Business Analytics DIG704AS3YE-3001 20.10.2025-12.12.2025 5 op(EVENING, ...)+-
Campus
Pasila Campus
Teaching language
English
Timing
20.10.2025 - 12.12.2025
Enrollment
14.05.2025 - 22.05.2025
Groups
EVENING
CONTACT
BLENDED
MADIGE
MADIGF
EXCH
MASBUTUMe2
Teachers
Lili Aunimo, Dmitry Kudryavtsev
Seats
31 - 60
Degree Programme
LITEM Liiketoiminnan teknologiat -koulutus
R&D proportion
0.00 cr
Virtual proportion
2.50 cr
Evaluation scale
H-5
Current implementations
No ongoing implementations yet.
Past implementations
No past implementations yet.
Learning objectives
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 data visualization, as well as a hands-on knowledge on how to implement data analytics projects in practice.
Upon successful completion of the course, the student:
• understands the concept of business analytics and how it can be applied to bring value to business
• knows how to apply descriptive and predictive analytics in a business context
• understand the role of knowledge models for business analytics and decision support
• is able to identify data sources and use data from them
• knows some tools and methods typically used in business analytics projects
• is capable of planning and implementing a business analytics project
Contents
- Concepts and terminology of business analytics
- Business opportunities and use cases of business analytics
- Methods and software tools for descriptive analytics, including reporting, data visualization, dashboards, performance management systems
- Methods and software tools for predictive analytics, including data mining, text mining, web data mining and machine learning
- Methods and tools for knowledge structuring and representation
- Methods and processes for managing business analytics projects
Implementation methods, Demonstration and Work&Study
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 has a project work, which is centered around a business case to which several data analytics project management as well as descriptive and predictive data analytics methods are applied. The topic may represent a real case in a company, or it may be picked up from a 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 apply business analytics methods. This casework is done in student teams.
In addition to the project work, the following learning methods are used: flipped classroom method, in-class and individual assignments, teamwork in contact lessons, and online lessons.
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. The demonstration must be agreed with the course teacher. More information and instructions for recognising and validating prior learning (RPL) are available at https://www.haaga-helia.fi/en/recognition-learning Look at "Instructions to students (master)"
Learning materials
Ramesh Sharda, Dursun Delen, Efraim Turban Business Intelligence, Analytics, Data Science, and AI: A Managerial Perspective, 5th edition, Pearson, 2024.
Other literature on business analytics given during the course, business analytics tutorials and software tools.
Intro
Due to digitalisation, organizations possess more and more data. This data can be leveraged to provide valuable insights for businesses. Data analytics may be used in decision making, marketing, and product development – just to name some very common business cases. This course provides the student with the skill set required to manage business analytics projects and to carry out data analytics tasks in a business context.
Starting level and linkage with other courses
The student must be ready for independent study planning and information seeking, as well as the trial-and-error -type approach for completing some hands-on assignments and part of the course project. No prior programming experience is required, and assignments may be done using graphical programming. However, a basic ability of algorithmic thinking is needed, and the student must acquire this kills independently if needed.
This course unit is part of the master's degree's curriculum. Completion of the course requires master's study entitlement.
Assessment criteria
Assessment criteria - grade 1
When the implementation type of the course is CONTACT, ONLINE or BLENDED it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25 %, your grade will be lowered by one. If you are absent more than 50 %, the course is failed.
The student understands the basic concepts of business analytics. S/he has an idea of how business analytics could be used to create value for business. S/he can name processes, methods and software tools, and knows at an abstract level how some of them could be applied in business analytics projects.
Assessment criteria - grade 3
The student has a good understanding of business analytics and how it can create value for business. S/he knows processes, methods and software tools, and how they can be applied in practice in business analytics projects.
Assessment criteria - grade 5
The student has an excellent understanding of business analytics and its application in creating value for business. S/he knows processes, methods and software tools, and is skilled at applying them in practice in business analytics projects.