Data Analytics - RDI2HM102 - Opinto-opas

Data Analytics, 5 cr - RDI2HM102

Upcoming implementations

No upcoming implementations yet.

Current implementations

Data Analytics RDI2HM102-3028 20.01.2025-16.05.2025 5 op (MAICTE, ...) +-
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.

Responsible person

Veijo Vänttinen

Learning material and recommended literature

Material given in Moodle

Campus

Pasila Campus

Exam dates and re-exam possibilities

Three exams and subject specific coding assignments issued throughout the course

Teaching language

English

Timing

20.01.2025 - 16.05.2025

Learning assignments

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

Enrollment

02.01.2025 - 17.01.2025
Groups
- MAICTE
- MALEAE
- MASALE
- MAEXPE
- EXCH
- MAAVBUM
- MASTROME
- MASBUTUMe1
- INSTRUCTED
- VIRTUAL
- MADIGE
- MASTRE
- MACOME
Teachers

Veijo Vänttinen

Seats

61 - 100

Further information

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

Degree Programme

BUTEM Degree Programme in Business Technologies, ATBUM Degree Programme in Aviation and Tourism Business, LEBUM Degree Programme in Leading Business Transformation, HOSBUM Degree Programme in Tourism and Hospitality Business, BUTUM Degree Programme in Business Technologies and Management

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5
Data Analytics RDI2HM102-3029 20.01.2025-16.05.2025 5 op (MAAIBUME, ...) +-
Teaching methods and instruction

The course has lessons every week. The lessons cover the basics of data analytics applications using Python coding.

The course implementation welcomes independent study of topics with the help of ready-made examples and videos, within the guidelines of the Haaga-Helia University of Applied Sciences.

No previous experience in coding is required.

Responsible person

Juha Nurmonen

Learning material and recommended literature

The learning material is mainly distributed through the Moodle learning environment. The course brings together the main techniques needed for the fundamentals of data analytics. It also provides tools for applied analytics research or thesis work. Such issues are constantly changing and the course will mainly make use of teachers’ and otherwise up-to-date material.

Working life connections

The course implementations are designed for students who are about to enter work life or are already working there. The course content takes into account the content used in the field.

Campus

Pasila Campus

Exam dates and re-exam possibilities

The organisations of exam and retake will be agreed together with the course participants.

The exam will be held at the end of the course on a date to be agreed with the participants in the implementation. The date of the re-examination will also be mutually agreed with the participants of the implementation.

The exam and retakes are organised in Pasila and participants are expected to be present; particularly the identity of the examiner will be verified.

In the tests, the author has extensive access to materials and to the Internet. It is therefore not possible to conduct the test as an Exam.

Teaching language

English

Internationality

Data analytics skills are international skills. Methodological expertise is international.

Timing

20.01.2025 - 16.05.2025

Learning assignments

The topics presented in the lectures are learned by doing related exercises. Assignments will be returned regularly during the course, every one or two weeks. Assignments will be given and returned via Moodle. Assignments will cover the following topics: descriptive analytics, explanatory analytics, time series and time series forecasting, predictive analytics and basics of machine learning models.

Enrollment

07.01.2025 - 17.01.2025

Content scheduling

The course has weekly lessons according to the timetable. A detailed weekly timetable will be made available on the Moodle learning environment if the Director of Studies decides to start this implementation. The timetable for the returnable learning assignments will follow the lecture timetable.
Groups
- MAAIBUME
- CONTACT
Alternative learning methods

There are no shortcuts to learning, and doing the work is essential. Other ways of obtaining a grade in the Haaga-Helia University of Applied Sciences credit register should be requested from the teacher responsible for the course.

Teachers

Juha Nurmonen

Seats

31 - 50

Further information

The determination of the implementation grade will be announced at the beginning of the implementation.

Degree Programme

AIBUM Degree Programme in AI for Business Transformation

R&D proportion

0.00 cr

Virtual proportion

0.00 cr

Evaluation scale

H-5

Past implementations

Data Analytics RDI2HM102-3031 19.08.2024-13.12.2024 5 op (AVOINAMK, ...) +-
Teaching methods and instruction

This is a virtual course. Course material (including examples and videos) will be available in Moodle.

Learning material and recommended literature

Course material will be available in Moodle.

Campus

Pasila Campus

Exam dates and re-exam possibilities

No exam.

Teaching language

English

Timing

19.08.2024 - 13.12.2024

Learning assignments

There are three exercises. Their topics cover descriptive analytics, diagnostic analytics, time series and machine learning.

Enrollment

03.06.2024 - 16.08.2024
Groups
- AVOINAMK
- AVOINMASTERS
- VIRTUAL
- INSTRUCTED
Teachers

Toni Ernvall

Seats

30 - 100

Further information

Final grade is the average of assignment grades rounded to integer. All assignments must be passed.

Degree Programme

AVO Avoin ammattikorkeakoulu

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5
Data Analytics RDI2HM102-3027 21.10.2024-13.12.2024 5 op (EVENING, ...) +-
Responsible person

Juha Nurmonen

Campus

Pasila Campus

Teaching language

English

Timing

21.10.2024 - 13.12.2024

Enrollment

03.06.2024 - 18.10.2024
Groups
- EVENING
- EXCH
- ONLINE
- MAAVBUM
- MASTROME
Teachers

Juha Nurmonen

Seats

31 - 60

Degree Programme

BUTEM Degree Programme in Business Technologies, STROME Degree Programme in Strategising in Organisations, ATBUM Degree Programme in Aviation and Tourism Business, LEBUM Degree Programme in Leading Business Transformation, AVBUM Degree Programme in Sustainable Aviation Business, HOSBUM Degree Programme in Tourism and Hospitality Business

R&D proportion

0.00 cr

Virtual proportion

1.00 cr

Evaluation scale

H-5
Data Analytics RDI2HM102-3025 25.03.2024-17.05.2024 5 op (MAICTE, ...) +-
Teaching methods and instruction

In the classroom or online classes, one will learn the basics of data analytics using Python coding under the guidance of a teacher. Students can also study independently with the help of ready-made examples.

No previous coding experience is required.

Responsible person

Heidi Rajamäki-Partanen

Learning material and recommended literature

https://1u.fi/hhjuhanurmonen

Campus

Pasila Campus

Teaching language

English

Timing

25.03.2024 - 17.05.2024

Learning assignments

The implementation includes individual tasks where the skills learned are applied. The topics of the tasks include Descriptive analytics Diagnostic analytics Time series description and analysis Time series forecasting Predictive analytics and machine learning models

Enrollment

02.01.2024 - 15.02.2024

Content scheduling

Period 2, Autumn 2024
Groups
- MAICTE
- MASALE
- MADIGE
- MASTRE
- EXCH
- MACOME
- ONLINE
Alternative learning methods

The implementation allows one to participate in common teaching according to your own skills. The course material supports indivudual learning.

Teachers

Juha Nurmonen

Seats

31 - 60

Further information

Individual assignments are assessed and each contributes its own weight to the final grade. Each individual assignment must be passed according to the criteria given in the implementation during the same course implementation.

Degree Programme

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

R&D proportion

0.00 cr

Virtual proportion

0.00 cr

Evaluation scale

H-5
Data Analytics RDI2HM102-3024 15.01.2024-15.03.2024 5 op (MALEAE, ...) +-
Teaching methods and instruction

Note: by the request of the training subscriber (degree program), starting in 2024, "Python for everyone" has been taken as the starting point instead of the previous "data analytics for everyone".
The course introduces basic features to analyze structured data and is supported by related material and assignments. Topics covered include:
- descriptive analytics
- diagnostic analytics
- time series analysis and forecasting
- introduction to machine learning.
All the necessary material will be found on Moodle including Q@A section whereby students are able to discuss matters concerning the course including assignments.

Responsible person

Heidi Rajamäki-Partanen

Learning material and recommended literature

Material given in Moodle

Campus

Pasila Campus

Exam dates and re-exam possibilities

Four graded assignments issued throughout the course

Teaching language

English

Timing

15.01.2024 - 15.03.2024

Learning assignments

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

Enrollment

02.01.2024 - 12.01.2024

Content scheduling

15.1 - 15.3.2024
Groups
- MALEAE
- MAENTE
- MAEXPE
- VIRTUAL
- INSTRUCTED
Teachers

Veijo Vänttinen

Seats

31 - 100

Further information

Four assignments:
- descriptive analytics, (0 - 5 points)
- diagnostic analytics, (0 - 5 points)
- time series analysis and forecasting, (0 - 5 points)
- machine learning, (0 - 5 points)

Degree Programme

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

R&D proportion

0.00 cr

Virtual proportion

5.00 cr

Evaluation scale

H-5
Show all (4) Hide

Upon completion of the course, the student will:
• Identify data sources and assess their suitability for business needs.
• Understand the stages of data preparation, modeling, and forecasting.
• Understand the fundamental concepts of machine learning and artificial intelligence.
• Master methods of descriptive and explanatory analytics.
• Be able to utilize various visualization and reporting techniques.
• Understand the concept of information design.

Content
• Data analytics based on process thinking (CRISP-DM).
• Methods for descriptive and explanatory analytics.
• Analysis and forecasting of time series data.
• Models for predictive analytics and machine learning.
• Applied examples using Python.
• Tools for visualization and reporting.

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)"

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.

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.

No prerequisites.

The student understands the data analytics process and can apply it, instructed by the teacher, to a business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions, guided by the teacher.

The student is able to derive and visualize dashboards, scorecards and publish those using related digital tools. The students is able to apply one or some forecasting algorithms to a business problem, instructed by the teacher. The students is able to assess the reliability and relevance of business reports.

The student understands the data analytics process and can apply it independently to a simple business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions independently.

The students is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently one or some forecasting algorithms to a business problem. The students is able to assess the reliability and relevance of business reports.

The student understands the data analytics process and can apply it to a slightly complicated business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve demanding business questions.

The student is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently several forecasting algorithms to different business problems. The student is able to assess the reliability and relevance of business reports.