Big Data Analytics for Business WSE-EKN-WMonAng
The exponential growth in the amount of human-generated data and increasingly powerful machines continues to accelerate. Data scientists have recently proposed different methods to analyze this category of data, known as Big Data.
It is important that economic decision-makers are at least able to fully understand and interpret the results provided by data scientists at economic level. This is precisely the ultimate goal of this course in terms of social competence for the future economists and / or business leaders: to familiarize them with big data visualization and analysis as a source of valuable information addressing to fundamental questions of professionals. The course begins with a basic introduction to big data. It then discusses what the analysis of this data entails, as well as the associated technical, conceptual and ethical challenges. It also provides a first practical experience in the management and analysis of large and complex data structures .
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Term 2023/24_Z:
The exponential growth in the amount of human-generated data and increasingly powerful machines continues to accelerate. Data scientists have recently proposed different methods to analyze this category of data, known as Big Data. |
(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się
(in Polish) E-Learning
(in Polish) Grupa przedmiotów ogólnouczenianych
(in Polish) Opis nakładu pracy studenta w ECTS
Subject level
Learning outcome code/codes
Type of subject
Preliminary Requirements
Course coordinators
Learning outcomes
Wiedza
Student będzie w stanie opisać różne rodzaje obserwacji statystycznych. Będzie on w stanie zdefiniować Big Data w różnych aspektach związanych z analityką biznesową i eksploracją danych. Student będzie ze znanymi wizualnymi reprezentacjami danych i będzie w stanie je zinterpretować.
UMIEJĘTNOŚCI
Student będzie mógł
• Wybierz metody ilościowe w zależności od rodzaju problemu
• Zastosuj podstawowe metody ilościowe Big Data i zinterpretuj wyniki
• Opiniuj mocne strony i ograniczenia treści wniosków empirycznych.
KOMPETENCJE
Student potrafi dobierać metody ilościowe w zależności od rodzaju postawionego problemu.
Student jest przygotowany do prawidłowego zastosowania podstawowych metod ilościowych Big Data oraz interpretacji otrzymanych wyników.
Student opiniuje mocne i słabe strony treści wniosków empirycznych.
Aktywność studencka Nakład pracy studenta w godzinach
udział w wykładzie 30
przygotowanie do dyskusji międzygrupowych(projekty w małych grupach) 25
konsultacja 5
czas na napisanie pracy 10
czas na samoocenę pracy międzygrupowej 5
przygotowanie do egzaminu 25
CAŁKOWITA LICZBA GODZIN 100
LICZBA PUNKTÓW ECTS 100 godzin / 30 (25) godzin ≈ 4
Assessment criteria
Form of the course: Lecture
Assessment: 2( 1 work in groups and a final written exam)
The final grade includes: a grade from the written test (50%) and a grade from the work self-assessment in-ter groups (50%).
10 pts - grade: 5,0;
8-9 pts - grade: 4,5;
7-8 pts - grade: 4,0;
6-7 pts - grade: 3,5;
5 - 6 pkt - ocena 3,0;
below 5 pts - grade: 2,0
2 –bad work- a student has not provided the work, or the work is not her independent achievement, is chaotic with regard to Big Data different concepts and technical analysis methods. a student does understand basic concepts related to Big Data analytics. He avoids any discussion related to Big Data issues.
3 – enough good- a student proves to understand basic concepts of Big Data in different aspects related to business analytics and data mining shown in the lecture. He can visualize the statistical data using the taught software during the lectures. He still shows difficulties to master the empiric side of Bid Data with respect to data mining techniques. a student basic insights related to Big Data concepts and visualization. He does not master computational tech-niques but recognizes its usefulness. He would be ready to increase knowledge and competences for professional purposes.
4 – good_ a student has provided a good work and stated problems and positions correctly. He is able to choose and apply the adequate quantitative methods depending on the type of the problem at hand. a student initiates discussions related to big data issues and can understand various reports presented by data engineers in the fields of business or economics analytics.
5 - very good- a student has provided a good work and stated problems and positions correctly. He is able to choose and apply the adequate quantitative methods depending on the type of the problem at hand. He can interpret adequately the solution and he shows to allude to the literature proposed in the syllabus. The student initiates discussions related to Big Data issues, knows to select and apply efficiently computational techniques to solve problems in business analytics, He understands the implications of Big Data in business, places them in the broader background of everyday.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: