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Data Analytics

Data Analytics

Code: RDI2HM102
Scope: 5 ECTS (135 h)
Timing: 1st or 2nd semester
Language: English
Curriculum: Master Curriculum

Starting level and linkage with other courses

No prerequisites.

Replacements

No replacements.

Learning objectives and assessment

Passed courses are assessed on a scale of 1 to 5. The assessment criteria are presented for grades 1, 3 and 5.

Grade 1
The student understands the difference between structured and unstructured data.  She/he understands the relevance of data in solving the research and development tasks.  The student can choose and use methods for data analysis and at least one related tool for reliability and relevance assessment as well as prepare a visual report presenting the results. She/he can choose a suitable channel for the dissemination of the results and distributing the report.

Grade 3
The student can apply different structured and unstructured data in research and development work.  She/he can assess the relevance of data collected for solving the research and development tasks.  The student can choose and use methods for data analysis and at least one related tool for reliability and relevance assessment as well as prepare a visual report presenting the results. She/he can choose suitable channels for the dissemination of the results and distributing the report. 

Grade 5
The student can professionally apply different structured and unstructured data in research and development work.  She/he can extensively assess the relevance of data collected for solving the research and development task. The student can fluently use different methods of data analysis and tools for reliability and relevance assessment. She/he can also prepare an excellent visual report presenting the results and choose suitable channels for the dissemination and publishing of the report. 

Course contents

  • Structured and unstructured data
  • Text mangling and analysis
  • Different tools for analysis (eg. Google analytics, Netlytics)
  • Reliability and relevance tools (eg.  SPSS, SAP predictive analytics, Excel analytics)
  • Visualisation of the results, the dissemination and distribution of reports (e.g. different Internet channels)

Learning methods

Depending on the implementation, learning takes place in contact lessons, as independent studies, teamwork and online-studies. Implementations can include literature, assignments, R&D co-operation and company projects. The course includes the assessment of one’s own learning.

Recognition of prior learning (RPL)

If students have acquired the required competence in previous work tasks, recreational activities or on another course, they can show their competence via a demonstration and progress faster through their studies. More information and instructions for recognising and validating prior learning (RPL) are available at MyNet.

Cooperation with the business community

The studies and learning assignments seek to mirror everyday business in corporations and other organisations. The learning sessions may include guest lectures, visits and/or real-life cases to be solved. Students can apply their learning within their current work environment.

International dimension

Dependent on the implementation.

Responsible teachers

Jarmo Ritalahti