La Master's Degree in Data Science – Regional Edition Rosario It approaches the discipline from a rigorous and applied perspective, aimed at developing judgment in working with data.
Throughout the program, the focus is on how to formulate problems, model solutions, and use information as consistent support for decision-making. blended mode This process is accompanied by a combination of face-to-face and virtual sessions to promote technical exchange and continuity in study.
A comprehensive education: The proposal integrates statistics, modeling, databases, mining, and machine learning as parts of a single analytical process. The goal is to understand how these dimensions interact in solving complex problems, avoiding fragmented approaches and fostering a systemic view of the discipline.
Fundamentals and application: Each tool and technique is approached from its conceptual foundation to its practical implementation. The focus is not only on developing models, but also on understanding when to use them, how to validate them, and how to interpret their results in relation to the organizational dynamics in which they are embedded.
A modality designed to support learning: The combination of in-person and virtual learning is not merely an operational decision, but a pedagogical one aimed at optimizing the learning experience. In-person classes foster interaction and collaborative work, while virtual spaces allow for deeper exploration of content and the ability to maintain a consistent pace of study alongside professional activities.
“The Master's in Data Science was a turning point in my career.”
At the time, I was working in the banking sector and wanted to learn more about the world of data. The most valuable aspect was the solid and structured theoretical foundation, which allowed me to understand data science and its fundamentals in depth, along with the networking opportunities with professionals from diverse fields.
Thanks to that training, I was able to make a professional leap towards roles related to data, work with business teams, generate insights for decision-making, and develop advanced models applied to real problems.
I recommend it for its academic excellence, its teaching staff, and its comprehensive and interdisciplinary approach.
“Data science is not just about building a predictive model.”
The most valuable thing I gained from the master's program was understanding that working with data involves much more than developing a model. I learned to manage large volumes of information, combine different databases, and analyze them in depth to truly understand what's happening before defining a solution.
Today I feel prepared to tackle challenges related to automation and data analysis with greater confidence. The master's degree gave me concrete tools to approach different professional scenarios with confidence.
I would recommend it for the variety of topics it covers, the constant practical application through exercises and real cases, and the faculty's technological resources that make the classes dynamic and up-to-date.
“The Master's in Data Science provided me with a solid foundation in data analysis and an interdisciplinary work experience close to professional reality.”
The most valuable aspect was combining technical learning with the dynamics of working in interdisciplinary teams, authentically reflecting the challenges of the working world. This learning allowed me to improve the quality of financial and accounting analysis, automate routine processes, and optimize time.
I would especially like to highlight the academic excellence, the human qualities of the teaching staff, and the network of contacts that is built along the way, factors that enhance professional development in a rapidly growing discipline with a concrete impact on current job opportunities.
“The master’s degree opened the doors to a world where data speaks.”
Throughout the course, I learned not only new and innovative tools, but also a different way of observing, processing, and studying large volumes of information. Whether in the form of images, lengthy texts, or tables, I understood that data is present in every field and that knowing how to listen to it, along with an understanding of the situation, allows for more informed decisions with a faster and more effective impact.
I especially appreciate the program's structure: we began by studying time-tested and validated ancestral techniques, and then progressed to more modern methods, including some launched on the market during the same year we took the course. I also value having been able to study in top-notch facilities with appropriate equipment, which made the experience comfortable and accessible.
In a field as dynamic as data science, continuous review of course content is essential. Student feedback was always well-received, and the authorities acted accordingly, which I consider a positive aspect of the program.
“I learned to structure data projects rigorously, from defining the problem to deploying the model.”
The most valuable thing I gained from the master's program was the incorporation of a rigorous methodology for developing data projects, from formulating the research problem to deploying and validating deep learning models. I learned that the real impact lies in understanding the context, working with high-quality datasets, and designing reproducible experiments.
I applied this knowledge to computer vision projects, where I developed an object detection system to identify and report empty spaces in parking lots. I am currently working on a project focused on detecting forged handwritten signatures, also using computer vision techniques and Gabor transforms.
Furthermore, my training in neural networks allowed me not only to apply existing models, but also to adapt architectures such as CNNs, ResNets, and visual transformers to specific problems, justifying each design decision with solid theoretical foundations.
I would recommend this master's program to those seeking training that combines academic rigor with practical application. The program provides tools to tackle complex problems from start to finish: from hypothesis formulation and data preparation to production deployment. In my case, the key difference was the opportunity to delve deeper into neural networks and computer vision, developing a structured analytical mindset for working with real-world data.
"With the Master's degree, I discovered new ways to analyze information, understand programming languages, how to store data correctly, and be more efficient at work. I apply the new tools I learned about 50% of my current work time."
Data science has evolved rapidly in recent years. Today, numerous tools, languages, and models are available, and access to technical knowledge is increasingly widespread. However, this same expansion has made it more complex to integrate concepts, validate results, and support well-founded decisions.
Working with data does not only involve implementing algorithms or automating processes. It requires understanding the assumptions behind each model, evaluating the quality of the information, interpreting results with sound judgment, and linking the analysis to the organizational dynamics in which it is embedded..
In this context, We designed a training program that proposes a path of in-depth study and conceptual order, aimed at integrating knowledge, contrasting approaches, and sustaining analysis with greater technical consistency.The master's program seeks to offer an academic framework that allows for the consolidation of criteria and the expansion of professional development opportunities in a field that demands ever greater rigor.
Throughout the master's program you will to delve deeper into the statistical and computational foundations that underpin data scienceUnderstanding how analytical models are built, validated, and interpreted in different organizational contexts.
You will incorporate advanced analytics tools and methodologies, data mining, and machine learning.Working with real datasets and applied problems that require rigorous formulation, modeling, and technical evaluation.
You will develop criteria for integrate information from different sources, evaluate data quality, and link quantitative analysis to decision-making in diverse professional fields.
Also You will address the legal and ethical aspects related to the use of informationincorporating a responsible perspective on the impact of models and algorithms on organizations and society.
The academic program is organized into stages that combine foundational concepts, in-depth technical study, and integrated application. As the program progresses, the content focuses on the construction, validation, and implementation of analytical models.
Data science is built at the intersection of different areas of knowledge. Statistics provides the framework for modeling and validating results; computer science and databases allow for structuring and processing large volumes of information; machine learning expands predictive capabilities; and organizational understanding connects analysis with decision-making.
The curriculum design integrates these dimensions in a coherent manner, avoiding isolated approaches and fostering a cross-cutting understanding of the issues. This convergence allows for technically sound analyses that are also relevant to the professional environments in which they are applied.
The master's program is taught by alternating one week in person and one week virtually. The face-to-face meetings are held on Fridays from 14 to 18 pm and on Saturdays from 9 am to 13 pm.
face-to-face classes They are geared towards the technical development of the content, solving exercises, and direct interaction with teachers and classmates. Classroom work fosters discussion of approaches, collective analysis, and the sharing of solutions.
The cohorts are small in size, which facilitates participation, academic monitoring and direct interaction with the teaching team, including personalized support in the development of the Final Project.
The classroom brings together professionals from fields such as computer science, statistics, engineering, administration, economics, health, and agriculture, among others. This diversity of profiles allows problems to be addressed from different technical and business perspectives, enriching the analysis and discussion.
virtual instances They complement the process, allowing for a deeper understanding of concepts, progress in practical work, and maintaining momentum between in-person meetings. Throughout the program, the labs and assignments integrate modeling, analysis, and application, consolidating a consistent and progressive work dynamic.
The master's degree is part of the Data Science area of the Faculty of Engineering of the Universidad AustralIt is a space with a long history of postgraduate training, research, and applied knowledge transfer. The program maintains a consolidated and coherent academic structure across its campuses, with updated content and a curriculum that has been sustained over time.
The faculty comprises professors with advanced academic training and professional experience in data analysis, modeling, artificial intelligence, and management. Part of the team is shared with the Buenos Aires campus, ensuring continuity of academic standards, while local coordination in Rosario ensures close monitoring and coordination with the cohort dynamics.
The title awarded is that of “Master's Degree in Data Mining and Knowledge Management”, issued by the Universidad Austral, with official recognition and national validity granted by the Ministry of Education (Resolution No. 2781-15) and career categorized by CONEAU.
This proposal is not an isolated offer, but part of a consolidated academic area that articulates teaching, research and links with public and private organizations in the field of data science.
The presence of these organizations reflects the concrete application of training in diverse professional environments.
To apply for the master's program, you need:
The admissions process allows for the evaluation of the applicant's academic and professional profile, ensuring the coherence of the group and the technical level of the cohort.
La Universidad Austral has exclusive enrollment benefits regarding current values. These include: corporate agreementsSouthern Community, distance benefits, early registration, among others.
They are subject to quotaFor more information, please consult with the executive in charge.
Doctor of Exact Sciences (Computer Science). Director of the Data Science Area of the Faculty of Engineering of the Universidad AustralFull Professor at UBA, UNLP and Austral, researcher and reference in databases, data science and technology transfer.
Master's and Bachelor's degree in Statistics, specializing in data analysis and research. Experience in interpreting information for strategic decision-making. Part of the Data Science department at the main campus. Rosario de Universidad Austral.
Actuary and PhD in Economics (UBA), specializing in risk management, financial modeling, and machine learning applied to finance. Director and postgraduate lecturer with extensive experience in banking, capital markets, and the development of quantitative models for strategic decision-making.
Computer Engineer and MBA, with over 20 years of experience in Data Mining and Data Science, combining postgraduate teaching and applied consulting. Full Professor and thesis advisor in Data Science master's programs at the Universidad AustralITBA and UTN, with a focus on applications to economics, finance and analytical marketing.
PhD in Computer Science, specializing in AI and Data Science, with over 25 years of experience in applied research, industry, and university teaching. Research Scientist Director at Voolkia Software & Services, leading Machine Learning, Deep Learning, and Blockchain projects for sectors such as Oil & Gas, Fintech, and Insurtech.
Senior Data Scientist & ML/AI Engineer with over 15 years of experience in predictive modeling, deep learning, NLP, and MLOps. ML & AI Manager at Scanntech, and lecturer at [institution name missing]. Universidad Austral and National University of RosarioHe led AI teams and solutions at scale in fintech and tech companies.
Doctor of Education from the University of Navarra, Bachelor of Economics from the University of Buenos Aires, and specialist in Economic History. Professor of History of Economic Thought, Ethics, Business and Society at the Universidad Austral, with publications in economic history and personalist philosophy.
Data Scientist with over 10 years of experience in advanced statistical analysis and predictive modeling, focusing on fraud prevention at companies like Mercado Libre and Equifax. Master's degree in Analytics from the University of Chicago, with a solid academic and research background in multivariate statistics at UNR.
PhD in Political Science and in Humanities and Arts (UNR), CONICET Researcher and President of the Society for Kantian Studies in Spanish. Professor of Philosophy, Anthropology and Business Ethics at the Universidad Austral.
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