Big Data Engineering and Analysis II- lecture WSE-BD-IABD-II-w

https://teams.microsoft.com/l/team/19%3A7yyqQ50vGRDKWEWBPC5snlnrQpL9G8SESSrus1Gd4yo1%40thread.tacv2/conversations?groupId=97b98835-7111-46a5-a0ff-b75a770d5b1d&tenantId=12578430-c51b-4816-8163-c7281035b9b3

1. Methodological melting of the value of information: from Big Data to artificial intelligence
2. Features of Big data and their statistical structure
3. Descriptive and diagnostic analysis of data
4. Data mining methods:
a) grouping
b) uncontrolled and controlled classification
5. Machine learning and artificial intelligence

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

sociology

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

Term 2023/24_L:

Student activity Student's workload in hours participation in the lecture 15 preparation for intergroup discussions 12 consultation 5 time to write your paper 5 preparation for the exam 15 TOTAL NUMBER OF HOURS 52 NUMBER OF ECTS POINTS 52 hours / 30 (25) hours ≈ 2

Term 2024/25_L:

Student activity Student workload in hours lecture participation 30 preparation for intergroup discussions 10 consultation 5 time to write a paper 10 preparation for an exam 15 TOTAL NUMBER OF HOURS 70 NUMBER OF ECTS POINTS 70hours / 30 (25) hours ≈ 3

Subject level

intermediate

Learning outcome code/codes

According to the study program adopted by the Cardinal Stefan Wyszyński University Senate: https://monitor.uksw.edu.pl/docs/search BDAS2_W08, BDAS2_W09, BDAS2_W14

Type of subject

obligatory

Preliminary Requirements

Big Data engineering and analysis I - lecture Descriptive statistics

Course coordinators

Second Bwanakare

Learning outcomes

W01. Has structured and in-depth knowledge of the methodological and technical connections of Big Data with artificial intelligence
W02. Has knowledge of descriptive and diagnostic analysis of mass data,
W03. Has knowledge of methods for predictive and prescriptive analysis of data in the context of machine learning and artificial intelligence
U01. Able to perform descriptive analysis and diagnosis on data using SPSS software
U02. Is able to perform predictive analysis of classification and/or clustering using SPSS software in the context of a social phenomenon.
K01. Team preparation of social projects in the field of data analysis
K02. Interdisciplinary cooperation in the context of tasks performed

Assessment criteria

Criteria for passing the course: - attendance and activity (20%), group project (80%). It will be presented by each group member. Oral questions during the presentation are expected to further verify the learning outcomes.

Practical placement

n/a

Bibliography

Szeliga M., Data science i uczenie maszynowe, Wydawnictwo Naukowe PWN, Warszawa 2017
Szeliga M., Praktyczne uczenie maszynowe, Wydawnictwo Naukowe PWN, Warszawa 2019
Stephenson D., Big data, nauka o danych i AI bez tajemnic, Wydawnictwo Helion, Gliwice 2020

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WSE-BD-IABD-II-w in USOSweb