Data Analytics (5 cr)

Code: RDI2HM102-3028

Basic information of implementation

Enrollment
02.01.2025 - 17.01.2025
Enrolment for the implementation has ended.
Timing
20.01.2025 - 16.05.2025
Implementation has ended.
ECTS Credits
5 cr
Campus
Pasila Campus
Teaching languages
English
Seats
61 - 100
Degree programmes
ATBUM Degree Programme in Aviation and Tourism Business
LEBUM Degree Programme in Leading Business Transformation
BUTEM Degree Programme in Business Technologies
HOSBUM Degree Programme in Tourism and Hospitality Business
BUTUM Degree Programme in Business Technologies and Management
Teachers
Toni Ernvall
Veijo Vänttinen
Groups
MAICTE
Information Services and Systems, Masters, Pasila
MALEAE
Leadership and Human Resource Management, Masters, Pasila
MASALE
Leading Sales and Customer Experience, Masters, Pasila
MAEXPE
Experience Economy and Designing Services, Masters, Pasila
EXCH
EXCH Exchange students
MAAVBUM
Sustainable Aviation Business, Masters, Pasila
MASTROME
Strategising in Organisations, Masters, Pasila
MASBUTUMe1
Masters, Business Technologies and Management, 1. year
INSTRUCTED
Instructed virtual implementation
VIRTUAL
Virtual implementation
MADIGE
Digital Business Opportunities, Masters, Pasila
MASTRE
Strategic Thinking and Leadership, Masters, Pasila
MACOME
Communication and Marketing Management, master, Pasila
Course
RDI2HM102

Evaluation scale

H-5

Implementation methods, demonstration and Work&Study

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. 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)"

Intro

In modern organizations, information is a crucial tool for management. Data analytics is a means of refining information for business needs. The objective of this course is to understand the process and methods of data analytics and to be able to apply them through practical examples. This course does not require prior programming skills.

Materials

Material given in Moodle

Teaching methods and instruction

Note: the course subscriber (degree program) has required that this course be implemented in the Python coding language. Therefore, no other data analytics application programs are used in the course, only the Python coding language.
The course is divided into four parts, each of which focuses on analyzing structured data from different perspectives. Each part includes coding tasks and an exam. The parts are as follows.
- descriptive analytics
- diagnostic analytics
- time series analysis and forecasting
- introduction to machine learning.

All necessary material can be found on Moodle, including a Q@A section where students can raise questions related to the course.

Exam dates and re-exam possibilities

Three exams and subject specific coding assignments issued throughout the course

Learning assignments

Four assignments:
- descriptive analytics
- diagnostic analytics
- time series analysis and forecasting
- machine learning.

Assessment methods

Four assignments on scale accepted/rejected.
Three exams:
1) descriptive and diagnostic
2) time series and forecasting
3) machine learning

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