UBA offers two distinct routes that help make this comparison concrete. The Licenciatura en Ciencias de la Computación focuses on the science and engineering of computing, with a five-year nominal path including the CBC and a later degree cycle. The Licenciatura en Ciencias de Datos is built around the intersection of mathematics, computing and data. Its current official study plan begins with CBC mathematics and a computing-oriented option, then proceeds to subjects such as Algebra, Algorithms and Data Structures, Analysis, Data Laboratory, Probability, Statistics and Data Science, and Operations Research and Optimization. It includes upper-cycle electives and a thesis or final project.
Choose Computer Science if you want to understand how computation works across a wide range of systems and software problems, or you have not yet settled on a specialization. Choose Data Science if you are most drawn to statistical inference, modelling, optimization and using data to answer questions across fields. The Data Science plan is not “less computing”: it includes algorithms and structured programming foundations alongside mathematical and statistical depth. Both require sustained quantitative work.
The decision also affects your course sequence. UBA’s Data Science plan is organized in three cycles and lets students shape a later path with electives; its official suggestions include data analysis, machine learning, natural-language processing and statistics. Computer Science’s plan combines theory, systems and a thesis milestone. Read the actual prerequisite chains and ask how often required modules are offered.
For international applicants, confirm Spanish proficiency, admissions eligibility, document recognition and fee category directly with the faculty. The programme pages do not promise an English-medium route or an international admission exception. Compare career goals too: CS can provide a broad route into software and systems, while data science aligns more directly with quantitative analysis and data-focused research, though neither degree guarantees a particular job.
Questions students often ask
Does Data Science still include programming? Yes. The plan lists Algorithms and Data Structures and Data Laboratory as well as statistical study.
Which has a thesis? UBA’s Data Science upper cycle explicitly includes a thesis or project; check the current CS plan for cohort-specific thesis requirements.
Can I switch after starting? Ask UBA about formal equivalencies and course recognition before assuming credits transfer.
Compare what the plans ask you to study
The first practical comparison is the balance among mathematics, programming, systems and statistical reasoning. The Data Science plan names Probability, Statistics, Data Laboratory and Operations Research alongside Algebra, Analysis, Algorithms and Data Structures. That combination points to a programme built around computational methods and quantitative models. The Computer Science plan uses a broader computing frame, with theory, systems, electives and a thesis. Read current subject descriptions to understand depth and sequence; titles alone do not show how much programming or mathematical proof a subject involves.
Create a table for each programme’s early subjects, prerequisites, electives and final project. If you are interested in machine learning, look for the mathematics and computing foundations that support it rather than choosing by label. If you have not settled on a specialism, compare how much room each plan leaves for later electives. Ask whether the listed options are regularly offered and how students choose them. A published elective menu is not evidence that every option runs each year.
Computer Science may fit students who want to investigate algorithms, operating systems, networks or compilers while keeping a broad range of technical paths open. Data Science may fit those who prefer combining data preparation, statistical reasoning, modelling and interpretation. These are tendencies suggested by the named curricula, not job guarantees. Both routes involve coding and quantitative work. Neither avoids foundational study.
Read the final project requirements before deciding. Ask how students find a supervisor, whether they may propose a topic, and what counts as an acceptable thesis or project. For Data Science, ask whether a project can combine a method with an application area you care about. For Computer Science, ask whether projects may be research-oriented or applied. The faculty must confirm what is available to your cohort.
To test the fit, choose several real job or postgraduate-study descriptions that interest you. Highlight their required skills, then map them to compulsory subjects, electives and project work in both plans. A data-analysis role may emphasize statistics and communication; software engineering may emphasize programming and system design. Those examples help compare curricula but do not predict hiring outcomes. Employers may also expect local-language ability, work authorization, a portfolio or experience that no degree page promises.
For international study, confirm Spanish proficiency, school-record recognition, admission dates and fees with the relevant faculty. Ask whether prior university study can be evaluated for credit and what evidence is needed. Request a dated reply that identifies the course and intake. If you need English-medium study, ask directly about teaching and assessment languages. Revisit the choice once the faculty confirms your entry route and the subjects available in the intake you can join.
A student with strong interest in a single application area should also test whether that interest is better served by a specialised project within Computer Science or by the Data Science curriculum as a whole. Ask each programme whether students can take relevant electives outside their home course and how such choices count toward graduation. Request examples of recent final-project topics from the faculty if they are publicly available. A project catalogue can reveal how students apply their methods, but it should not be interpreted as a promise that the same supervisor or topic will be available when you enrol.
Your preferred way of working can also distinguish the routes. Computer Science often asks students to reason about software and systems as engineered and theoretical objects; Data Science asks them to evaluate data, models and uncertainty as part of an analysis. The curricula cited here show overlap as well as distinction, so compare assessment methods and final projects rather than relying on labels. Ask how much students must explain assumptions, validate results and communicate findings. Those practices matter in both fields, but their relative emphasis may help you decide which course aligns with the work you enjoy.
For a more realistic test, choose one subject from each plan that you would be eager to study and one that may challenge you. Read the current programme descriptions and note the expected background, assessment and workload. This helps reveal whether your preference is based on the subject matter or just the programme name. If you are unsure, ask the faculty for course counselling and discuss how electives can preserve options while you build foundations. Any answer about course choices should be confirmed for your intake.
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Editorial note
Requirements can change and may differ by institution, programme and applicant. Recheck current university and government guidance before paying or travelling.
For comparisons, we assess the stated student brief, current route availability, discovery breadth, application ownership, progress visibility, support, costs and duplicate-application risk. Univs publishes this page and may have commercial relationships with institutions; those relationships do not determine admission decisions. Read our editorial standards.
