Basic information
| Study programme | Big Data Analytics |
|---|---|
| Higher Education Institution | RISEBA University of Applied Sciences |
| Study field | Economics |
All data
| Code of the study programme in accordance with the Latvian Education Classification | 45311 |
|---|---|
| EQF/LQF Level | 7 |
| Study Programme Type | Akadēmiskā izglītība (maģistra grāds), īstenojama pēc bakalaura vai profesionālā bakalaura grāda ieguves. Studiju ilgums pilna laika studijās viens–divi gadi. Kopējais pilna laika studiju ilgums vismaz pieci gadi |
| Study programme (short name) | Academic master study programme |
| Thematic group | Ekonomika |
| ISCED code | 0311 |
| ISCED | Ekonomika |
| Credit points | 60; 80 |
| Degree to be acquired | Master of Social Sciences in Economics |
| Qualification to be obtained | |
| Study type and form | Full time studies |
| Study lenght | 1 years, 6 months; 2 years |
| Language | latvian; english |
Licence information
| Licence number | 04037-35 |
|---|---|
| Licence date | 10.07.2018 |
| Licenced till |
Accreditation information
| Accreditation page number | 2021/35 |
|---|---|
| Accreditation date | 04.08.2021 |
| Accreditation duration (in years) | 6 |
| Accreditation till | 05.08.2027 |
Results
Knowledge:
1.In business processes related to entrepreneurial data:
1) Acquiring knowledge information technology components, the information systems development process and its management within a business, the business requirements and specifications for information systems. Students will also do an internship building IT infrastructure within a business.
2) Acquiring knowledge of forecasting modelling processes in business and rows of time, forecasting their future value,
3) Acquiring knowledge of risk management within a business, multi-dimensional risks ( including operational risk, business continuity risk, supply chain failure risk, project risk, cyber risk and diverse financial risks).
4) Acquiring in-depth knowledge of statistical modelling situations involving several variables, in business, including knowledge of multi-variation statistical methods, factor analysis and spatial and time data analysis, and mastering the skill of forming certain guidelines, in order to describe real situations within a business.
5) Acquiring knowledge of business data processing within a business, using the SPSS programme.
6) Students acquire an understand of the latest management development trends.
2. In Big data management:
1) Acquiring knowledge of the characteristics and types of Big data, obtaining and collecting it, data analytics mechanisms, as well as regarding data governance, and data strategy implementation and review within a business, as well as gaining an introduction to the fundamentals of machine learning, cognitive computing, artificial intelligence and Industry 4.0.
2) Acquiring theoretical knowledge working with data in relational databases, using SQL language
3) Students acquire knowledge of various database systems and their management, and regarding the use of the R language in work with Big data.
4) Acquiring knowledge of various Big data collection ( data mining) methods including CRISP-DM, cluster and discriminant analysis, and data mining on the Internet.
5) Acquiring knowledge of data governance and its various application, including the use of the Elasticsearch instrument in NoSQL databases, and Python language basics
6) Acquiring knowledge of machine learning, using the Python language.
7) Acquiring knowledge regarding various data visualisation methods, including time-determined and spatial data visualisation techniques and data visualisation designs.
3. Use of Big data in cutting edge technology and data security:
1) Acquiring knowledge of machine learning, machine learning in algorithms and a general description of Python,
2) Acquiring practicable knowledge of business platforms - completely new business models in microeconomics
3) Getting an introduction to the blockchain concept, which is studied in detail together with the support of cryptography technology
4) Acquiring knowledge of the nature of data security and protection, organisation of the circulation of electronic documents and various tools and systems for classified data storage
Skills
1.In business processes related to entrepreneurial data:
1) Doing an internship building IT infrastructure within a business.
2) Acquiring the skill of depicting time rows and
forecasting its future values, able to analyse and perform activities related to business optimisation and decision making.
3) Acquiring the skill to creating various Risk management models within a business.
4) Acquiring the skill of forming certain guidelines, in order to describe real situations within a business, and acquiring the skill of applying various strategic models and instruments in practice in various business situations.
5) Acquiring the skill of business data processing within a business, using the SPSS programme.
2. In Big data management:
1) Acquiring the skill of using the SQL language in work with data, including in business.
2) Acquiring the skill of using the R language working with Big data.
3) Acquiring the skill of applying various Big data collection (data mining) methods including CRISP-DM, cluster and discriminant analysis, and data mining on the Internet.
4) Learning to use the Elasticsearch instrument on NoSQL databases
5) Acquiring the skill to use various data visualisation methods, including time-determined and spatial data visualisation techniques and data visualisation design
3. Use of Big data in cutting edge technology and data security:
1) Acquiring the skill to use individual machine learning instruments (Anaconda,Pandas,Numpy, Matplotib, etc.)
2) Acquiring practical experience of developing an application prototype and placing it on the Apple iOS platform.
3) Mastering various blockchain usage methods.
4) Acquiring the skill of developing and preparing standard-methodological material in the organisation of data security.
Competences:
1. Proficient in various data (including Big data) collection, storage, processing analysis and visualisation concepts and theories, types, forms and models, as well as the relevant data processing instruments and opportunities for their application in business.
2. As a Data specialist is capable of working in various business projects and business management, if necessary forming collaborations with professionals in other industries and integrating knowledge from various fields in solving research problems.
3. Capable of conducting research work for the development of various theories and practices, in connection with data (incl. Big data) processing within a business, applying acquired management knowledge and using the latest information technologies and solutions.
4. Abilities to independently formulate, set and communicate professional activity objectives, in order to make innovations within their fields of activity, in data processing and analysis or inter-disciplinary realms.
5. Demonstrates a critically analytical approach, assessing contemporary economic processes, business development and social processes in society.
6. Able to independently acquire new knowledge and keep track of the development of ICT and economic processes after the end of studies, and able to use these creatively in research and practice, facilitating the development of their industry and its socioeconomic role.
1.In business processes related to entrepreneurial data:
1) Acquiring knowledge information technology components, the information systems development process and its management within a business, the business requirements and specifications for information systems. Students will also do an internship building IT infrastructure within a business.
2) Acquiring knowledge of forecasting modelling processes in business and rows of time, forecasting their future value,
3) Acquiring knowledge of risk management within a business, multi-dimensional risks ( including operational risk, business continuity risk, supply chain failure risk, project risk, cyber risk and diverse financial risks).
4) Acquiring in-depth knowledge of statistical modelling situations involving several variables, in business, including knowledge of multi-variation statistical methods, factor analysis and spatial and time data analysis, and mastering the skill of forming certain guidelines, in order to describe real situations within a business.
5) Acquiring knowledge of business data processing within a business, using the SPSS programme.
6) Students acquire an understand of the latest management development trends.
2. In Big data management:
1) Acquiring knowledge of the characteristics and types of Big data, obtaining and collecting it, data analytics mechanisms, as well as regarding data governance, and data strategy implementation and review within a business, as well as gaining an introduction to the fundamentals of machine learning, cognitive computing, artificial intelligence and Industry 4.0.
2) Acquiring theoretical knowledge working with data in relational databases, using SQL language
3) Students acquire knowledge of various database systems and their management, and regarding the use of the R language in work with Big data.
4) Acquiring knowledge of various Big data collection ( data mining) methods including CRISP-DM, cluster and discriminant analysis, and data mining on the Internet.
5) Acquiring knowledge of data governance and its various application, including the use of the Elasticsearch instrument in NoSQL databases, and Python language basics
6) Acquiring knowledge of machine learning, using the Python language.
7) Acquiring knowledge regarding various data visualisation methods, including time-determined and spatial data visualisation techniques and data visualisation designs.
3. Use of Big data in cutting edge technology and data security:
1) Acquiring knowledge of machine learning, machine learning in algorithms and a general description of Python,
2) Acquiring practicable knowledge of business platforms - completely new business models in microeconomics
3) Getting an introduction to the blockchain concept, which is studied in detail together with the support of cryptography technology
4) Acquiring knowledge of the nature of data security and protection, organisation of the circulation of electronic documents and various tools and systems for classified data storage
Skills
1.In business processes related to entrepreneurial data:
1) Doing an internship building IT infrastructure within a business.
2) Acquiring the skill of depicting time rows and
forecasting its future values, able to analyse and perform activities related to business optimisation and decision making.
3) Acquiring the skill to creating various Risk management models within a business.
4) Acquiring the skill of forming certain guidelines, in order to describe real situations within a business, and acquiring the skill of applying various strategic models and instruments in practice in various business situations.
5) Acquiring the skill of business data processing within a business, using the SPSS programme.
2. In Big data management:
1) Acquiring the skill of using the SQL language in work with data, including in business.
2) Acquiring the skill of using the R language working with Big data.
3) Acquiring the skill of applying various Big data collection (data mining) methods including CRISP-DM, cluster and discriminant analysis, and data mining on the Internet.
4) Learning to use the Elasticsearch instrument on NoSQL databases
5) Acquiring the skill to use various data visualisation methods, including time-determined and spatial data visualisation techniques and data visualisation design
3. Use of Big data in cutting edge technology and data security:
1) Acquiring the skill to use individual machine learning instruments (Anaconda,Pandas,Numpy, Matplotib, etc.)
2) Acquiring practical experience of developing an application prototype and placing it on the Apple iOS platform.
3) Mastering various blockchain usage methods.
4) Acquiring the skill of developing and preparing standard-methodological material in the organisation of data security.
Competences:
1. Proficient in various data (including Big data) collection, storage, processing analysis and visualisation concepts and theories, types, forms and models, as well as the relevant data processing instruments and opportunities for their application in business.
2. As a Data specialist is capable of working in various business projects and business management, if necessary forming collaborations with professionals in other industries and integrating knowledge from various fields in solving research problems.
3. Capable of conducting research work for the development of various theories and practices, in connection with data (incl. Big data) processing within a business, applying acquired management knowledge and using the latest information technologies and solutions.
4. Abilities to independently formulate, set and communicate professional activity objectives, in order to make innovations within their fields of activity, in data processing and analysis or inter-disciplinary realms.
5. Demonstrates a critically analytical approach, assessing contemporary economic processes, business development and social processes in society.
6. Able to independently acquire new knowledge and keep track of the development of ICT and economic processes after the end of studies, and able to use these creatively in research and practice, facilitating the development of their industry and its socioeconomic role.
Documents
| Document | Document type | Language |
|---|---|---|
| Expert / Experts joint report | Expert / Experts joint report | english |
| Self-evaluation report | Self-evaluation report | english |
| Self-evaluation report | Self-evaluation report | latvian |
| Study programme description (07.03.2018) | Study programme description | latvian |
| Expert / Experts joint report (22.06.2018) | Expert / Experts joint report | latvian |
History of study programme