Specialty F4 “Systems Analysis and Data Science”: A Story from IASA KPI

Why systems analysis?

Systems analysis and data science is an extremely interesting and sought-after field, because it combines an engineering approach with deep methods of modelling and optimisation. Today, in the era of big data, the Internet of Things and AI, a systems analyst can design complex ecosystems: from “smart” cities and power grids to medical diagnostic platforms. Their tools make it possible to predict how systems behave, minimise risk and save resources, which is critical for business, public administration and science. That is why this niche is not only relevant but one of the key drivers of technological progress worldwide.

What are IASA and systems analysis about?

The “Systems Analysis and Data Science” specialty at the Institute for Applied System Analysis (IASA) of Igor Sikorsky Kyiv Polytechnic Institute combines deep mathematical subjects with practical programming and modelling skills. The methodology of systems analysis lets you formalise complex business processes, predict how they will behave and optimise decisions — something that is particularly valued in IT, finance, stock trading and even the military sphere.

The key strength of IASA is the combination of theoretical grounding and practical cases: students study not only discrete mathematics and linear algebra but immediately apply that knowledge to build models in a computer environment.

Which subjects do systems analysis students take?

• Years 1–2: Foundations and algorithmic thinking.
  The first years lay the base without which you cannot become a high-level engineer or a data specialist.
  Fundamental mathematics (mathematical analysis, linear algebra and analytic geometry, discrete mathematics, differential equations, probability theory and mathematical statistics, functional analysis, numerical methods).

How it is used at work: this is the language Data Science and Machine Learning “speak”. Eigenvectors, matrix computations and function optimisation lie at the heart of any neural network. Understanding these subjects lets you go beyond calling ready-made libraries and genuinely understand how the algorithms work. Probability theory is the basis for building predictive models, A/B testing and risk assessment.

Programming and computer science (algorithms and data structures, programming and algorithmic languages, object-oriented programming, graph theory, computer systems architecture).