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Where I live, there is often a limited understanding of what Computer Science truly entails. It is frequently perceived as simply the "Science of Computers" (or just "Computers" for short), which creates confusion among young students and their parents. However, Computer Science is fundamentally about Computing Science—as the famous quote goes, "Computer Science is no more about computers than Astronomy is about telescopes".
If you love programming but have little interest in theoretical foundations, computational models, or experimental evaluation, a degree program in Computer Science may not be the best fit for you. On the other hand, if you are eager to develop a scientific mindset, immerse yourself in a vibrant campus ecosystem, and satisfy your curiosity to expand your intellectual horizons, then a Computer Science degree could be an excellent choice!
Despite being a relatively young discipline, Computer Science has matured significantly. Its achievements are deeply embedded in our daily lives and often intersect with other sciences in a highly multidisciplinary way, driving innovation and collaboration across fields.
Tip
Tip
Dropout rates often peak during two critical transitions: between the first and second semester of the first year, and between the first and second year. This is partly due to the ambiguity surrounding what students expect from their program, as previously mentioned, and partly because of the significant "paradigm shift" that occurs when transitioning from high school to university.
In university, the environment changes dramatically—classrooms are larger, and the familiar structure of weekly written or oral exams disappears. This shift often leads students into a psychology of procrastination, where they operate under the illusion that there is always plenty of time to catch up. However, this mindset can quickly derail academic progress...
Success in this new environment requires a proactive approach. Students must learn to organize their time effectively by attending lessons consistently, keeping their notes in order, studying regularly rather than cramming, and engaging deeply with course materials through homework and additional resources. By building these habits early, students can overcome the challenges of the transition and set themselves up for long-term academic success!
📚 Teaching is a big part of what I do! Here are my current courses:
📚 Teaching is a big part of what I do! Here are my current courses:
🧠 Deep Learning, M.Sc. in Data Science
Deep learning is still more like alchemy than chemistry—a blend of intuition, experimentation, and a bit of magic—but it's precisely this "alchemy" that powers AI as the transformative technology it is today. In this course, students dive into both the theoretical foundations and the hands-on applications of deep learning, exploring how neural networks and advanced architectures drive innovation across fields.
🤖 Computational Intelligence, B.Sc. in Computer Science
This course explores the development of intelligent systems capable of adapting, learning from experience, and solving complex problems—essentially, systems that can perform tasks requiring human-like intelligence. Co-taught with Professor Corrado Mencar, my focus is on foundational neural networks, providing students with the basics of machine learning and its applications.
💻 Computer Science Laboratory, B.Sc. Programs in Computer Science (Bari and Taranto)
The course bridges the gap between programming theory and practical software engineering skills. It emphasizes coding fundamentals, allowing students to move from theory into practical application, building a foundation essential for advanced studies and industry practice.
🤔 The answer to the ultimate question of life, the universe, and everything: Computer Science or Computer Engineering?
🤔 The answer to the ultimate question of life, the universe, and everything: Computer Science or Computer Engineering?
This distinction is not universal, but in Italy, it's a common one—and a question I frequently get from first-year students. (This is likely due to the confusion mentioned earlier.)
Short answer: In the long run, they're basically the same, and your professional career depends primarily on you.
Long answer: The distinction between Computer Science and Computer Engineering arises mainly for historical reasons. These two curricula have roots in different academic communities with distinct "pedigrees". Formally, their contents overlap, and their job opportunities are largely the same. However, there are some differences:
Computer Science programs typically focus more on programming, programming languages, and related areas. This often means more hands-on practice. Students here tend to view programming as an end in itself, moving from theory to practice and often adopting a more scientific mindset.
Computer Engineering programs, on the other hand, are often broader in scope and may cover topics beyond programming, making the field more generic. These students often see programming as a means to an end, focusing on problem-solving and developing an engineering mindset, where practicality may trump abstraction.
Both paths aim to provide foundational knowledge, but success ultimately depends on your curiosity, dedication, and willingness to go beyond the basics. You'll need to cultivate soft skills, learn to promote and enhance your work, and consistently push beyond your limits to stand out.
Sadly, there's often a misconception outside academia that computer engineers are superior to computer scientists. This simply is not true—each has its strengths, and the career you build depends more on how you apply your skills than the title on your diploma.
🤔 Another fundamental question: Data Science or Artificial Intelligence?
🤔 Another fundamental question: Data Science or Artificial Intelligence?
As universities increasingly offer second-level degrees and specializations in these two disciplines, this question arises frequently—especially toward the end of a first-level degree.
Short answer: While they are not exactly the same, Data Science and Artificial Intelligence professionals can synergistically complement each other in research and work teams.
Long answer: Data Science sits at the intersection of multiple disciplines, focusing on analyzing data to extract knowledge. It draws students from diverse backgrounds, including Mathematics, Statistics, Economics, and Computer Science, creating highly heterogeneous classes. Artificial Intelligence, on the other hand, is a much broader discipline. It goes far beyond the popular domains of machine learning and deep learning, encompassing areas like reasoning, planning, and more. AI classes tend to consist primarily of Computer Science students.
Similar to the distinction between Computer Science and Computer Engineering, Data Scientists and AI specialists often approach their work differently. Data Scientists typically see algorithms and computational techniques as tools to accomplish broader goals, such as managing the full data processing pipeline—including legal and privacy considerations. In contrast, AI specialists often focus on algorithms and techniques as their primary area of interest, with a deeper engagement in theoretical, ethical, and even philosophical aspects of intelligence.
Despite these distinctions, the two disciplines are increasingly in demand and often overlap in terms of skills and tasks. Their ability to complement each other stems from their different focuses, making them valuable collaborators in both academia and industry.
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