The purpose of this program is to help students learn the skills required to work as a data analyst or data researcher and build a career in data science. This master covers three type of knowledge required in the field:
(1) Programming languages required for data management and construction of data warehouses: SQL (1 course), Python (4 courses), R (three courses); Power Query (1 course).
(2) Visual data exploration tools: types of graphs, dashboards, infographics and types of reporting using specialized tools such as Excel, PowerBI, Tableau, ArcGis.
(3) tools for predictive statistical and dimensional analysis: R, JASP, Excel, ArcGIS, RapidMiner.
(4) mastery of machine learning algorithms (3 courses in Python): algorithms for data reduction and prediction, natural language processing, neural networks.
The courses provided by this master’s program will cover aspects of population segmentation theory at urban, regional, national and global levels, as well as theories of segmentation processes.
The courses require logical thinking;
No prior knowledge of statistics is required;
No knowledge of mathematics is required;
No prior knowledge of programming languages is required.
Over the past fifteen years, the service sector has recorded gradual growth, so today it generates a quarter of Romania’s gross domestic product. Services exports have quadrupled in the past ten years. The fastest growing subsector in this sector is information technology, which accounted for 9% of GDP in 2019 and is the second largest contributor to GDP growth (1%). In 2017, the field had about 120,000 employees. Cluj and Bucharest centralize half of the country’s IT operations.
The field of data analytics is increasingly important in these service activities. Both those with knowledge of programming languages and database queries (hard statistics) as well as those with skills in computer science and soft statistics work in this field. There are three types of data science-related careers available, and this program prepares students to work as data analysts and data researchers.
|Data analyst||Excel, SQL, Tableau/Qview, Power BI||Cleaning and organizing raw data; using descriptive statistics to get an overview of the data; Trend analysis; creating data visualizations and dashboards; Presenting the results of a technical analysis in an intelligible way to non-specialists.|
|Data researcher||Python, R, SPSS/SAS, Tableau||Evaluation of statistical models to determine the validity of the analysis; Using machine learning to build better predictive algorithms; Testing and continuous improvement of the accuracy of machine learning models; Creating data visualizations to summarize the conclusions of an advanced analysis.|
|Data engineer||Hadoop, Hive, Java Script, C++, Amazon Cloud||Construction of applications for data consumption; Integrating external or new data sets in existing repositories; Transformation of new data for machine learning models; Continuous monitoring and testing of systems to ensure optimum performance.|
- Data collection and structuring in an organization;
- Data synthesis and aggregation;
- Discovering trends and co-dependencies;
- Integration of various data to facilitate the analysis;
- Finding the necessary data in large data packets;
- Intuitive data visualization and analysis reporting;
- Visualization of online traffic and users.
- Organisational flow management in order to use data in the business model;
- Organisational skills to collect and integrate data;
- The ability to coherently integrate dispersed data;
- The ability to turn various information into data, in various formats;
- Synthesize and interpret information from data;
- Understanding the procedures needed to build predictive models;
- Ability to represent data graphically;
- The ability to represent geographic data in time series.
- Understanding the process of data collection and organization;
- The ability to integrate various data sets;
- The ability to search within integrated data or databases;
- The ability to aggregate data of different sizes;
- The ability to understand the links within data sets;
- Possibility to view data in an intelligible manner.
1st semester – DATA EXPLOITATION
Data visualization - Norbert Petrovici/UBB - Sociology Structured Query Languages: SQL Introduction - Petru Varlan/Betfair The basics of statistics in R - Ionut Foldes/UBB- Sociology Introduction to GIS - Ciprian Moldovan/UBB - Geography
2nd semester – DATA ANALYSIS
Predictive analysis and co-dependencies in R - Cristian Pop/ UBB - Sociology Advanced GIS - Titus Man/UBB - Geography Times series forecasting - Codruta Mare/UBB - FSEGA Predictive models with game theory in R - Adrian Ludusan/UBB - European Studies Bibliographic documentation for the dissertation
3rd semester – DATA AND POPULATION SEGMENTATION
Social research methodology - Anca Simionca/UBB Sociology Statistical programming in Python - Radu Lazin/ 8x8 Machine learning: prediction, classification and grouping - Cristian Gabriel Fălcuțescu/Romanian Institute of Science and Technology The political economy of big data - Irina Culic/UBB-Sociology Dissertation research
4th semester – CONNECTIVITY ANALYSIS
Machine Learning: Natural language processing - Daniela Manate/Globant Machine Learning: Neural networks with supervised learning - Rareș Roșca/Tapptitude Advanced SQL - Petru Varlan/Betfair Academic writing, ethics and deontology in social sciences - Norbert Petrovici/UBB - Sociology
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