Data Analysis Technologies
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DepartmentFaculty of Fundamental Sciences
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Program code6121AX009
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Field of studyMathematical Sciences
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QualificationBachelor of Mathematical Sciences
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Duration1
Fun fact
Digital data is often called the “oil of the 21st century.” From your morning commute, to your latest Instagram story, to the readings on your fitness tracker—all of it is data waiting to be analyzed. Data scientists dive into these massive data lakes to find patterns, generate insights, and build smarter solutions that shape how we live, work, and connect.
We live in the age of big data—where every click, post, and search creates valuable information. Statistics is the key that helps us understand the past, make sense of the present, and even predict the future.
About
Programme Objective
We train highly skilled data analysis specialists with a strong foundation in mathematical statistics and informatics. You’ll learn to:
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collect and interpret data
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design statistical analysis models
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apply advanced forecasting and modeling techniques
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and gain hands-on experience through individual and group projects in real-world contexts.
Core Study Modules
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Multivariate Data Analysis
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Big Data Processing Technologies
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Statistical Software (R, Python, SQL)
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Mathematical Statistics
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Sampling Methods
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Time Series Analysis (with course project)
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What will I be able to do?
• Analyze and interpret statistical data in practical contexts
• Build models for forecasting and problem-solving
• Program confidently in R and Python
• Work with databases and manage large datasets
• Apply big data processing technologies to complex real-world challenges. -
What are my career opportunities?
• Pharmaceutical companies
• Banks and insurance companies
• Private companies in data-driven sectors
• Public institutions such as the State Data Agency
Career roles include Data Analyst, Business Intelligence Specialist, Risk Analyst, or even Head of a Data Analysis Department.
Admission requirements
“The knowledge I gained here gave me the confidence to enter the job market and the foundation to keep growing in both my career and academic path.”
Study subjects
1 Semester
obligatory
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FMSAB24218 6 credits
Programming in Python
Module aim
Provide students with the knowledge and skills that can enable to understand the peculiarities of programming Python, to know the syntax of Python programming language, to be able to write your own programs. Provide an adequate understanding of the core Python libraries and their application possibilities, which should enable students to use them for their intended purpose.
Module description
The module explores Python programming peculiarities. Introduces Python programming language syntax, types of data structures. The module provides the basics of programming Python, an introduction to functional programming. The main Python libraries are provided, their application options are reviewed.
Students must attend at least 51% of the lectures, at least 80% of the exercises. -
FMSAB16542 6 credits
Regression Analysis
Module aim
The aim of this course is to introduce models, principals and methods of regression analysis and related model, their applications and interprataion in econometric studies and to teach students to perform with computers statistical analysis of real economic data.
Module description
The basis of this introductory econmometric course is regression analysis and related models: dispersion and factor analysis. The aim of the course is to introduce principals of econometric analysis, main concepts and methods of regresion, dispersion and factor analysis, to teach students to apply this methods in real studies and correctly to interpret the results.
Students must attend at least 80 % of the time scheduled practical lectures and at least 80 % laboratory works. -
FMSAB20532 6 credits
Computational Statistics
Module aim
The aim of this course is to train students in statistical analysis of real data and in interpratation of resultsand to prepare him for e tuding of econometrics and other advanced statistical courses.
Module description
The aim of this course is to introduce principles, main problems and methods of statistical data analysis. The elements of robust statistica and Bayes appoach are also encompassed. R is used for practical statistical tasks.
Students must attend at least 80 % of the time scheduled practical lectures. -
FMSAB16338 6 credits
Probability Theory
Module aim
To provide basic knowledge about natural phenomena of stochastic nature and their mathematical models.
Module description
The first part of the course introduces the concepts of a random event and its probability, and defines a probabilistic space. The following is the classical definition of probability, the main properties of probabilities are proved. The second part of the course is devoted to the analysis of random variable distributions. The numerical characteristics of random variables are introduced in detail. The course introduces the law of large numbers, the central limit theorem.
Students must attend at least 51% of the lectures, at least 80% of the exercises.
2 Semester
obligatory
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FMSAB21641 9 credits
Time Series Analysis (with course project)
Module aim
The aim of this course is to introduce the main time šeries models and to consider their application in practice.
Module description
The main conceptions in creating time series statistical models are presented in this course. The questions of a model identification, fitting the models parameters, model diagnostic and forecasting are considered. Besides theoretical knowledge students have to solve tasks on the black board and using the special statistical program package for full statistical analysis.
Students must attend at least 80 % of the time scheduled practical lectures. -
FMMMB21602 6 credits
Big Data
Module aim
The goal is to introduce the basic numerical methods and to learn how to apply these methods for solution of specific problems.
Module description
In this course students learn the concepts of computer arithmetic and stability of numerical algorithms, numerical methods for solution of nonlinear equations and systems of equations, direct and iterative methods for solution of linear systems of equations, interpolation and approximation, numerical methods for solution of eigenvalue and eigenfunction problems, optimization methods, and numerical integration methods.
Students must attend at least 60% of the time scheduled practical works. -
FMSAB16674 6 credits
Data Classification Methods
Module aim
The aim of this course is to make students familiar with major concepts of classification analysis, to understanding the mathematics behind classification methods, to provide practical experience in solving data classification tasks.
Module description
Classification is one of the main tasks in statistical data analysis in biology, medicine, engineering and other fields. In this course, students are introduced to the basic principles of data classification, the most common types of tasks and methods of solving them: cluster analysis, discriminant analysis, etc.
Students must attend at least 80 % of the time scheduled practical lectures and at least 80 % laboratory works.