The second-cycle studies in Advanced Analytics – Big Data at SGH Warsaw School of Economics provide advanced knowledge and competencies in the area of the extraction and analysis of data from variety of sources.
- Career prospects
A graduate of Data Analysis - Big Data is a comprehensively educated Data Scientist, sought after on the domestic and foreign labor market. Can work in many areas and sectors of activity, social, economic, business, government, local government, non-governmental (NGO’s), local government, science and education, non-profit organizations, corporations and international organizations; in analytical, consulting, ICT, insurance, banking and telecommunications companies, for example in teams such as:
- Teams for managing any automatic and mass process,
- CRM management and analytical marketing teams,
- Analytical teams in debt collection companies,
- Analytical teams supporting the sales process,
- Credit risk management teams at banks, etc.
The studies also prepare students to conduct research and to undertake third-cycle studies.
- Mode and duration of study
Mode of study: full-time
Duration: 2 years (4 semesters)
- Admissions and tuition
The application and admissions procedure has the following stages:
- registration in the online system,
- submission of required documents,
- end of submission of documents period,
- verification of documents by SGH,
- issuing decisions on admission,
- information about registration for classes sent by Master Dean’s Office (in the registration system).
More information can be found here:
1900 EUR per semester
A graduate of the second-cycle studies in the field of Advanced Analytics - Big Data at the SGH Warsaw School of Economics is a comprehensively educated Data Scientist in demand on the labor market in Poland and abroad, having knowledge, skills and social competences in the field of:
- has advanced knowledge in the field of computer science regarding the methods of generating, collecting, storing and processing structured and unstructured data,
- has knowledge of how to define an IT structure for a given business process and understands the essence of extracting knowledge from complex data structures,
- has advanced knowledge: about mathematical, statistical, econometric and IT methods and tools for data analysis, about methods and tools for building forecasting and simulation models with reference to social, economic and business phenomena, where such knowledge can be used,
- knows the possibilities and areas of application of data analysis in batch and real-time mode and understands the business needs of making decisions in a very short time,
- knows the mathematical foundations of modeling economic, economic and business processes using the methods of artificial intelligence, machine learning and deep learning,
- has knowledge of supervised and unsupervised methods, reinforcement learning and is able to combine various methods to achieve the best results of analysis and modeling, knows the IT technologies used for analyzes on desktops, clusters and in cloud solutions,
- has knowledge of programming with the use of programming languages: Python, SAS, R, SCALA.
- distinguishes between structured and unstructured data and is able to obtain and process data from various sources (databases, text files, multimedia files, websites, social networks, sensory and geolocation data),
- can solve the problems of scalability of IT systems and can prepare an IT solution such as a data warehouse or a data lake for the processing of tabulated and unstructured data,
- can choose an IT structure for a given business process, can process data in the ETL procedure and in real time, can build queries for SQL and non-SQL databases, prepare data for analysis,
- is able to implement decision-making rules in a simplified programming environment and conduct a simulation process to verify the correctness of their operation and build data analysis models based on various statistical, mathematical and IT tools, as well as formulate judgments and draw conclusions based on them,
- can manage the process, where data-driven decision making is made in a fully automatic way, including identifying bottlenecks in the process and empty runs; can manage the entire life cycle of the predictive model,
- is able to optimize the decision-making process by setting its parameters in a way that allows maximization of financial indicators,
- can use the sampling method to analyze large volumes of data, conduct statistical and econometric analyzes in modeling economic phenomena and processes, analyze and model multidimensional data, apply them in economic, social and business (market and marketing) research.
- understands the need and can use a quantitative approach for a better perception, description and analysis of the surrounding reality of economic, social, business,
- is aware of professional responsibility at work in business entities and institutions where it is required to use mathematical, statistical,
- econometric and IT tools,
- can justify the needs of building predictive models and an automatic process using business language and financial indicators,
- can communicate in the business environment, convincing with his passion and commitment to the use of advanced data analysis,
- teaches others humility to analyze data, the need for a thorough understanding of the process and identification of estimation errors, and also gives an example of ethical professional behavior,
- has the competence to work in teams in data science teams,
- has the readiness and skills to use knowledge from data in practice in order to transform processes and increase the innovation of the organization.
The list of learning outcomes and programs for the first and second cycle programs at SGH