Business Analytics - DIG4HM102 - Opinto-opas

Business Analytics, 5 cr - DIG4HM102

Upcoming implementations

No upcoming implementations yet.

Current implementations

Business Analytics DIG4HM102-3006 21.08.2023-15.12.2023 5 op (MAICTE, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

21.08.2023 - 15.12.2023

Enrollment

12.06.2023 - 18.08.2023
Groups
- MAICTE
- MAICTF
- EVENING
- BLENDED
- MADIGE
- MADIGF
- ONLINE
- EXCH
Teachers

Lili Aunimo

Seats

31 - 60

Degree Programme

BUTEM Degree Programme in Business Technologies, ATBUM Degree Programme in Aviation and Tourism Business, LEBUM Degree Programme in Leading Business Transformation

R&D proportion

0.00 cr

Virtual proportion

2.00 cr

Evaluation scale

H-5

Past implementations

Business Analytics DIG4HM102-3004 22.08.2022-16.12.2022 5 op (MAICTE, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

22.08.2022 - 16.12.2022

Enrollment

13.06.2022 - 19.08.2022
Groups
- MAICTE
- EVENING
- BLENDED
- MADIGE
Teachers

Lili Aunimo

Seats

15 - 40

Degree Programme

BUTEM Degree Programme in Business Technologies, ATBUM Degree Programme in Aviation and Tourism Business, LEBUM Degree Programme in Leading Business Transformation

R&D proportion

0.00 cr

Virtual proportion

4.00 cr

Evaluation scale

H-5

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:
• 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

- Concepts and terminology of business analytics, artificial intelligence and machine learning
- 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
- Software tools for 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 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 business case, the following learning methods are used: flipped classroom method, individual online assignments, teamwork in contact lessons, hands-on lab guidance online and in contact 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. 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)"

Ramesh Sharda, Dursun Delen, Efraim Turban: Business Intelligence, Analytics and Data Science: A Managerial Perspective. Pearson, 2018.

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.

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

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

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.

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.