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FREDERICK UNIVERSITY
Big Data Foundations and Infrastructure
Christos Markides
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Course Information
Thematic Area
Big Data as a Platform
Study Format
Online
Course Type
Microcredential
Date
TBA
Sync Hours
5-10
Async Hours
40-45
Sectors
Information and Communication Technologies (ICT); Financial Services and Insurance; Retail and E-commerce; Healthcare and Life Sciences; Manufacturing
EQF Level
4
SDGs
SDG 4 – Quality Education; SDG 9 – Industry, Innovation and Infrastructure
Course Description
This course introduces fundamental Big Data concepts, technologies, and hands-on skills: characteristics of Big Data; the role of Big Data in digital transformation; cloud concepts; and foundational tools including Hadoop and Spark. Learners practice basic processing, visualisation, and analysis with Python while reflecting on ethical considerations of large-scale data.
Assessment
Self-Assessment Quizzes; Practical Lab Exercises; Assignments; Essays/Reports; Knowledge Checks; Final Exam (Online); Case Studies; Projects
Study Methods
Lectures; Videos; Hands on Labs; Practical Labs; Case Studies; Live Code Demos; Code Reviews; Literature Reading
Learning Outcomes
Define Big Data and describe 4Vs; identify Hadoop and Spark components; navigate HDFS and use Spark RDDs; use Python/DataFrames for basic operations; examine Big Data datasets with Python; explain Big Data’s evolution and relevance.
Hard Skills
Big Data concepts and terminology; HDFS; Spark and RDDs; Python and PySpark; Basic ETL
Soft Skills
Problem-solving; Digital literacy; Communication; Self-management
Related Courses
Microcredential
Microcredential
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