Data Analytics (5op)

Osaamistavoitteet

Kurssin jälkeen opiskelija: • tunnistaa tietolähteet ja osaa arvioida niiden käytettävyyttä liiketoiminnan tarpeisiin. • ymmärtää datan valmistelun, mallintamisen ja ennustamisen vaiheet. • ymmärtää koneoppimisen ja tekoälyn peruskäsitteet. • hallitsee kuvailevan ja selittävän analytiikan menetelmiä. • osaa hyödyntää erilaisia visualisoinnin ja raportoinnin tapoja. • ymmärtää informaatiomuotoilun käsitteen.

Sisältö

Sisältö • prosessiajatteluun perustuva data-analytiikka (CRISP-DM) • kuvailevan ja selittävän analytiikan menetelmät • aikasarjojen analysointi ja aikasarjaennustaminen • ennakoivan analytiikan ja koneoppimisen malleja • soveltavia esimerkkejä Pythonin avulla • visualisoinnin ja raportoinnin työvälineitä

Esitietovaatimukset

No prerequisites. This course unit is part of the master's degree's curriculum. Completion of the course requires master's study entitlement.

Arviointikriteerit, tyydyttävä (1)

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. 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.

Arviointikriteerit, hyvä (3)

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.

Arviointikriteerit, kiitettävä (5)

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.

Toteutustavat

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)"

Suoritustavat

Nykyajan organisaatioissa tieto on keskeinen johtamisen väline. Data-analytiikka on väline tiedon jalostamiseen liiketoiminnan tarpeisiin. Opintojakson tavoitteena on ymmärtää data-analytiikan prosessi ja menetelmiä sekä osata soveltaa niitä käytännön esimerkkien avulla. Opintojakso ei edellytä aikaisempaa ohjelmointiosaamista.