Kursen ingår i dessa läroplaner och studiehelheter
- Big data analytics 2016 - Big data analytics
- Big data analytics 2017 - Big data analytics
- Big data analytics 2018 - Big data analytics
- Big data analytics 2019 - Big data analytics
- Big data analytics 2020 - Big data analytics
- Big data analytics 2021 - Big data analytics - specialised professional studies
- Big data analytics 2022 - Big data analytics - specialised professional studies
- Big data analytics 2023 - Big data analytics - specialised professional studies
Kursens undervisningsperiod
1 (2022-08-01 till 2022-10-23)
Nivå/kategori
Undervisningsspråk
Engelska
Kurstyp
Obligatorisk
Cykel/nivå
Högre yrkeshögskoleexamen
Rekommenderat studieår
1
Omfattning
5 sp
Kompetensmål
The aim of the course is to introduce the
student to the different concepts of
implementing an analytics process.
Students learn the process of problem solving in
analytics from data understanding and
preprocessing, through modelling choices and
implementation until the interpretation,
visualization and utilization of the analysis.
We will look at typical real-life applications of
analytics.
The course will provide hands-on lectures to
performing the steps of modeling and analysis.
Läranderesultat
At the end of the course the student is
expected to be able to model
predictive time series problems that use
machine learning for performing
regression. The student learn to implement an
analytics process for forecasting and to validate
results by calculating forecasting errors
Innehåll
This course includes topics on analytics systems,
Python development, feature engineering, time series
forecasting, visualization, and error calculation.
Förkunskaper
Basic python programming skills are required. Bases on Linear algebra and statistics are an asset. Knowledge of UNIX operative systems is recommended, but not required.
Litteratur
See literature as specified on Itslearning.
Studieaktiviteter
- Föreläsningar - 30 timmar
- Projekt- och produktionsarbete/konstnärlig verksamhet - 55 timmar
- Självstudier - 50 timmar
Arbetsbelastning
- Kursens totala antal arbetstimmar: 135 timmar
- Varav självstyrda studieformer: 135 timmar
- Varav schemalagda studier: 0 timmar
Undervisningsform
Flerformsundervisning (delvis nätundervisning handledd eller självstudier)
Examinationskrav
To pass the course the student should pass the
following examinations:
Assignment 1 as specified in Itslearning.
Project 1 as specified in Itslearning.
The examinations contribute to the final grade
as follows:
Assignment 1 - 10%
Project 1 - 90%
Lärare
- Scherbakov-Parland Andrej
- Westerlund Magnus
Examinator
Westerlund Magnus
Kursens hemsida
Antal kursplatser
Ingen begränsning (27 studenter anmälda)
Delprestation i kraft till
12 månader efter kursens slutdatum
Kursanmälningstid
2022-08-10 till 2022-09-06
Datum | Tid | Rum | Titel | Beskrivning | Organisatör |
---|---|---|---|---|---|
2022-09-01 | 13:00 - 18:00 | D4109 | Introduction to Analytics | Scherbakov-Parland Andrej Westerlund Magnus |
|
2022-09-02 | 13:00 - 18:00 | D4109 | Introduction to Analytics | Scherbakov-Parland Andrej Westerlund Magnus |
|
2022-09-15 | 13:00 - 18:00 | A511 | Introduction to Analytics | Scherbakov-Parland Andrej Westerlund Magnus |
|
2022-09-16 | 13:00 - 18:00 | A511 | Introduction to Analytics | Scherbakov-Parland Andrej Westerlund Magnus |
|
2022-09-29 | 13:00 - 18:00 | D4110 | Introduction to Analytics | Scherbakov-Parland Andrej Westerlund Magnus |
|
2022-09-30 | 13:00 - 18:00 | D4109 | Introduction to Analytics | Scherbakov-Parland Andrej Westerlund Magnus |