Data Analyst 2

Data Analyst 2

  • Study facts
    • Prog. Code
      PDAN
    • NQF Level
      5.2
    • Credits
      120
    • Valid from
      H24
    • Dated
      14.08.2024
    • Version
      1.1
    • Study mode
      Full-time, Part-time
    • Program manager
      Bertram Haskins
About the programme

Data analysts have a quintessential portfolio in every modern company ecology. Their ability to guide business leaders to make informed decisions using relevant and up-to-date information based on real-world data makes 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 developing the data analyst aptitude. Candidates will acquire first-hand training in fundamental data identification skills and 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. Candidates will also learn how to solve data problems using industry-relevant statistical tools programmatically. Supporting systems such as native tools ingrained into programming languages and cloud platforms will be mastered. Advanced data concepts are introduced throughout this program, allowing candidates to construct real-world solutions to the broad area of problems. Industry requirements have outlined the need for candidates to expand their understanding of business intelligence concepts, data sustainability, and GDPR legal compliance.

Data analysis techniques will be practised 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 an elementary introduction to R and Python, data storage, analytics and visualisation tools.

The programme is aimed towards people interested in real-world data and how hard numbers and heuristics can be used to shape the decision-making process. The course content harmonises 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 can use data from other fields of knowledge and learn how to leverage results better information for non-analyst consumption, and report findings elegantly.

Learning Environment

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. Lectures and live-stream sessions are not a part of the delivery model online but may be given as an add-on. 

Campus 
As part of the campus community, students will have access to on-site teachers, guest lecturers, and other students during their learning journey. Students on campus study in modern working environments and have access to equipment used for practical training.

After graduation

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 one of our partner universities. 

Career opportunities

After graduation, the candidate may qualify for work within these areas:

  • Financial Analyst
  • Marketing Analyst
  • Logistics Analyst
  • General data analyst
  • Technical analyst
  • Information Scientist
  • Operational Management
Learning Outcome

The Norwegian Qualifications Framework for lifelong learning (NQF) defines the levels of qualifications in the Norwegian educational system. These levels describe what a learner knows, understands, and can do 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:

Knowledge

The candidate:

  • has knowledge of concepts and theories that are used in the field of data analysis
  • has knowledge of the processes and tools that are used for data analysis
  • has knowledge of databases, cloud services and native cloud tools that are used in the field of data analysis
  • has knowledge of programming and programmatic data analysis
  • 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
  • has knowledge of industry-relevant mutually exclusive tools that are used in the field data analysis
  • has knowledge of industry-relevant mutually exclusive tools that are used in the field data analysis
  • has knowledge of essential concepts and theories that are used in data science and engineering in relation to Big Data and data analysis
  • has knowledge of dashboard theory, universal design principles and interactive dashboards development
  • has insight into regulations, the data analysis lifecycle and quantitative versus qualitative data
  • has insight into own opportunities for development in the field of data analysis
  • can assess own work within data analysis in relation to relevant regulations and guidelines for GDPR, data maintenance and critical data thinking
  • is familiar with the history, traditions, distinctive nature and place in the society of the data analysis discipline
Skills

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 master tools and techniques to generate and visualise data through reports and info- graphs
  • can apply knowledge of suitable data analysis use-cases to problems within a current project
  • can explain vocational choices in the field of data analysis
  • can explain vocational choices of tools, methods and techniques for data analysis
  • can reflect over own vocational practice into the field of data analysis and adjust it under supervision
  • can reflect on their own choices of relevant data analysis tools and work methods and adjust under supervision
  • can find information about data analysis techniques and methodologies that are relevant to projects
  • can find and refer to information and vocational material and assess its relevance to data analysis
  • 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
  • can find and interact with data from large data sources such as on-premises databases and cloud-based systems
  • can find applicable data models for data sets during a project planning phase
General Competence

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 plan and carry out data analysis tasks and projects alone or as part of a group and in accordance with ethical requirements of data maintenance and GDPR principles and practices
  • can exchange points of view with others with a background in the data analysis discipline and participate in discussions about the development of good practice
  • can contribute to organisational quality assurance, streamlining and optimisation through data analysis practices
  • can contribute to solving practical problems relating to the data lifecycle through computational thinking techniques
  • can contribute to data safety by considering security measures during each phase of any data analysis project
  • can develop products of relevance to data analysis and optimize his / her own work methods
Course Overview
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
FI2BCDC75 Databases and Cloud Services 3 4 168 7.5
FI2BCPP10 Programming Fundamentals 3 6 252 10
FI2BCPA05 Programmatic Data Analysis 3 3 126 5
FI2BCP175 Semester Project 2 3 4 168 7.5
FI2BCIT75 Industry Tools 4 5 210 7.5
FI2BCCT05 Critical Data Thinking 4 4 168 5
FI2BCBD05 Big Data and Advanced Topics 4 4 168 5
FI2BCID05 Interactive Dashboards 4 3 126 5
FI2BCP275 Exam Project 2 4 6 126 7.5
Total 78 3150 120

 

Course Models
Data Analyst 2
Teaching and Learning

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.

Work Requirements and Assessment

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.

Equipment Requirements

Information about equipment requirements is available on our webpage: Programme information.

Online students are required to purchase and maintain their equipment.

Admission requirements

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.