Data Analytics and Basics of Artificial Intelligence (8 cr)

Objectives (course unit)

The student knows the basics of data analysis and the most important methods in the Python programming language. The student knows technically and statistically how to process, analyze and visualize data. The student knows the basics of artificial intelligence, the most important concepts and knows different applications.

Content (course unit)

Fundamentals of data analysis and key methods in Python programming language. Processing, analyzing and visualizing data. The basics of artificial intelligence, the most important concepts and different applications. (5 ECTS)

Basics of data analysis and visualization (3 cr): Concepts: population, sample, sampling. Statistical indicators: Mean, standard deviation, median, mode, confidence intervals. P-value and tests (single variable, correlation, khii ^ 2, t-test of two independent / dependent samples. Data visualization. Linear regression, fitting the line to a set of points, the correlation coefficient and its square. Excel / Matlab etc. as a tool.

Assessment criteria, satisfactory (1-2) (course unit)

The student is able to technically and statistically process data, analyze and make visualizations. The student knows the basics of artificial intelligence and the most important concepts.

Assessment criteria, good (3-4) (course unit)

The student is able technically and statistically to process data, analyze it and make visualizations of it. The student knows the basics of artificial intelligence, the most important concepts and can create artificial intelligence applications based on examples.

Assessment criteria, excellent (5) (course unit)

The student is able to technically and statistically process data, analyze it and visualize data in a versatile way. The student knows well the basics of artificial intelligence, the most important concepts and knows how to create artificial intelligence applications.

Assessment methods and criteria

Kurssin arvosana koostuu kahdesta osasta:DataL&AI sekä tilastomatematiikka. Opiskelija saa molemmista osista arvosanan 0-5 ja lopullinen arvosana lasketaan siten, että DataL&AI osuuden paino on 5/8 ja tilastomatematiikan 3/8.

DataL&AI osuuden arvosanan muodostuminen:

Arvosana koostuu viikkoharjoituksista ja kahdesta oppimistehtävästä. Toinen oppimistehtävä on data-analyysiin liittyvä ja toinen koneoppimiseen / tekoälyyn.

Viikkotehtävistä voi saada 0-1 pisteen ja oppimistehtävistä kustakin 0-2. Pisteet lasketaan yhteen ja summasta muodostuu arvosana.

Viikkotehtäviä tulee tehdä min. 80 %, jotta yhden pisteen voi saada.



Tilastomatematiikan osuuden arvosanan muodostuminen:

Tilastomatematiikan osion arvosana muodostuu kurssin aikaisesta jatkuvasta näytöstä [30%] ja kokoavasta näytöstä (loppukoe) [70%].

Tilastomatematiikan osion arvosanarajat osuuksina maksimipisteistä
1: 30%
2: 50%
3: 65%
4: 80%
5: 90%

Assessment scale

0-5