The aim of studying and bibliography

The main aim of study: The main aim of education on the module is the presentation of selected issues in the field of data warehouse and multidimensional data analysis using OLAP cubes and R / Python language elements.

The general information about the module: During the course, students learn the basics of multidimensional data analysis and selected algorithms in selected database and programming environments.

Teaching materials: http://v.prz.edu.pl/pawel.dymora

Bibliography required to complete the module

Bibliography used during lectures

1	Hadley Wickham, Garrett Grolemund	Język R. Kompletny zestaw narzędzi dla analityków danych	Helion.	2018
2	Osowski Stanisław	Metody i narzędzia eksploracji danych	BTC.	2017
3	Robert Layton	Learning Data Mining with Python	.	2017
4	Chodkowska-Gyurics Agnieszka	Hurtownie danych Teoria i praktyka	PWN.	2014
5	Pelikant A,	MS SQL Server. Zaawansowane metody programowania	Helion, Gliwice.	2014
6	Pelikant A	Hurtownie danych. Od przetwarzania analitycznego do raportowania	Helion, Gliwice.	2011

Basic requirements in category knowledge/skills/social competences

Formal requirements: Completed course of basics of databases and programming in the selected programming language. Knowledge of SQL. The student satisfies the formal requirements set out in the study regulations.

Basic requirements in category knowledge: The student should know the basic issues in the field of relational databases, algorithms, SQL language and basics of programming.

Basic requirements in category skills: He can explore data sets, write scripts, manipulate data.

Basic requirements in category social competences: Group work, communication skills.

Module outcomes

MEK	The student who completed the module	Types of classes / teaching methods leading to achieving a given outcome of teaching	Methods of verifying every mentioned outcome of teaching	Relationships with KEK	Relationships with PRK
01	Has basic knowledge about the organization of wholesalers and can indicate the benefits of implementing a data warehouse.	lecture, laboratory, project	pass, observation of performance	K_W05+ K_W06++ K_W07++ K_U05+ K_U06++ K_U07++ K_U08+++ K_U15+ K_U18++ K_U23+ K_K01+ K_K03+	P6S_KK P6S_KO P6S_KR P6S_UK P6S_UW P6S_WG
02	He knows the concept and understands the meaning of the OLAP cube and can perform advanced operations on the data cube.	lecture, laboratory, project	pass, observation of performance	K_W05+ K_W06++ K_W07++ K_U05++ K_U06++ K_U07++ K_U08+++ K_U15+ K_U18++ K_U23+ K_K01+ K_K03+	P6S_KK P6S_KO P6S_KR P6S_UK P6S_UW P6S_WG
03	He can design an effective data warehouse model and build an OLAP cube in the selected data warehouse tool and design ETL processes.	lecture, laboratory, project	pass, observation of performance	K_W05+ K_W06++ K_W07++ K_U05++ K_U06+++ K_U07++ K_U08+++ K_U15++ K_U18+++ K_U23+ K_K01+ K_K03++	P6S_KK P6S_KO P6S_KR P6S_UK P6S_UW P6S_WG
04	Student is able to use the SQL / MDX language and selected implementations of packages and data mining algorithms in the R and Python environment for multidimensional exploring data.	lecture, laboratory, project	pass, observation of performance	K_W05+ K_W06++ K_W07++ K_U05+++ K_U06+++ K_U07+++ K_U08+++ K_U15+ K_U18+++ K_U23+ K_K01+ K_K03++	P6S_KK P6S_KO P6S_KR P6S_UK P6S_UW P6S_WG

Attention: Depending on the epidemic situation, verification of the achieved learning outcomes specified in the study program, in particular credits and examinations at the end of specific classes, can be implemented remotely (real-time meetings).

The syllabus of the module

Sem.	TK	The content	realized in	MEK
6	TK01	Organizational classes. Determining the form of credit and the scope of the material. Familiarizing with the work regulations in the laboratory.	W01, L01
6	TK02	The genesis of data warehouse (HD) (Data Warehouse) and data mining systems (SED) (Data Mining Systems).	W01, W02, L01, L02, P01	MEK01 MEK02
6	TK03	Data and processing modeling (relational and multidimensional model, on-line analytical processing models (OLAP), multidimensional operations and data diagrams, OLAP classes and architectures - comparative analysis). Data extraction processes (ETL) (design and modeling of data extraction, specialized and universal ETL systems).	W03, L02, L03, P02	MEK01 MEK02 MEK03
6	TK04	Creating and using a SQL Server data warehouse. Use of wizards: OLAP cube, virtual dimension, warehouse design, usage-based optimization, usage-based analysis, dimension and virtual cube. Use the cube editor and dimension editor. Data mining. Creating structural and information dimensions. Creating calculated measures and category dimensions.	W04, L04, P3	MEK02 MEK03
6	TK05	Analytical processing and its optimization: materialized perspectives (rewriting queries, selecting a set of perspectives, refreshing anomalies), BY GROUP optimization, compression, parallel processing, partitioning. Using the SQL / MDX query language for data mining: designing and execution of queries.	W05, L05, P4	MEK03 MEK04
6	TK06	Implementacja modeli data mining (drzewo decyzyjne, asocjaceje, klasteryzacja, ML) w środowisku R.	W06, L06, P05, P06	MEK03 MEK04
6	TK07	Implementation of data mining models (decision tree, associations, clustering, ML) in Python.	W07, L07, P07	MEK01 MEK02 MEK03 MEK04

The student's effort

The type of classes	The work before classes	The participation in classes	The work after classes
Lecture (sem. 6)		contact hours: 15.00 hours/sem.	Studying the recommended bibliography: 3.00 hours/sem.
Laboratory (sem. 6)	The preparation for a Laboratory: 6.00 hours/sem. The preparation for a test: 3.00 hours/sem.	contact hours: 15.00 hours/sem.	Finishing/Making the report: 6.00 hours/sem.
Project/Seminar (sem. 6)		contact hours: 15.00 hours/sem..
Advice (sem. 6)	The preparation for Advice: 2.00 hours/sem.	The participation in Advice: 3.00 hours/sem.
Credit (sem. 6)	The preparation for a Credit: 5.00 hours/sem.	The written credit: 2.00 hours/sem.

The way of giving the component module grades and the final grade

The type of classes	The way of giving the final grade
Lecture	The lecture ends with an oral test.
Laboratory	Presence is obligatory in all laboratory classes - medical exemptions are allowed with the need to make up for classes.
Project/Seminar	The aim of the project classes will be an independent (also permissible team) implementation of an IT project, the effect of which is to be a documented implementation of selected data mining algorithms.
The final grade	The final grade is issued as the weighted average of 1/3 of the laboratory grade, 1/3 of the project grade and 1/3 of the lecture grade. The condition for admission to the exam is to obtain a positive final grade from the laboratory and a positive evaluation of the implementation of an independent project.

Sample problems

Required during the exam/when receiving the credit
(-)

Realized during classes/laboratories/projects
(-)

Others
(-)

Can a student use any teaching aids during the exam/when receiving the credit : no

The contents of the module are associated with the research profile: no