Scope: 5 ECTS (135 h)
Timing: 2nd - 3rd semester
Curriculum: Master Curriculum
Suomenkielinen opintojaksokuvaus julkaistaan viimeistään tammikuussa 2020.
Starting level and linkage with other courses
The student may focus on the business aspects or on the technical aspects of business analytics. The former approach does not require programming skills but for the latter approach, basic programming skills are required. Skills and interest in a logical and systematic way of working are required.
It is beneficial if the student has completed courses on research and development methods, quantitative research, statistics, digital marketing and business intelligence.
This course replaces the course Big Data (ISM8TX100) from the previous curriculum.
Learning objectives and assessment
The overall learning objective of the course is to give the students insight into both how business may benefit from data analytics, including advanced analytics and machine learning, as well as a hands-on knowledge on how to implement data analytics in practice.
Master’s degree students focus more on the business value whereas bachelor’s degree students have the focus closer to the practical implementation.
Upon successful completion of the course, the student:
- knows the concepts of artificial intelligence and machine learning and how they are related to each other
- understands the concept of business analytics and how it can be applied to bring value to business
- knows the concept of big data and how it differs from traditional data sets
- is able to identify new data sources and collect data from them. These include sources consisting of both company internal and external data. Such sources may be data warehouses, public open data, un- and semistructured data (social media data, log data ) and IoT data.
- knows some tools and methods for taking advantage of business analytics in product development and management
- is capable of planning and implementing a business analytics project
Passed courses are assessed on a scale of 1 to 5. The assessment criteria are presented for grades 1, 3 and 5.
The student understands the basic concepts of business analytics. S/he knows how business analytics can be used to create value for business. S/he can name related software tools and knows at an abstract level how they could be used.
The student has a good understanding of business analytics and its application for creating value for business. S/he knows related software tools and can use them in practice.
The student has an excellent understanding of business analytics and its application in creating value for business. S/he knows related software tools and is skilled at using them in practice.
- Concepts and terminology of business analytics, artificial intelligence and machine learning
- New business opportunities and use cases of business analytics, artificial intelligence and machine learning
- Concepts and methods for both descriptive and predictive analytics
- Methods and algorithms for machine learning
- KNIME and other software tools advanced business analytics and machine learning
- The contents may evolve during the implementation.
Depending on the implementation, learning takes place in contact lessons, independent studies, teamwork and online-studies. The course includes the assessment of one’s own learning.
The course is centred on a business case to which several data analytics and machine learning methods are applied. The topic may represent a real case occurring in a company or it may be picked up from the set provided by the course organiser. In the case study, the student will learn both how to create value for business as well as how to implement advanced business analytics methods. This casework is done in student groups that consist of both technical and business skilled students.
In addition to the business case, the following learning methods are used: flipped classroom method, individual online assignments, teamwork in contact lessons, hands-on lab guidance online and incontact lessons. The business case project can be done individually if the student has the necessary business and technical skills and upon agreement with the teacher.
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 thus 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 apply their learning within their current work environment.
Depending on the implementation. Students’ backgrounds and work environments will bring an international perspective to the course. Internationality can also be present through case studies, books, articles and assignments.