UC3BDA10 Big Data Analysis
UC3BDA10 Big Data Analysis
- Course description
- NQF LevelBachelor's degree (Level 6 1. Cycle)
- Area of StudyComputing
- Program of StudyApplied Data Science
- ECTS10
- CampusKristiansand, Online, Oslo
- Course LeaderSeifedine Kadry
Language of Instruction and assessment: English
May be offered on Campus and Online.
May be offered as a separate course.
Included in the following bachelor's degrees:
- Applied Data Science
The course aims to provide students a thorough overview of Big Data Analytics and how to apply this to authentic data sets. The student will learn how to translate business objectives into computational problems and apply Big Data analytics techniques to mine large structured and unstructured data sets in order to discover hidden patterns and unknown relationships, and gain insight into previously unknown information within data in order to make smart data-driven decisions.
The student has knowledge of
| K1 | historical and emerging research and developments in the field of data analytics, specifically with regard to big data. |
|---|---|
| K2 | methods for data-driven problem decomposition and problem-solving. |
| K3 | how to apply a variety of appropriate tools and techniques for the analysis of large data sets. |
| K4 | legal and ethical challenges to big data analytics. |
The student gain skills in
| S1 | location, procurement and manipulation of data sets to solve specific data-driven problems. |
|---|---|
| S2 | ability to select, apply and critically evaluate appropriate tools and techniques for Big Data analysis. |
| S3 | interpretation and presentation of the results of data analysis in order to shed light on data-driven problems for relevant stakeholders. |
The student can demonstrate
| G1 | critical reflection on the usage and efficacy of a variety of data analytics tools and techniques. |
|---|---|
| G2 | identifying and understanding relevant legal and ethical issues relating to big data analytics. |
| G3 | structuring and implementing approaches to solving data-driven problems using big data analytics. |
| G4 | appropriately communicating data analytics processes, theories and ideas, and the results obtained from the application of appropriate analytics tools and techniques. |
- Introduction to Big Data Analysis
- Application of Tools and Techniques to Big Data
- Data Analytics Programming
- Analysis and Exploring Data
- The Social Web
- Further Issues in Big Data Analytics
- Teaching will be based on a hybrid-flexible approach. Instructor-led face-to-face learning is combined with online learning in a flexible course structure that gives students the option of attending sessions in the classroom, participating online, or doing both.
- All activities require active student participation in their own learning.
- Learning delivery methods and available resources will be selected to ensure constructive alignment with course content, learning outcomes and assessment criteria.
- Students will be taught using a mixture of guidance, self-study, and lecture material. Topics will be introduced in a series of weekly lectures. The guidance sessions will be directed practical exercises and reading in which students can explore topics with support from a teacher. This material will also require students to self-manage their time to ensure tasks are completed and the theory is fully understood. This will allow the students to fully engage with lectures and with their peers.
- Learning resources are available in the LMS and include, but is not limited to:
- literature and online reading material (essential and recommended)
- streams, recordings and other digital resources, where applicable
- video conferencing and communication platforms, if applicable
- tools, software and libraries, where applicable
- Students must have access to an internet connection, and suitable hardware.
- Accessing live streams and virtual laboratories requires a minimum broadband connection of 2Mbps (4Mbps recommended).
- Students working on their own laptop/computer are required to acquire appropriate communications software, e.g., webcam, microphone, headphones.
The reading list for this course and any additional electronic resources will be provided in the LMS.
| Activity | Duration |
|---|---|
Teacher-led activity | 24 |
Teacher-supported work | 48 |
Self-study | 178 |
This course has two (2) exams contributing towards the overall and final grade of the course.
All exams must be assessed as passed to receive the final Course Grade.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Online Exam | A-F | ||
Online Exam | A-F |