Complex data analysis

The goal of this program is to provide students with the skills needed to become data analysts or data scientists, preparing them for a career in data science. By enrolling in this master’s program, you will aquire the following types of knowledge required in the field:

programming languages necessary for data management and database design – SQL (2 courses), Python (4 courses), R (three courses);
data visualization tools – specialized tools such as Excel, PowerBI, Tableau, ArcGis for graphs, dashboards, infographics and reporting forms;
predictive statistical analytics tools and dimensional analysis – tools such as R, JASP, Excel, ArcGIS, E-Views;
mastering machine learning algorithms (3 courses in Python) – data reduction and prediction algorithms, natural language processing, neural networks;

In terms of training level, the courses appeal to logical thinking and do not require prior knowledge of mathematics, statistics, or programming languages.

Objectives

knowledge on the collection and integration of diverse data to facilitate analysis;
development of skills for visualizing data and reporting analyses intuitively;
knowledge for identifying trends and co-dependencies;
development of the ability to use specific programming languages for data: SQL, R, Python;
development of skills in the use of machine learning algorithms;
knowledge on spatial analysis and time series analysis;
development of team coordination skills for project implementation.

Acquired competencies

Abilities:

managing organizational flows to use data in the business model;
organizational skills for data collection and integration;
the ability to coherently integrate dispersed data;
the ability to transform diverse information into data in various formats;
synthesizing and interpreting information from data;
understanding procedures for building predictive models;
the ability to graphically represent data;
the ability to represent geographic and time series data.

Professional skills:

understanding the process of collecting and organizing data;
the ability to integrate diverse data sets;
the ability to search in integrated data or database sets;
the ability to aggregate data of different sizes;
the ability to understand the connections in data sets;
the ability to visualize data in an understandable way.

Courses

1st Semester

Introducere în sisteme informaționale geografice / Introduction to Informational Geographical Systems

Bazele statisticii / Fundamentals of Statistics

Limbaje de interogare structurate / Structural Interogation Languages

Vizualizarea datelor / Data Visualisation

Practica profesională / Professional Practice of Speciality

2nd Semester

Analiză predictivă și a co-dependențelor / Predictive and Codependencies Analysis

Sisteme informaționale geografice avansate / Advanced Geographical Information Systems

Automatizarea și Integrarea Datelor in Power Platforms / Automation and Data Integration in Power Platforms

Limbaje de interogare structurate avansate / Advanced Structural Interogation Languages

Curs opţional de specialitate 1 / Elective Course of Speciality 1

3rd Semester

Metodologia cercetării sociale (Limba Engleza) / Social Research Methodology

Fluxuri de lucru pentru științele datelor / Workflows for Data Sciences

Machine learning: predicţie, clasificare şi clusterizare / Machine Learning: Prediction, Classification and Clustering

Programare statistică în Python / Statistical programming in Python

Stagiu practic de documentare în vederea elaborării dizertaţiei / Documentation for Master’s Thesis Preparation

4th Semester

Algoritmi AI avansati (limba engleză) / Advance AI algorithms (English Course)

Inginerie ML / ML Engineering

Serii de timp şi previziune / Time Series and Prediction

Elective Courses

PACHET OPȚIONAL 1 (An I, Semestrul 2)

Scriere academică și raportare de specialitate/ Academic Writing and Specialised Reporting

Etică și integritate academică / Ethics and Academic Integrity

Careers

sociology analyst, economic sociology analyst, urban sociology analyst

Educational plan

Academic curriculum Complex data analysis 2026-2027