The boom of professionals in the data field brings up some questions: where to study and which path to follow?
After reviewing the different data profiles that range from the most technological ones to the most business orientated (see image below), one needs to decide on an offer on the academic market.
Below, you can read about the different offers available to study Data Science, Analytics, Data Science and AI, along with the places where you can study.
Undergraduate degrees – Data Science
Recommended for those young profiles who are interested in the world of data, programming and statistics.
Postgraduate degrees – Data Science
Recommended for those who already have an university degree and want to complement their profiles with an orientation towards data processing.
- UBA: Master in Data Exploitation and Knowledge Discovery
- ITBA: Master and Specialization in Data Science
- UDESA: Master in Data Science
- Austral: Master in Data Science
- Di Tella: Master in Management + Analytics
- UCAECE: Master in Data Science and Business Innovation
- UADE: Diploma in Data Science
- UNAJ: Superior Diploma in Data Science
Specific data-related professions
Recommended for those looking for something more disjunctive and technical. Lots of programming and systems.
- UDESA: Artificial Intelligence Engineering
- UP: Bachelor of Artificial Intelligence
- ITBA: Big Data, Innovation School Program
- ITBA: Cloud Data Engineering, Innovation School Program
- UNLP: Specialization on Data Intelligence oriented to Big Data
- Data Science Institute Argentina
- Siglo 21: Artificial Intelligence and Robotics Engineering
Short online courses
Recommended for those who seek to enter the data world quickly. It is suggested to combine them with work experience, and/or an academic degree.
They give a deep and intense dive in a short time. Recommended for those who want to reinvent themselves professionally or direct their career towards the data sector. Requires a considerable investment of time
There are multiple choice, which one will you pick?
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