Data Analytics - RDI2HM102 - Opinto-opas

Data Analytics, 5 cr - RDI2HM102

Upcoming implementations

Data Analytics RDI2HM102-3014 27.03.2023-19.05.2023 5 op (MAICTE, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

27.03.2023 - 19.05.2023

Enrollment

02.01.2023 - 24.03.2023
Groups
- MAICTE
- MASALE
- MAENTE
- EVENING
- MAEXPE
- EXCH
- ONLINE
Teachers

Juha Nurmonen

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

Virtual proportion

2 cr

Evaluation scale

H-5

Current implementations

No ongoing implementations yet.

Past implementations

Data Analytics RDI2HM102-3013 16.01.2023-17.03.2023 5 op (MAICTE, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

16.01.2023 - 17.03.2023

Enrollment

02.01.2023 - 13.01.2023
Groups
- MAICTE
- EVENING
- MAEXPE
- CONTACT
- MADIGE
- EXCH
Teachers

Juha Nurmonen, Aki Taanila

Seats

31 - 50

Degree Programme

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

Evaluation scale

H-5
Data Analytics RDI2HM102-3012 24.10.2022-16.12.2022 5 op (MAICTE, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

24.10.2022 - 16.12.2022

Enrollment

13.06.2022 - 21.10.2022
Groups
- MAICTE
- EVENING
- MAENTE
- CONTACT
- MASTRE
- EXCH
- MACOME
Teachers

Veijo Vänttinen

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

Evaluation scale

H-5
Data Analytics RDI2HM102-3010 22.08.2022-14.10.2022 5 op (EVENING, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

22.08.2022 - 14.10.2022

Enrollment

13.06.2022 - 19.08.2022
Groups
- EVENING
- ONLINE
- MAAVBUM
- MASTROME
Teachers

Veijo Vänttinen

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

Virtual proportion

5 cr

Evaluation scale

H-5
Data Analytics RDI2HM102-3009 28.03.2022-20.05.2022 5 op (MALEAE, ...) +-
Campus

Pasila Campus

Teaching language

English

Timing

28.03.2022 - 20.05.2022

Enrollment

03.01.2022 - 25.03.2022
Groups
- MALEAE
- MASALE
- EVENING
- MAEXPE
- CONTACT
- MASTRE
- MACOME
- EXCH
Teachers

Juha Nurmonen

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

Evaluation scale

H-5
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- Process thinking driven Data analytics
- Descriptive, Predictive and Prescriptive analytics
- Structured, unstructured and semi-structured data
- Advanced data analytics
- Algorithms in data analyses, e.g. machine learning algorithms in predicting
- Significance, relevance and other important statistical terms to verify the outcome
- Several applications are used, depending on the subject including the following non-exclusive list: Microsoft, SAP and Qlik analytics families, SPSS
- Overview introduction to analytical coding enablers such as R, Python, C, SQL
- Overview to Social Media analytics and related tools such as Google analytics and SAP Predictive Analytics, etc.
- Visualization and reporting tools

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)"

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.