and practical applications in cybersecurity, renewable energy, logistics, and mining. His leadership in the field includes serving as general chair of ACM GECCO 2016 and co-organizing ACM FOGA 2013 in Adelaide, while maintaining roles as Associate Editor for "Evolutionary Computation" and ACM Transactions on Evolutionary Learning and Optimization. His approach to optimization problems emphasizes diversity in solution sets, developing innovative algorithms that can produce multiple high-quality solutions rather than single outcomes. Through his work at Adelaide's School of Computer Science, he continues to advance the field of evolutionary computation while supervising PhD projects in AI-based optimization, though notably taking a strong stance against "novel" metaphor-based algorithms that he considers detrimental to scientific progress.
1 Course
A Distinguished Researcher in Bio-Inspired Computing and Dynamic Optimization
Vahid Roostapour completed his PhD in Computer Science at the University of Adelaide under the supervision of Professor Frank Neumann, focusing on bio-inspired computing for complex and dynamic constrained problems. His doctoral research made significant contributions to understanding evolutionary algorithms and ant colony optimization, particularly in addressing problems with dynamically changing constraints. His thesis work spans both static and dynamic combinatorial problems, including innovative research on minimum spanning tree problems, packing while travelling problems, and dynamic knapsack problems. Through his research, he demonstrated the advantages of evolutionary algorithms over traditional approaches in solving complex optimization problems, particularly in environments with high-frequency changes. His work has earned recognition in the field of evolutionary computation, with important contributions to theoretical and empirical understanding of bio-inspired algorithms. Beyond his research, he has been involved in teaching within the Mining Big Data course at Adelaide, combining his expertise in optimization with practical applications in data science.
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