BIBV3310 Web Development Emneplan

This course deals with a variety of tasks related to the process of web development. It both expands on earlier courses that have covered user analysis, web technologies and the organization of data and digital documents, and introduces new topics. Among these are information architecture, structured data, practical skills in publishing tools, user testing and project management including the development of requirement specifications, prototypes and user testing.

None.

Knowledge

After completion of the course, the student has

• an understanding of the key characteristics of different types of web based services and solutions
• knowledge of the core issues in the development of different types of web based services and solutions
• knowledge about current trends, practices and tools

Skills attainment

After completion of the course, the student is able to

• plan a web development project
• use tools and standards to develop web based services and solutions
• procure web based services and solutions

The course consists of a combination of lectures, group work, labs and homework.

None.

Assessment of Web Development will be by two submissions - an individual term paper (approx. 10 pages) and a group-based project (a practical work related to a set of web development tasks). The group must submit a report (between 5-10 pages) describing their work and documenting the outcomes. The topic of the project must be approved by the tutors. Font: Arial / Calibri. Size: 12 point. Line spacing: 1.5

The term paper accounts for 60% of the grade, while the remaining 40% comes from the group-based project. The student must pass both parts to pass the course.

Students who have legitimate absence from the exam or have failed the regular examination are entitled to sit a new examination in the part(s) they have failed. If the project work fail, the whole group need to sit the new examination.

We are witnessing the era of Big Data where data is generated, collected, and processed at an unprecedented scale and data-driven decisions influence many aspects of modern life.

Data mining is the process of discovering patterns in large data sets involving methods in statistics and database systems. A large number of applications such IoT sensors generate large amounts of data streams. The necessity of data stream mining and learning from the data is increasingly becoming more prevalent and urgent.

Extracting knowledge from data sets requires not only computational power but also programming abstractions as well as analytical skills. In this course, the students will be exposed to the different approaches for data mining and stream processing such as association rules learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. The students will be exposed to concrete data mining and neural network architectures including deep learning models for handling large data streams such as convolutional neural networks, recurrent neural networks, autoencoders, transformers and attentions. In this course, the student will also be exposed to different data mining systems, working end-to-end pipelines including performance evaluation, detecting overfitting, underfitting, and data defects. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.

No formal requirements over and above the admission requirements.

Emne 2 bygger på emne 1. Temaa i emne 2 er nærare knyta til ferdigheitsdelen av norskfaget, slik som den vidare lese- og skriveopplæringa, produksjon av skriftlege, munnlege og samansette tekstar og vurdering og analyse av andrespråkstekster frå eigen praksis, i tillegg til samtidsdikting og samtidstekster i andre sjangrar.

Emne 2 ligg i vårsemesteret og har ein avsluttande semesteroppgåve som eksamensform. Målforma er nynorsk. Studentane må levere to arbeidskrav før dei kan gå opp til eksamen: eit fagnotat om vurdering av ein elevtekst skrive av elev med norsk som andrespråk (målform: bokmål) og eit fagnotat knyta til arbeidet ein digital læringsressurs.