English | Size: 790 MB
Genre: eLearning
Master Distributed Databases and Cloud Analytics. Gain advanced skills in distributed database systems, cloud computing, data reliability, and machine learning to design and optimize large-scale data solutions.
What you’ll learn
Master database systems, including transaction management, query optimization, and data warehousing principles for large-scale environments.
Develop proficiency in cloud computing concepts, using Hadoop and Accumulo for data processing and storage in distributed systems.
Apply machine learning techniques such as clustering and collaborative filtering to analyze big data and enhance system reliability.
Specialization – 3 course series
The specialization “Large-Scale Database Systems” is intended for post-graduate students seeking to develop advanced skills in distributed database systems, cloud computing, and machine learning. Through three comprehensive courses, you will dive into key topics such as distributed database architecture, transaction management, concurrency control, query optimization, and data reliability protocols, equipping you to handle complex data environments. You will also gain hands-on experience with cloud computing concepts, including Hadoop and the MapReduce framework, essential for large-scale data processing. In addition, you’ll explore machine learning applications such as collaborative filtering, clustering, and classification techniques, learning to optimize these models for scalable analysis in distributed systems.
By the end of the specialization, you will have developed an understanding of optimizing large-scale data warehouses and implementing machine learning algorithms for scalable analysis. This specialization will prepare you to design and optimize high-performance, fault-tolerant data solutions, making you well-equipped to work with large-scale distributed systems in industries like data analytics, cloud services, and machine learning development.
Applied Learning Project
Learners will engage in scenarios that simulate real-world challenges in managing and optimizing distributed database systems while incorporating self-reflective readings. Through self-reflective readings, learners will connect the technical concepts of distributed database theory, query optimization, and machine learning integration to their own professional goals and experiences. They will reflect on the implications of their decisions in designing fault-tolerant systems, improving scalability, and balancing performance with security in real-world scenarios.
This holistic approach ensures that learners develop a reflective mindset for tackling the complexities of distributed systems in data-driven industries like cloud computing, machine learning, and large-scale data analytics.
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