Data Analyst 1
Data Analyst 1
- Study facts
- Prog. CodePDAN1
- NQF Level5.1
- Credits60
- Valid fromH24
- Dated14.08.2024
- Version1.4
- Study modeFull-time, Part-time
- Program manager
Data analysts have a quintessential portfolio in every modern company ecology. Their ability to guide business leaders to making informed decisions using relevant and up to date information, based on real world data, make them a highly desired addition to every managerial team. Effective data analysis can isolate workflow bottlenecks, reduce operational costs, solve overarching problems, and identify inefficient processes.
This programme incorporates theoretical knowledge, practical skills, and technical competency, to create a balanced learning experience crucial for the development of the data analyst aptitude. Candidates will acquire first hand training in fundamental data identification skills along with accompanying theory. The course will use industry standard technologies such as Microsoft Excel, Google Spreadsheets, and related tools to construct a thorough understanding of known practices. Once the foundation is laid, candidates will be engaged with how data is collected, stored, organized, analysed, interpreted, visualized, and reported.
Data analysis techniques will be practiced using proxy data sets which will immediately engage the candidate’s ability to address business-oriented problem areas such as operational management, sales, finances, marketing, and even human resources. Finally, the course will cover essential data analysis, visualization, and reporting skills using software prevalent in most. business environments.
The programme is aimed towards people that are interested in real world data and how hard numbers and heuristics can be used to shape the decision-making process. The course content harmonizes with individuals who wish to learn the basics from scratch, as well as established professionals who wish to update their skills to modern standards. Candidates will be able to use data from other fields of knowledge and learn how to better leverage results, visualize information for non-analyst consumption, and report findings elegantly.
The digital classroom
All students at Noroff have access to a digital classroom, referred to as the learning platform. Here the student can access relevant academic and practical information about the study programme. The learning platform also contains learning content, activities, delivery deadlines, work requirements and assessments for every course.
Online
Online studies are flexible since students can study from anywhere and at their own pace according to the academic progression and scheduled deadlines. Students access their learning material for each course through the learning platform, and discussion forums are used for communication between fellow students and teachers.
Campus
As part of the campus community, students will have access to on-site teachers, guest lecturers, and other students during their learning journey. Campus students study in modern working environments and have access to professional equipment used for practical training.
Vocational education at Noroff can expand career opportunities and lay lifelong learning foundations. Throughout the programme, students will familiarise themselves with key competencies relevant for industry employment. Students who graduate with a higher professional degree may be eligible to enter a bachelor ́s degree at one of our partner universities.
Career opportunities
After graduation, the candidate is qualified for employment as:
- Financial Analyst
- Marketing Analyst
- Logistics Analyst
- Information Scientist
- Operational Management
The Norwegian Qualifications Framework for lifelong learning (NQF) defines the levels of qualifications in the Norwegian educational system. These levels as a result of a learning process. Categories in NQF are defined as:
Knowledge: Understanding theories, facts, principles, procedures in the discipline, subject area and/or occupation.
Skills: Ability to utilise knowledge to solve problems or tasks (cognitive, practical, creative and communication skills).
General Competence: Ability to independently utilise knowledge and skills in different situations.
After graduation from this programme, students have acquired the following learning outcomes:
The candidate:
- has knowledge of data collection, cleaning, organization, and storage of data used in a spreadsheet environment
- has knowledge of the processes and tools that are used for data analysis
- has insight into regulations, the data analysis lifecycle and quantitative versus qualitative data
- has knowledge of processes and tools that are used for data visualization
- has knowledge of problem identification methodologies, processes and tools that are used for problem solving and data error discovery
- has knowledge of conclusive report writing methodologies that are used to communicate results clearly and concisely
- has knowledge of the data analysis field and is familiar with real-world situations to guide decision-making
- can update his/her own knowledge related to the field of data analysis
- understands the importance of data analysis discipline in a societal and value-creation perspective
The candidate:
- can apply knowledge of data model results to business problems
- can apply knowledge of data collection and cleaning from various sources to secure storage and optimize maintenance-masters tools
- masters relevant tools, techniques and material used in data analysis and presentation of results
- can masters tools and techniques to generate and visualise data through reports and info-graphs
- can find information about data analysis techniques and methodologies that are relevant for projects
- can apply knowledge of suitable data analysis use-cases to problems within a current project
- can study workplace environments and identify issues through data analysis and what measures needs to be implemented based on results
- can study a project brief and identify workflow issues and what measures are needed to deliver insight into a project
The candidate:
• understands the ethical principles that apply to sourced, stored, and used data
• has developed an ethical attitude as a responsible data analyst
• can carry out design processes, data models, and applicable techniques based on a project specification
• can build relations with his/her peers and external data specialists and business intelligence agents
• can develop products of relevance to data analysis and optimize his / her own work methods
| Course code | Course name | Semester | Weeks | Hours | Credits |
|---|---|---|---|---|---|
| FI1BBDF05 | Data Analysis Fundamentals | 1 | 3 | 126 | 5 |
| FI1BBSF05 | Spreadsheet Fundamentals | 1 | 3 | 126 | 5 |
| FI1BBDD75 | Data Driven Decision-Making | 1 | 4 | 168 | 7.5 |
| FI1BBST05 | Statistical Tools | 1 | 3 | 126 | 5 |
| FI1BBP175 | Semester Project 1 | 1 | 4 | 168 | 7.5 |
| FI1BBEO10 | Evaluation of Outcomes | 2 | 8 | 336 | 10 |
| FI1BBDV75 | Data Visualisation | 2 | 5 | 210 | 7.5 |
| FI1BBAR05 | Analysis Reporting | 2 | 3 | 126 | 5 |
| FI1BBP275 | Exam Project 1 | 2 | 6 | 252 | 7.5 |
| Total | 39 | 1638 | 60 | ||
Noroff offers an engaging and student-active learning experience that prepares candidates for professional working life through unique and industry-relevant teaching and learning activities governed by the current learning outcomes. Teaching and learning engage students in the learning process by promoting a holistic understanding of the different issues and challenges relevant to the subject areas. By fostering critical thinking, creativity, collaboration, and communication, students will develop lifelong learning skills.
Activities can vary for campus and online delivery and are composed of theoretical and practical approaches, providing students with the best possible outcome for each course. Noroff distinguishes between teacher and student-led activities. Both are equally important and tailored to each course’s educational approach. Teaching and Learning activities used in the courses are outlined in the course descriptions.
For all online studies, English is the primary language for teaching. English can also be used as the teaching language on some campuses.
Assessment impacts the student’s learning significantly and concludes if the student has achieved the intended learning outcome and, if so, at what level. Assessments include summative and formative methods depending on the content of the learning outcome of each course.
A course usually consists of one or more work requirements. The most common is compulsory course assignments that assess the acquired competencies outlined in the course learning outcomes. Course assignments are assessed as Passed/Failed or graded from A to F, after which verbal or written feedback is provided. Tests can also evaluate students’ achievements and are usually used in combination with compulsory assignments.
Online studies may also require students to deliver one or more compulsory module assignments during a course. This is to follow up and support the online students’ learning path. Module assignments can be used as learning activities for campus students.
Work requirements and assessment methods for each course are described in the course descriptions.
Information about equipment requirements is available on our webpage: Programme information.
Online students are required to purchase and maintain their equipment.
There are three ways to meet the admission criteria and be enrolled as a student:
1. By upper secondary education (videregående skole)
- Higher education entrance qualification from Norway or abroad
2. By Norwegian vocational upper secondary education
- Documented vocational qualifications diploma (yrkeskompetanse) within Dataelektronikerfaget, Automatiseringsfaget, IT-driftsfaget og IT-utviklingsfaget etc.
- Documented craft certificate (fag og svennebrev) within: Automatiker, dataelektroniker, IT-driftstekniker, IT-utvikler etc.
3. Prior learning and work experience
More information about admission requirements is available on our webpage under Admission Requirements.