Big Data Analytics for Business
General data
Course ID: | WSE-EK-MON-BDAB |
Erasmus code / ISCED: |
(unknown)
/
(0311) Economics
|
Course title: | Big Data Analytics for Business |
Name in Polish: | Big Data Analytics for Business |
Organizational unit: | Faculty of Social and Economic Sciences |
Course groups: |
Courses at UKSW |
ECTS credit allocation (and other scores): |
4.00
OR
6.00
(depends on study program)
|
Language: | English |
(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się: | economics and finance |
Subject level: | intermediate |
Learning outcome code/codes: | EK1_WO4 EK1_UO3 EK1_UO4 EK1_UO9 EK1_kO3 |
Preliminary Requirements: | Basic knowledge of descriptive statistics |
Full description: |
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 . |
Efekty kształcenia i opis ECTS: |
TEACHING EFFECT’S KNOWLEDGE The student knows different types of statistical observations. He is able to define the concept of Big Data in different as-pects related to business analytics and data mining shown in the lecture. The student knows how to visualize the data and interpret them. SKILLS Student is able to choose quantitative methods depending on the type of the problem at hand. The student is able to properly apply the Big Data basic quantitative methods and interpret main issues of the found re-sults. COMPETENCES Student is able to choose quantitative methods depending on the type of the problem at hand. The student is able to properly apply the Big Data basic quantitative methods and interpret the found results. The student gives his opinion on the strengths and limitations of the content of the empirical conclusions. Student activity Student workload in hours participation in the lecture 30 preparation for the inter group discussions 25 consultation 5 time to write the work 10 time to the self-assessment of the inter-group work 5 preparation for the exam 25 TOTAL HOURS 100 NUMBER OF ECTS 100 hours / 30 (25) hours ≈ 4 |
Assessment methods and 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. |
Classes in period "Winter semester 2021/22" (past)
Time span: | 2021-10-01 - 2022-01-31 |
Navigate to timetable
MO WYK_MON
TU W TH FR |
Type of class: |
Monographic lecture, 30 hours, 20 places
|
|
Coordinators: | Second Bwanakare | |
Group instructors: | Second Bwanakare | |
Students list: | (inaccessible to you) | |
Examination: |
Course -
graded credit
Monographic lecture - graded credit |
|
(in Polish) E-Learning: | (in Polish) E-Learning (pełny kurs) |
Classes in period "Winter semester 2022/23" (past)
Time span: | 2022-10-01 - 2023-01-31 |
Navigate to timetable
MO TU WYK_MON
W TH FR |
Type of class: |
Monographic lecture, 30 hours, 24 places
|
|
Coordinators: | Second Bwanakare | |
Group instructors: | Second Bwanakare | |
Students list: | (inaccessible to you) | |
Examination: |
Course -
graded credit
Monographic lecture - graded credit |
|
(in Polish) Opis nakładu pracy studenta w ECTS: | Student activity Student workload in hours participation in the lecture 30 preparation for the inter group discussions 25 consultation 5 time to write the work 10 time to the self-assessment of the inter-group work 5 preparation for the exam 25 TOTAL HOURS 100 NUMBER OF ECTS 100 hours / 30 (25) hours ≈ 4 |
Classes in period "Winter semester 2023/24" (past)
Time span: | 2023-10-01 - 2024-01-31 |
Navigate to timetable
MO TU WYK_MON
W TH FR |
Type of class: |
Monographic lecture, 30 hours, 31 places
|
|
Coordinators: | Second Bwanakare | |
Group instructors: | Second Bwanakare | |
Students list: | (inaccessible to you) | |
Examination: |
Course -
graded credit
Monographic lecture - graded credit |
|
(in Polish) Opis nakładu pracy studenta w ECTS: | Student activity Student workload in hours participation in the lecture 30 preparation for the inter group discussions 25 consultation 5 time to write the work 10 time to the self-assessment of the inter-group work 5 preparation for the exam 25 TOTAL HOURS 100 NUMBER OF ECTS 100 hours / 30 (25) hours ≈ 4 |
|
Type of subject: | optional with unlimited choices |
|
(in Polish) Grupa przedmiotów ogólnouczenianych: | (in Polish) nie dotyczy |
|
Short description: |
This course will introduce you to the basic concepts of Big Data and discuss what Big Data analysis involves, as well as the technical, conceptual and ethical challenges involved. It also provides fundamental techniques for managing and analyzing large and complex data structures, commonly referred to as data mining or exploratory analysis. To facilitate understanding of this topic, you will need basic knowledge of descriptive statistics. |
|
Full description: |
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 . |
|
Bibliography: |
1) Craig Stedman, redaktor w Large, SearchDataManagement.com, The ultimate guide to big data for businesses, https://searchdatamanagement.techtarget.com/pro/The-Ultimate-Guide-to-Big-Data-for-Businesses?vgnextfmt=confirmation. Aby uzyskać więcej informacji, odwiedź stronę http://SearchDataManagement.com/ 2) Hrudaya Kumar Thripathy, Analiza danych (str. 1-55), https://www.slideshare.net/hktripathy/lecture1-introduction-to-big-data 3) S. Bwanakare (et al.), ESSnet Big Data I, WP7 Reports, milestones and deliverables1, EUROSTAT, 2017, https://ec.europa.eu/eurostat/cros/search/site/WP7%2520Multiple%2520domains_en . Teksty fakultatywne: 1) Thomas H. Davenport , Analytics 3.0: Big Data and Small Data in Big and Small Companies, Wykład dziekana, Berkeley School of Information, 18 września 2013 r. https://www.ischool.berkeley.edu/events/2013/analytics-30-big-data-and-small-data-big-and-small-companies 2) S. Bwanakare (et al.), Reconciling conflicting cross-border data sources for updating national accounts: The cross-entropy econometrics approach, Statistical Journal of the IAOS, vol. Pre-press, nie. Pre-press, ss. 1–9, 2020 r., https://content.iospress.com/articles/statistical-journal-of-the-iaos/sji180489 , 3) R. Raka i S. Bwanakare, Ilościowa charakterystyka korelacji danych meteorologicznych, Polska Akademia Nauk, Acta Physica Polonica A,vol. 129/5, maj 2016, DOI: 10.12693 / APhysPolA.129.922 lub http://przyrbwn.icm.edu.pl/APP/PDF/129/a129z5p05.pdf |
Classes in period "Winter semester 2024/25" (future)
Time span: | 2024-10-01 - 2025-01-31 |
Navigate to timetable
MO TU W TH FR |
Type of class: |
Monographic lecture, 30 hours, 10 places
|
|
Coordinators: | Second Bwanakare | |
Group instructors: | Second Bwanakare | |
Students list: | (inaccessible to you) | |
Examination: |
Course -
graded credit
Monographic lecture - graded credit |
|
Type of subject: | obligatory |
|
(in Polish) Grupa przedmiotów ogólnouczenianych: | (in Polish) nie dotyczy |
Copyright by Cardinal Stefan Wyszynski University in Warsaw.