FI2BCPD15-CC Data Analysis and Visualization with Python
FI2BCPD15-CC Data Analysis and Visualization with Python
- Course description
- Course codeFI2BCPD15-CC
- Program of studyData Analyst 1
- Credits15
- Course coordinatorMartins Macs
This course provides a comprehensive introduction to programming fundamentals and the practical application of data analysis using Python 3.x. Candidates will begin by learning foundational programming concepts, such as data types, operators, collections, objects, comprehensions, file input-output, and the use of libraries. Through hands-on experience, candidates will develop structured code and utilize integrated development environments (IDEs) like Jupyter Notebook to document their code, present outputs, and export reports in Markdown format.
Building on this foundation, the course advances into the use of software libraries to support data analysis. Candidates will work with powerful Python libraries such as NumPy, SciPy, and Pandas for programmatic data manipulation, cleaning, and correlation analysis. They will also learn to create visualizations programmatically using tools like seaborn or matplotlib. Python's ease of use and versatility make it an ideal language for beginners and professionals, and this course prepares candidates to integrate programming with data analysis, gaining the skills needed to solve real-world problems.
Vocational education at Noroff can expand career opportunities and lay lifelong learning foundations. Throughout the programme, students will familiarise themselves with key competencies relevant to industry employment.
Career opportunities
After graduation, the candidate may qualify for work within these areas:
- ...
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:
The candidate:
- has knowledge of concepts that are used in computational thinking to solve data analysis related problems using simple programs
- has knowledge of industry-relevant data tools that are available for use within the selected programming language
- has knowledge of theories, techniques and methods that used in external data manipulation libraries
- has knowledge of processes and techniques that are used in a selected programming language
- has knowledge of industry relevant tools which are able to export code examples and documentation into alternative forms such as Markdown
- is familiar with data preparation and how to pre-emptively identify and remove data errors using the appropriate technique
- has knowledge of concepts, techniques and methods that are used in external data visualisation libraries such as Matplotlib, and Seaborn
- has knowledge of concepts and processes that are used in relation to data access layers and the use of APIs
- has insight into own opportunities for further development in programming
- is familiar with the history of programming languages and fundamental programming traditions
The candidate:
- can explain vocational choices in programming fundamentals, such as control structures and objects to create iterative solutions to repetitive tasks
- can explain vocational choices of APIs to create access layers within databases to import data into their programs
- can reflect over insight into database integration with the selected programming environment and adjust it under supervision
- can reflect on own use of programming syntax and the use of the interactive interpreter and adjust it under supervision
- can reflect on own the use of alternative text editing syntax to descriptive dialog within coded reports and adjust it under supervision
- can find and refer to information and vocational material about scripting or coding with a programming language to develop diverse and robust programs
- can explain vocational choices of external data tools with data analytical methods to create robust data reports
- can explain vocational choices of external visualisation tools to programmatically create graphs and other visual indicators
- can explain vocational choices of corresponding tools given any data project outline
- can reflect on own use of key external data analytical tools to perform data analysis in Python and adjust it under supervision
- can find and refer to information about additional external Python data libraries and assess them to keep up to date with the latest analytical solutions
The candidate:
- can plan and carry out well-documented programs alone or as part of a group to solve real-world data-related problems in accordance with ethical requirements and principles
- can plan and carry out fast, powerful, small scripts with a programming language and in accordance with ethical requirements and principles
- can exchange points of view with other data analysts and programmers and participate in collaboratively discussions to create complete and well-documented programs
- can plan and carry out effective work methods to streamline data analysis pipelines using programmatic problem-solving techniques alone or as part of a group and in accordance with ethical requirements and principles
- can exchange points of view with others with a background in the data analysis field and participate in discussions about programmatic problem-solving questions and the newest industry-relevant external Python tools
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. 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.
In this course, the following teaching and learning methods can be applied, but are not limited to:
- Lecture: Educator-led presentations or activities providing knowledge, skills, or general competencies in the subject area.
- Group work: Collaborative activities where students work together to solve problems or complete tasks.
- Tutoring: One-on-one or small group sessions with an instructor for personalized guidance and support.
- Student presentations: Opportunities for students to demonstrate their understanding of course material by presenting to peers.
- Online lessons: Digital content delivered via an online learning platform.
- Guidance: Individualized advice and direction from instructors to support students in their learning journey.
- Workshops: Practical sessions focused on hands-on application of theoretical concepts or skills.
- Self-study: Independent study where students engage with course material on their own without any teacher support.
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.
A single 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.
A single course may also require students to deliver one or more compulsory module assignments during a course. This is to follow up and support the students’ learning path.
| Form of assessment | Grading scale | Grouping | Duration of assessment |
|---|---|---|---|
Course Assignment | Pass / Fail | Individual | 1 Week(s) |
Teaching materials, reading lists, and essential resources will be shared in the learning platform and software user manuals where applicable.
Information about equipment requirements is available here: 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:
- By upper secondary education (videregående skole)
- Higher education entrance qualification from Norway or abroad
- 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. Documented relevant craft certificate (fag og svennebrev)
- Prior learning and work experience
More information about admission requirements is available on our webpage under Admission Requirements.