Datan esikäsittely (4 cr)

Evaluation scale

0-5

Objective

After the course, you will understand the data analytics process and the challenges it brings. You can identify different data formats, the most common interface solutions and the tools and methods used in data preprocessing. In addition, you know how to apply the methods necessary for data preprocessing.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

o Various data sources and data formats, JSON, APIt, retrieval of tables from SQL
o Types of variables
o Data preprocessing before bringing it into analysis program
o Data preprocessing in Pandas (basics of Pandas/DataFrames)
o Connecting various data sources

Location and time

Opintojakso toteutetaan verkkototeutuksena (ei kontaktiopetusta). Opiskelija voi edetä toteutuksella omaan tahtiin.

Materials

Opintojakson verkkosivut (luentomateriaali, harjoitukset, harjoitustyöohjeistus).

Teaching methods

Opintojakso sisältää harjoituksia eri aihealueilta sekä opintojakson aihepiirejä yhdistävän harjoitustyön.

Student workload

Harjoitukset 80 h ja harjoitustyö 28 h. Yhteensä 108 h

Assessment criteria, satisfactory (1)

Satisfactory 2: The student masters data retrieval from a selected source. You are able to implement data preprocessing to datasets. You are able to apply simple methods used in data preprocessing to his/her data. You can assess your own solutions for data preprocessing.

Sufficient 1: You know and understand the significance of data and its advantages. You know the significance of data preprocessing and its most common methods. You are able to apply simple methods obtained and used in data preprocessing.

Assessment criteria, good (3)

Very good 4: You master the retrieval of data from various sources. You are able to plan and implement data preprocessing for various datasets. You are able to apply the methods used in data preprocessing widely. You can assess and justify your own solutions for data preprocessing.

Good 3: You master data retrieval from several sources. You are able to plan and implement data preprocessign for datasets. You are able to apply methods used in data preprocessing. You can to assess and justify your own solutions for data preprocessing.

Assessment criteria, excellent (5)

Excellent 5: You master the retrieval of data from various sources. You are able to plan and implement data processing for various datasets. You are able to apply the methods used in data preprocessing. You can critically assess and justify your own solutions for data preprocessing.

Qualifications

Basic skills of computer use, basic skills in programming, knowledge of Python programming language.