Big Data Analytics for Business WSE-EK-MON-BDAB

Module I. Big Data
1. Introduction: History, Definition, and Examples
2. Characteristics of Big Data
3. Types of Big Data
4. Basis for Classifying Big Data
5. SWOT Analysis of Big Data
6. Big Data in Economics
Module II. Big Data Analytics
1. Data Uncertainty
2. Analytical Models
3. Descriptive Analytics
4. Predictive Analytics
5. Prescriptive Analytics
6. Analytics: Requirements of Competitive Organizations in the Case of:
- Organizational Transformation
- System Complexity
- Volume Operations
- Analyst Knowledge
- The Case of Amazon.com
Module III. Big Data Visualization
1. Facts and Definition
2. Types of Data Presentation
3. Case Illustration
Module IV. Data Mining
1. Data Mining and Application Areas
2. Main Data Mining Techniques
3. Example Cases from:
- Principal Component Analysis (PCA) using SPSS

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.
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 .

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

economics and finance

(in Polish) E-Learning

(in Polish) E-Learning (pełny kurs)

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

Term 2023/24_Z:

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

Term 2022/23_Z:

Term 2025/26_Z:

Student activity Student workload in hours Lecture participation 30 Preparation for intergroup discussions (small group projects) 25 Consultation 5 Time to write a paper 15 Exam preparation 25 TOTAL HOURS 100 ECTS CREDITS 100 hours / 30 (25) hours ≈ 4

Subject level

intermediate

Learning outcome code/codes

EK1_WO4 EK1_UO3 EK1_UO4 EK1_UO9 EK1_kO3

Type of subject

Term 2023/24_Z:

optional with unlimited choices

Term 2025/26_Z:

obligatory

Term 2024/25_Z:

obligatory

Term 2026/27_Z:

obligatory

Preliminary Requirements

Basic knowledge of descriptive statistics

Course coordinators

Second Bwanakare

Learning outcomes

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 criteria

Form of the course: Lecture
The final mark is made of:
a) Activity during lectures and attendance: 20%
b) Project in group and its individual presentation + oral exam during the project presentation: 80%.
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.

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-bigdata
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 .
Facultative literature:
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

Term 2023/24_Z:

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

Term 2025/26_Z:

Literatura:
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-bigdata
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

Additional information

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

Description of WSE-EK-MON-BDAB in USOSweb