Data Economy and Big Data
- Kredity: 10
- Ukončenie: Skúška
- Rozsah: 16sP
- Semester: zimný
- Ročník: 1
- Podnikovohospodárska fakulta so sídlom v Košiciach
Vyučujúci
Zaradený v študijných programoch
Výsledky vzdelávania
The aim of the course is to provide students with knowledge in the field of data economics. The course deals with the analysis and processing of large data sets (Big data). Students should become familiar with the creation, structure and management of data warehouses, understand approaches to data mining as well as advanced knowledge related to neural networks.
Knowledge:
The graduates will gain knowledge related to the creation of data warehouses for Big Data and their management. They will also gain knowledge related to the processing, transformation, modeling, and evaluation of Big data (information, data) to identify the trends and to detect, interpret and share significant patterns in the data. The students will understand non-trivial models and tools of machine learning and neural network.
Competences:
The graduate will master the work with databases and data techniques of the processing and evaluation of big data. The graduate will be able to create and manage a data warehouse as well as use sophisticated techniques of data mining and knowledge discovery for the analysis of this data.
Skills:
The graduate will be able to analyze a large amount of data from business processes using statistical, database and data mining tools. They will know the techniques of pattern detection, classification, association, and prediction and be able to select appropriate procedures for the creation and validation of neural networks.
Stručná osnova predmetu
Big data
Big Data, Complexity of Big Data, Big Data Processing Architectures, Big Data Technologies, Big Data Business Value, Data Warehouse, Re-Engineering the Data Warehouse, Workload Management in the Data Warehouse, New Technology Approaches.
Data mining
Data and file formats (structured, unstructured, etc.), SQL and databases, Text processing (parsing, tokenizing, stemming, etc.), Data representation (feature vector representation, etc.) Need for data mining, Pre-processing: Dimensionality reduction, Missing values, Normalization & standardization, Noise and outlier detection. Pattern’s detection, classification, association and prediction techniques.
Machine learning
Basic concepts of Neural Networks, Characteristics of Neural Networks, Terminologies, Applications of the artificial neural networks. Supervised learning, Unsupervised learning, Re-inforcement learning. Knowledge Representation, Artificial Intelligence, Learning rules, Error correction learning, Memory based learning, Hebbian learning, Competitive learning, Boltzmann learning, Single layer perceptron, Multilayer perceptron, Back propagation, Recurrent networks, Network Pruning, Adaptive networks, Decision-based neural networks, Hierarchical neural networks, Probabilistic neural network, Radial basis function networks, Comparison of RBF Networks and Multilayer perceptron. Classification of linearly separable patterns, Boltzmann machine, Helmholtz machine, Support vector machines, Self organization maps, Genetic Algorithms, Prediction Systems.
Recommended software
R Studio, IBM SPSS Modeler.
Odporúčaná literatúra
1. TKÁČ, Michal - VERNER, Robert. Artificial neural networks in business: two decades of research. In Applied soft computing. - Amsterdam: Elsevier Science Publishers B.V. ISSN 1872-9681, 2016, vol. 38, pp. 788-804.
2. TKÁČ, Michal - VERNER, Robert - DANISHJOO, Enayat. Modern computation methods for business applications. Reviewers: Adrian Olaru, Jozef Mihok. 1. vyd. Vaterstetten: Adoram, 2013. 276 s. [13,85 AH]. ISBN 978-3-00-044092-2.
3. SHARDA, Ramesh; DELEN, Dursun; TURBAN, Efraim. Analytics, Data Science, & Artificial Intelligence. Pearson, 2020.
4. GOPAL, M. Applied machine learning. McGraw-Hill Education, 2019.
5. KOTU, Vijay; DESHPANDE, Bala. Data science: concepts and practice. Morgan Kaufmann, 2018.
6. SCHMARZO, Bill. The The Economics of Data, Analytics, and Digital Transformation: The theorems, laws, and empowerments to guide your organization’s digital transformation. Packt Publishing Ltd, 2020.
7. CARRIERE-SWALLOW, Mr Yan; HAKSAR, Mr Vikram. The economics and implications of data: an integrated perspective. International Monetary Fund, 2019.
8. TADDY, Matt. Business data science: Combining machine learning and economics to optimize, automate, and accelerate business decisions. McGraw Hill Professional, 2019.
9. GHAVAMI, Peter. Big Data Management: Data Governance Principles for Big Data Analytics. Walter de Gruyter GmbH & Co KG, 2020.
10. GHAVAMI, Peter. Big data analytics methods: analytics techniques in data mining, deep learning and natural language processing. Walter de Gruyter GmbH & Co KG, 2019.
11. ZHOU, Hong. Learn Data Mining Through Excel. Apress, 2020.
12. KUMAR, D. G.; KUMAR, G. D. Machine learning techniques for improved business analytics. 2018.
13. FINLAY, Steven. Artificial intelligence and machine learning for business. A No-Nonsense Guide to Data Driven Technologies, 2017,
Sylabus predmetu
Big data Big Data, Complexity of Big Data, Big Data Processing Architectures, Big Data Technologies, Big Data Business Value, Data Warehouse, Re-Engineering the Data Warehouse, Workload Management in the Data Warehouse, New Technology Approaches. Data mining Data and file formats (structured, unstructured, etc.), SQL and databases, Text processing (parsing, tokenizing, stemming, etc.), Data representation (feature vector representation, etc.) Need for data mining, Pre-processing: Dimensionality reduction, Missing values, Normalization & standardization, Noise and outlier detection. Pattern’s detection, classification, association and prediction techniques. Machine learning Basic concepts of Neural Networks, Characteristics of Neural Networks, Terminologies, Applications of the artificial neural networks. Supervised learning, Unsupervised learning, Re-inforcement learning. Knowledge Representation, Artificial Intelligence, Learning rules, Error correction learning, Memory based learning, Hebbian learning, Competitive learning, Boltzmann learning, Single layer perceptron, Multilayer perceptron, Back propagation, Recurrent networks, Network Pruning, Adaptive networks, Decision-based neural networks, Hierarchical neural networks, Probabilistic neural network, Radial basis function networks, Comparison of RBF Networks and Multilayer perceptron. Classification of linearly separable patterns, Boltzmann machine, Helmholtz machine, Support vector machines, Self organization maps, Genetic Algorithms, Prediction Systems. Recommended software R Studio, IBM SPSS Modeler.
Podmienky na absolvovanie predmetu
30% - active participation in colloquia, presentation of a selected topic
30% - research study
40% - final exam
Pracovné zaťaženie študenta
Participation in colloquia: 16 hours
Preparation for colloquia: 44 hours
Elaboration of a research study: 100 hours
Preparation for the final exam: 100 hours
Jazyk, ktorého znalosť je potrebná na absolvovanie predmetu
English language
Dátum schválenia: 20.12.2022
Dátum poslednej zmeny: 20.12.2022
Dátum schválenia: 20.12.2022
Dátum poslednej zmeny: 20.12.2022