Министерство освіти і науки, молоді і спорту України



APPROVED

Order of the Ministry of Education and Science

dated June 5, 2013 № 683

Form № H-3.04

MINISTRY OF EDUCATION AND SCIENCE OF UKRAINE

PRYAZOVSKYI STATE TECHNICAL UNIVERSITY

Faculty of Economics

Department of Marketing and Business Administration

APPROVED

Dean

Faculty of Economics

_______________________ О. Khadzhynova

"____" __________ 20__

1

2

SYLLABUS

| |BIG DATA ANALYTICS AND BUSINESS INTELLIGENCE |

| |(code and name of the discipline) |

|for applicants for an educational degree |Second (master's) |

|specialties |073 "Management", 075 "Marketing" |

| |(code and name of the direction of training) |

|branch of knowledge |07 "Management and Administration" |

| |(code and name of the field of knowledge) |

|educational program |"Business Administration", "Marketing", "IT Marketing" |

| |(name of specialization) |

3

The Big Data Analytics And Business Intelligence syllabus is developed in the framework of ERASMUS+ CBHE project “Digitalization of economic as an element of sustainable development of Ukraine and Tajikistan” / DigEco 618270-EPP-1-2020-1-LT-EPPKA2-CBHE-JP

This project has been funded with support from the European Commission. This document reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained there in.

2021 - 2022 academic year

[pic] [pic]

Syllabus on the discipline "Big Data Analytics And Business Intelligence" for students of knowledge 07 "Management and Administration" in the specialties: 073 "Management", 075 "Marketing" educational programs: "Business Administration", "Marketing" of the second (master's) educational level ..

PROGRAM DEVELOPER:

Professor of Marketing and Business Administration, GONCHAR V.

Professor of Marketing and Business Administration, Kalinin O.

The work program was approved at a meeting of the Department of Marketing and Business Administration

Protocol from “__16 __” _______ 12_________2019 № 9

Head of the department _______________ V, Gonchar

Approved by the methodical commission of the faculty

Minutes from “__20 __” _______ 12_________2019 № 5

The chairman of the commission ____________________ Т. Chernata

© Gonchar V., Kalinin O., SHEI "PSTU" 2021

© SHEI "PSTU", 2021

1. Description of the discipline

| | ECTS |Hours |Classroom hours |Independen|Distribution by semesters |

| |credits| | |t work | |

|Form of study | | | | | |

| | | |

| | |25 |26 |

| |BA-21-M, |Gonchar V., Professor |Gonchar V., Professor |

| |MK-21-M, | | |

| |MK-21-M | | |

4. TOPIC AND CONTENT OF LESSONS

|Module |Week |Kind of occupations|

|1 |IT factor of influence in modern management tasks |thirteen |

|2 |Data modeling and analysis |15 |

|3 |Data Warehouse architectures and techniques |15 |

|4 |Extraction, Transformation & Load (ETL) |15 |

|5 |Information Provisioning (Reporting, Dashboards) |15 |

|6 |Analytical Life Cycle and Methods: Clustering, Classification, Machine Learning |15 |

|7 |Big Data modeling |15 |

|8 |Architecture and Deployment |13 |

| |Total |118 |

Calculation of time for independent work of the student by types

|№ s / n |Type of work |Number |

| | |hours |

| | |(Full-time) |

|1 |Elaboration of program material taught at lectures |20 |

|2 |Preparation for laboratory work |- |

|3 |Preparation for practical (seminar) classes |20 |

|4 |Execution of individual tasks (abstracts, creative, calculation and graphic works, presentations, etc.) |20 |

|5 |Preparation for control measures (modular control work) |22 |

|6 |Course design |- |

|7 |Preparation of independent homework |36 |

| |Total |118 |

Independent work is performed in accordance with the guidelines for independent work of the student

6. Individual tasks - not provided by the curriculum

7. Teaching methods

Lectures are accompanied by a demonstration of diagrams, drawings, diagrams, using a multimedia system.

During the study of the course tests, seminars and credit - module control of students' knowledge are provided.

8 Assessment of learning outcomes

8.1 Evaluation criteria

|Number |Evaluation criteria |Level |Score on a national scale |

|points | |competence | |

|90-100 |The student shows special creative | |Examination |Test |

| |abilities, is able to independently, | | | |

| |sufficiently fully and accurately answer| | | |

| |questions, convincingly argues the |High | | |

| |answers, gives the necessary examples, |(creative) |perfectly | |

| |correctly solves problems. | | | |

| | | | | |

| | | | | |

| | | | | |

| | | | | |

| | | | | |

| | | | |Credited |

| | | | | |

| | | | | |

| | | | | |

|82-89 |The student is fluent in the material of| | | |

| |disciplines, can give examples, | | | |

| |correctly and reasonably answer | | | |

| |questions, freely performs standard | | | |

| |tasks, the answers contain a small | | | |

| |number of inaccuracies or omissions | | | |

| | | | | |

| | | | | |

| | | | | |

| | | | | |

| | | |Good | |

|74-81 | The student has the material of the | | | |

| |discipline, correctly and reasonably | | | |

| |answers questions, performs standard | | | |

| |tasks, the answers have a small number | | | |

| |of significant errors | | | |

|64-73 |The student in his answers reproduces a | | | |

| |significant part of the theoretical | | | |

| |material, shows knowledge and | | | |

| |understanding of the basic principles; | | | |

| |his answer has errors, some of which are| | | |

| |significant | | | |

| | |Average |satisfactorily | |

| | |(reproductive) | | |

|60-63 |The student has the study material at a | | | |

| |level higher than the initial, a | | | |

| |significant part of it is reproduced at | | | |

| |the reproductive level | | | |

|35-59 |The student has the material at the | | | |

| |level of individual fragments that make | | | |

| |up a small part of the study material | | | |

| | |Low | | |

| | |(receptive-productive) | | |

| | | |unsatisfactorily |Not credited |

|1-34 |The student has the material at the | | | |

| |level of elementary recognition and | | | |

| |reproduction of individual facts, | | | |

| |elements, objects | | | |

8.2 Assessment tools

To determine the level of assimilation of educational material by students use the following forms and methods of control and evaluation of knowledge:

- assessment of the student's work during practical classes in the form of an oral interview or performance of calculation tasks;

- writing final modular control and test works;

- assessment of completed independent homework and its protection;

- passing the exam.

Assessment of students' knowledge of the discipline " BIG DATA ANALYTICS AND BUSINESS INTELLIGENCE " is carried out in accordance with the requirements of the credit-module system of the educational process. This system is based on the implementation of end-to-end control in the classroom in accordance with its form (lecture, practical).

The final assessment of the current control is the assessment per module, ie the principle of modular accounting of students' knowledge is implemented.

The curriculum in the discipline " BIG DATA ANALYTICS AND BUSINESS INTELLIGENCE " provides for passing the credit. The ECTS assessment scale is used to assess knowledge.

The procedure for the current assessment of students' knowledge.

The current assessment of students' knowledge is carried out during practical classes and aims to check the level of readiness of the student to perform a particular job. Objects of current control are:

- activity and efficiency of the student's work during the semester on studying the program material of the discipline, attending classes;

- performance of tasks in practical classes;

- performance of tasks of current control.

Students' work in practical classes is evaluated on a 100-point system. When assessing the implementation of practical tasks, attention is paid to their quality and independence.

Monitoring the implementation of independent homework involves identifying the student's mastery of the lecture module and the ability to use it to solve a practical situation and is carried out in the form of defense of independent homework.

Carrying out final control.

The condition for admission to the exam is a positive grade obtained by the student in practical classes and the performance and defense of independent homework. The exam is conducted in writing on control questions, which are formed in the examination tickets, which gives the opportunity to assess the student's knowledge of the entire discipline. The exam is carried out on the basis of the REGULATIONS ON SEMESTER EXAMS AND MESSAGES ON SHEE "PDTU" 7.6-02: 2015 and the order on certification.

EXAM PREPARATORATION’S QUESTIONS

1. Application Design for Analytical Processing Technologies (ADAPT)

2. ADAPT: Dimensions

3. Architecture of A „Big Data System“

4. Components of a Data Warehouse: Data Cube

5. ADAPT: Parallel Hierarchies

6. Approaches For Implementation – Possible Solutions

7. Comparison Data Marts – Core Data Warehouse

8. Components of a Data Warehouse: Basis database

9. ETL – Extraction

10. Components of a Data Warehouse: Data Marts

11. Components of a Data Warehouse: Manager

12. Data Warehouse/ Data Marts – Architecture Variants

13. Data Integration: Business Perspective

14. ADAPT: Connectors

15. Data sources

16. Data sources – quality requirements

17. Multidimensional Data Model – Operators

18. Data Warehouse – Characteristics

19. ADAPT: Facts & Measures

20. Data Warehouse vs. Data Lake

21. ETL – Purpose and Requirements

22. ETL – Tools

23. Extraction: Initial Loads vs. Delta Loads

24. Data Lake: Definition

25. Extraction: Types of Data Delivery

26. Homogenization: Simple Conversions

27. Implementation of Dashboards: Requirements

28. Multidimensional Data Model – Basic Elements

29. ADAPT: Attributes & Members

30. Information Consumers: Dashboards

31. ETL – Challenges

32. OLAP-Operators: Drill Through & Drill Across

33. Extraction: Monitoring Strategy

34. Information Provisioning in BI

35. Information Provisioning in BI

36. OLAP-Operators: Pivoting / Rotation

37. Integration of unstructured Data

38. Intended benefits through the use of BI (dashboards)

39. Logical Data Model for Data Warehousing

40. ADAPT: Hierarchies

41. Modeling in Data Warehousing

42. Multidimensional Data Model – Hypercube

43. Multidimensional Entity Relationship (ME/R-Model)

44. ODS-extended Data Warehouse Architecture

45. Transformation: Challenges

46. OLAP-Operators: Dice

47. Requirements for Dashboards: „SMART“

48. Online Analytical Processing (OLAP): FASMI

49. Phases of Data Warehousing

50. Visual Design of dashboards – Diagrams

51. Requirements for BI systems

52. ROLAP – Dimensions / Classification Hierarchy

53. Scheme Mapping: Normalized vs. Denormalized

54. OLAP-Operators: Roll-Up & Drill-Down

55. Semantic Modeling in Data Warehousing

56. Transformation: Tasks In ETL Process

57. Types of dashboards: Strategic dashboards

58. Visual Design of dashboards – Scales

59. OLAP-Operators: Slice

60. Visual Design/ Encoding

9. Control methods

Integrated assessment of students' knowledge in the discipline

" BIG DATA ANALYTICS AND BUSINESS INTELLIGENCE " is based on the results of current control and final control of knowledge on a 100-point scale

Distribution of points received by students of BA-19:

|Type of lesson or control measure |Points for one |Before the 1st certification |Until the 2nd certification |For the semester |

|(for example) |lesson or control | | | |

| |event | | | |

| |number of classes or control measures |the sum of points |number of classes or control measures |the sum of points |number of classes or control measures |the sum of points | |Lectures, including: | | | | | | | | |compendium | | | | | | | | |Practical, including: | | | | | | | | |- current control |10 |2 |20 |1 |5 |3 |25 | |Modular control work |20 |1 |20 |1 |20 |2 |40 | |Independent homework |25 |1 |10 |1 |15 |1 |25 | |SPD protection |10 | | |1 |10 |1 |10 | |The amount of current control | | |50 | |50 | |100 | |Total | | | | | | |100 | |

4 Recommended Sources

Basic

1. C. Chen, M. Lin, and X. Guo, “High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things,” Computers & Electrical Engineering, vol. 61, pp. 48–66, 2017.

2. Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis und Florian Mansmann. „Visual Analytics“. 2010

3. Dimitri P. Bertsekas and John N. Tsitsiklis. Introduction to Probability. Charles Wheelan. Naked Statistics: Stripping the Dread from the Data. W. W. Norton and Company, 2013.

4. F. Liu, Y. Liu, D. Jin, X. Jia, and T. Wang, “Research on Workshop-Based Positioning Technology Based on Internet of Things in Big Data Background,” Complexity, vol. 2018, Article ID 875460, 11 pages, 2018.

5. H. Mora, M. Signes-Pont, D. Gil, and M. Johnsson, “Collaborative Working Architecture for IoT-Based Applications,” Sensors, vol. 18, no. 6, p. 1676, 2018.

6. H. Tahaei, R. Salleh, S. Khan, R. Izard, K.-K. R. Choo, and N. B. Anuar, “A multi-objective software defined network traffic measurement,” Measurement, vol. 95, pp. 317–327, 2017.

7. Hariri, R.H., Fredericks, E.M. & Bowers, K.M. Uncertainty in big data analytics: survey, opportunities, and challenges. J Big Data 6, 44 (2019).

8. INMON, W.H.; LINSTEDT, D.: Data architecture a primer for the data scientist: big data, data warehouse and data vault. 2014.

9. J. Han, M. Kamber. 2011. Data Mining. Concepts and Techniques Visualize This by Nathan Yau

10. J. Pan and J. McElhannon, “Future edge cloud and edge computing for internet of things applications,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 439–449, 2018.

11. L. J. M. Nieuwenhuis, M. L. Ehrenhard, and L. Prause, “The shift to Cloud Computing: The impact of disruptive technology on the enterprise software business ecosystem,” Technological Forecasting & Social Change, vol. 129, pp. 308–313, 2018.

12. M. Giacobbe, R. Di Pietro, A. Longo Minnolo, and A. Puliafito, “Evaluating Information Quality in Delivering IoT-as-a-Service,” in Proceedings of the 2018 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 405–410, June 2018.

13. M. Osman, “A novel big data analytics framework for smart cities,” Future Generation Computer Systems, vol. 91, pp. 620–633, 2019.

14. Marrone, M. and Hazelton, J. (2019), "The disruptive and transformative potential of new technologies for accounting, accountants and accountability: A review of current literature and call for further research", Meditari Accountancy Research, Vol. 27 No. 5, pp. 677-694.

15. Osborne, Jason W. “Best practices in data cleaning: A complete guide to everything you need to do before and after collecting your data.” 2013

16. R. Lovas, A. Farkas, A. C. Marosi et al., “Orchestrated Platform for Cyber-Physical Systems,” Complexity, vol. 2018, Article ID 8281079, 16 pages, 2018.

17. R. M. Müller, H.-J. Lenz. 2013. Business Intelligence

18. R. Y. Zhong, X. Xu, E. Klotz, and S. T. Newman, “Intelligent Manufacturing in the context of industry 4.0: a review,” Engineering Journal, vol. 3, no. 5, pp. 616–630, 2017.

19. Steven Skiena. “The Data Science Design Manual”

20. TURBAN, EFRAIM ; SHARDA, RAMESH ; DELEN, DURSUN ; KING, DAVID: Business intelligence: a managerial approach. Boston, Mass. : Pearson, Prentice Hall, 2011 vismaster.eu/wp-content/uploads/2010/11/VisMaster-book-lowres.pdf

21. X. Wang, C. Xu, G. Zhao, K. Xie, and S. Yu, “Efficient Performance Monitoring for Ubiquitous Virtual Networks Based on Matrix Completion,” IEEE Access, vol. 6, pp. 14524–14536, 2018.

22. Y. Guo, Z. Yang, S. Feng, and J. Hu, “Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study,” Complexity, vol. 2018, Article ID 8496187, 21 pages, 2018.

23. Y. Su, X. Meng, Q. Kang, and X. Han, “Dynamic Virtual Network Reconfiguration Method for Hybrid Multiple Failures Based on Weighted Relative Entropy,” Entropy, vol. 20, no. 9, p. 711, 2018.

Information resources:

1. State Statistics Service [Electronic resource] Access mode:

2. National Institute for Strategic Studies. Official site. [Electronic resource] Access mode:

3. National Library of Ukraine named after VI Vernadsky Official site [Electronic resource] Access mode:

................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download