Curricula

Objectives

Purpose:
For the development of modern applications and for their functionality, a vital part is played by the data analysis concerning the data. Applications use data that is to be presented to the end users. For the end user, data as such is not in a presentable format. Hence, analysis methods are needed to support the end user who makes the decisions based on the information content.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice
Investigations and information retrieval

Course objectives:
You are able to identify data with the help of its content and metadata. You are able to present the data in a way that is appropriate to the situation. You have analyzed the data based on its definition in such a way that conclusions can be drawn from the results of the analysis. You are able to present the data you are analyzing.

Content

- Quantity and quality of data
- Datan analysis as a part of information processing
- Describing data
- Modifying data
- Data visualization
- Statistics
- Time series
- Correlation
- Linear ja nonlinear regression model
- Modelling periodical data
- Representing the analysed results

Time and location

Opintojakso toteutetaan kevätlukukaudella 2023.

Learning materials and recommended literature

Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana. Opintojaksolla hyödynnetään Python 3.9+-ympäristöä, Git-versiohallintaa, NumPy- ja Pandas-kirjastoja, visualisointikirjastoja sekä muita soveltuvia kirjastoja.

Harjoitustehtävien tekemisessä hyödynnetään myös Anaconda-ympäristöä sekä Jupyter Notebook -formaattia.

Teaching methods

Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.

Practical training and working life connections

Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.

Exam dates and retake possibilities

Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettujen aikataulujen puitteissa.

Alternative completion methods

Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op.) kurssilla on 108 tuntia.

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Sufficient (1):
You can identify data based on content and metadata. You know the most common techniques of data analytics used in data analysis tasks. You can apply the most common techniques for your data analysis. You can assess the results of data analysis in a restricted manner. You can present results of the data analyzed by you.

Satisfactory (2):
You are able to take data into use based on its content and metadata. You are familiar with the most commonly used techniques of data analytics in data analysis tasks. You are able to select the techniques for data analysis and apply the technical competence in practice. You are able to assess the data analysis result superficially. You are able to present the essential results of the data analyzed by you.

Evaluation criteria, good (3-4)

Good (3):
You can take essential data into used based on its content and metadata. You know the most commonly used techniques of data analytics in various data analysis tasks. You can justify and select the techniques for data analysis and apply your technical competence in practice. Additionally, you are able to assess the implementation and justify its development. You are able to present the essential results of the data analyzed by you.

Very good (4):
You are able to take the essential data into use based on its content and metadata. You know the most commonly used techniques in data analytics and are able to justify comprehensively the use of the implemented techniques in various data analysis tasks. You are able to justify and select the correct techniques for data analysis. You are able to apply your technical competence to practice. You are able to assess the implementation comprehensively and validate its further development. You are able to present the data analyzed by you extensively.

Evaluation criteria, excellent (5)

Excellent (5):
You are able to take the essential data take into use based on its content and metadata taking into account the substance. You know the most commonly used techniques in data analytics and are able to critically justify the use of the implemented techniques in various data analysis tasks. You are able to apply your technical competence to practice. You are able to assess the implementation critically and validate its further development. You are able to present the results of the data analyzed by you comprehensively.

Prerequisites

Basics in computing, programming, knowledge and know-how of Python programming language.