ACIT4530 Data Mining at Scale: Algorithms and Systems Emneplan

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 associationrule learning, anomaly detection, data clustering, visualizations, and extracting statistical features on the fly from large data streams. In this course, the student will also be exposed to different data mining systems including the landscape of MapReduce and the ecosystem it spawned, such as Spark and its contemporaries. With a focus on data mining applications, we will study some powerful numerical linear algebra methods.

It is an advantage to have some experience with the following subjects:

• Mathematical Analysis
• Basic programming, such as scripting
• Statistics, specifically probability theory

No formal requirements over and above the admission requirements.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• has a deep understanding of how data mining can be used to extract knowledge from data sets.
• has advanced knowledge of the different data mining algorithms.
• should be able to use data mining systems to mine data.

Skills

Upon successful completion of the course, the student:

• can design and implement data mining algorithms
• can deploy different data mining systems and configure them
• can utilize a specialized library for data mining

General competence

Upon successful completion of the course, the student:

• can analyse data mining solutions with regard to robustness and in relation to his/her intended tasks
• can explain how data mining can be used in different applications areas such as business analytics

This course is divided into two parts. The first part with focus on covering the principles of data mining and stream processing. Different seminars will be given on the different methodological aspects of data mining and stream processing as well as the programming paradigms and software tools that enable them.

The second part will focus on the students completing a programming project. The project can be chosen from a portfolio of available problems. The student will work in a group on the project and submit a final code-base with a report.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

The course description was approved 1 June 2016 by the Academic Affairs Committee and established by the Dean 9 June 2016 at the Faculty of Education and International Studies. Latest revision approved by the Academic Affairs Committee 11 May 2017. Minor revision made 20 December 2017.

The Faculty of Education and International Studies at the Oslo and OsloMet offers interdisciplinary courses in Development Studies and North-South relations, leading to a Bachelor's degree in Development Studies comprising 180 ECTS credits. The module in "Media and Development" is a course at the intermediate level, 4th semester in the BA programme. Fulfilled requirements and a passable grade entitle the student to 10 ECTS credits.

This interdisciplinary course seeks to combine perspectives from the social sciences and the humanities on media and development. Drawing on contributions from various disciplines the course is concerned with historical processes of media, their uses and the social consequences of media practices.

The language of instruction is English or Norwegian, depending on the language proficiencies of the student body. However, students may submit assignments in English, Norwegian, Swedish or Danish.

Upon successful completion of the course, the student should master these learning outcomes:

Knowledge

The student

• knows the main analytical approaches and key themes and terms in the study of media and development on a global scale.
• knows different perspectives on how media is connected to social change and development.

Skills

The student

• can reflect critically on various types of perspectives on the relationship between media and development.
• is able to build transferable analytical skills of media and development across the global South.

General competence

The student

• knows how to link media and development to cross-cultural issues.
• is able to apply this knowledge in new academic contexts.

The course is a full-time academic programme lasting four to six consecutive weeks, offered in the spring semester. The course consists of lectures and seminars with active student participation.

To qualify to sit for the final exam, students shall as a group task produce a podcast over a given part of the curriculum. Alternatively, If the student is unable to attend such a group, s/he must write an academic paper of 2000 words (+/-10 %) on a given topic. This will enable the student to engage with the course literature and critically reflect on a particular topic. Papers must be handed in digitally through OsloMet's Learning Management System and within the stipulated deadline. No individual supervision will be provided for this paper, but students will be able to work on their papers in course seminars.

The podcast/paper will be assessed as either "approved" or "not approved". Students who do not get the required pass, may revise their podcast or rewrite and resubmit their paper once within a given deadline. Students who due to illness or other documented reasons for legal absence fail to submit this coursework requirement with the set deadline, will be given a new submission deadline. In this case, the student must present the documents confirming his/her illness.

The final assessment of this module consists of a four-hour written exam.

New/postponed exam

In case of failed exam or legal absence, the student can apply for a new or postponed exam. New or postponed exam is offered within a reasonable time span following the regular exam. Submission and assessment of this will be in accordance with the conditions originally applicable. The student is responsible for applying to sit for a new or postponed exam within the deadlines stipulated by OsloMet and the Faculty of Education and International Studies.