Business AnalyticsLaajuus (5 cr)

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 machine learning, as well as a hands-on knowledge on how to implement data analytics in practice.
Business students focus more on the business value whereas information technology students have the focus closer to the technical implementation.
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
• knows the concepts of artificial intelligence and machine learning and how they are related to business analytics
• understand the role of knowledge models for business analytics and decision support
• is able to identify new data sources and use data from them.
• 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

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 generative AI
- Software tools for business analytics and machine learning
- Methods and tools for knowledge structuring and representation
- Methods and processes for managing and organizing business analytics projects
The contents may evolve during the implementation.

Lähtötaso ja sidonnaisuudet muihin opintojaksoihin

The student must complete Data Analytics (RDI2HM102) or possess equivalent skills before attending the course.

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 and machine learning methods are applied. The topic may represent a real case occurring 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 implement advanced business analytics methods. This casework is done in student groups that consist of both technically and business-wise skilled students.

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)"

Assessment criteria - grade 1

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.

Assessment criteria - grade 3

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.

Assessment criteria - grade 5

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.

Learning materials

Ramesh Sharda, Dursun Delen, Efraim Turban Business Intelligence, Analytics, Data Science, and AI: A Managerial Perspective, 5th edition, Pearson, 2023.
Data Science for Dummies, by Lillian Pierson and Jake Porway, 2017. Wiley et Sons.

Other literature on business analytics given during the course, business analytics tutorials and software tools.

Further information

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.

This course replaces the course Big Data (ISM8TX100) from the previous curriculum.