KIUA2003 High Performance Computing with Big Data (Big Data Analysis)
- Course codeKIUA2003
- Number of credits10
- Teaching semester2026 Autumn
- Language of instructionNorwegian/English
- CampusHamar
- Required prerequisite knowledge
Recommended: KIUA2002 Computer Vision
The course will equip students with a deep understanding of big data characteristics, prominent tools and analytical algorithms. Students will gain practical skills in pre-processing, use of Hadoop and Spark, optimising performance and efficiently visualising and communicating big data insights. Through a combination of theory and practical experience, students will gain an understanding of the unique characteristics of big data, the tools and technologies used to process big data and the ethical considerations involved. Upon completion of the course, students will be able to effectively analyse and derive meaningful insights from large and complex data sets.
Learning Outcome
Upon successfully passing the course, students will have achieved the following learning outcomes:
The student will have
- knowledge of the properties of big data, including volume, variation, speed and validity, and is able to understand the challenges and opportunities associated with big data storage, processing, analysis and visualisation
- knowledge of prominent big data tools and technologies, such as Hadoop, Spark, NoSQL databases and distributed file systems, with an understanding of the functionality and use cases of such tools for the processing and analysis of big data
- knowledge of various big data analytics algorithms, including monitored learning algorithms, unmonitored learning algorithms and deep learning algorithms
The student will be able to
- pre-process and cleanse big data, including handling missing data, extraneous data and noise, in order to manage techniques for data integration, transformation, normalisation, aggregation and feature development
- use Hadoop and Spark frameworks for scalable big data processing in order to develop their skills in writing MapReduce programs, working on Spark RDDs, Spark SQL, Spark Streaming and Spark MLib
- use techniques to optimise the performance of workflows for big data analysis, including data partitioning, inventory and parallel data processing, for performance adjustments and optimisation of big data processing tasks
- visualise big data insights and communicate these effectively to stakeholders and will learn to create interactive visualisations, develop dashboards and use visual analysis tools to communicate meaningful insights from big data analyses
The student will be able to
- enhance their critical thinking using high-performance computing techniques to solve complex big data analysis problems by evaluating different approaches, algorithms and technologies and making informed decisions in the design and implementation of big data analytics solutions
- work efficiently as part of a team, share responsibilities and utilise the different skills and perspectives of team members to tackle the challenges of big data analytics
- demonstrate awareness of ethical considerations and responsible practices in big data analysis when it comes to understanding the implications of data protection, the importance of protection and presence of bias in order to incorporate ethical principles in the design and implementation of big data analytics solutions
- demonstrate a research-oriented mindset and the ability to explore and contribute to advances in high-performance data processing and big data analysis to contribute to the exploration of new trends, propose innovative solutions and develop new techniques and algorithms
The course comprises a combination of lectures, practical exercises, independent study and academic supervision.
- 2 individual assignments
Compulsory coursework requirements that have been passed are valid for 12 months only. Students wishing to take examinations after 12 months must pass the compulsory coursework requirements again in connection with the next scheduled delivery of the course.
| Form of assessment | Grading scale | Grouping | Duration of assessment | Support materials | Proportion | Comment |
|---|---|---|---|---|---|---|
Written assignment | ECTS - A-F | Individual | 100 |
- 1 individual project-based assignment
The assignment is assessed using a grading scale from A-F, where E is the lowest passing grade.
Students are able to choose which language to use for their examination. The available options are Norwegian Bokmål, Nynorsk and English.
Permitted aids:
- Literature
- All printed and written resources
- Any use of AI-generated text and content must be clarified with the lecturer, clearly labelled and academically justified in the submission