BD-2-001 (0) - Introduction to Analytics

• 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

1 (2022-08-01 till 2022-10-23)

Engelska

Obligatorisk

Högre yrkeshögskoleexamen

1

5 sp

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.

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

This course includes topics on analytics systems,
Python development, feature engineering, time series
forecasting, visualization, and error calculation.

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.

See literature as specified on Itslearning.

• Föreläsningar - 30 timmar
• Projekt- och produktionsarbete/konstnärlig verksamhet - 55 timmar
• Självstudier - 50 timmar

• Kursens totala antal arbetstimmar: 135 timmar
• Varav självstyrda studieformer: 135 timmar
• Varav schemalagda studier: 0 timmar

Flerformsundervisning (delvis nätundervisning handledd eller självstudier)

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%

• Scherbakov-Parland Andrej
• Westerlund Magnus

Westerlund Magnus

https://arcada.itslearning.com/ Extern länk

Ingen begränsning (27 studenter anmälda)

12 månader efter kursens slutdatum

2022-08-10 till 2022-09-06