The document outlines a master's degree program in Computer Science with a specialization in Data Science at the Ukrainian Catholic University, which requires foundational knowledge in mathematics, programming, and data structures. The 15-month curriculum includes courses in advanced programming, algorithms, databases, and data science topics such as machine learning, data visualization, and big data. Graduates will be equipped with skills needed for careers in computer science and data science, adhering to industry standards.

1. Master Degree in Computer Science
with specialization in Data Science
Ukrainian Catholic University, Lviv

2. Prerequisites
● Knowledge of the calculus (function, differentiation,
integration, series), basics of linear algebra (vectors,
matrices, linear equation systems)
● Satisfactory knowledge of C/C++ or Java, or Python, or
C#, Object Oriented programming
● Basic data structures: arrays, trees, lists, stack, queue
● Basic knowledge of relational databases, SQL
● Discrete math: sets, relations, boolean algebra, graphs,
basic algorithms on graphs
● Good to know: Statistical background (distributions,
Bayes theorem), Basic proficiency in R, Matlab (Octave)

3. Program duration
● 15 months
● 3 semesters with 7 study sessions
● Study session – 3 days (Thu, Fri, Sat) every other week
● Study day – 10 study hours

4. Two streams
Graduation skills
● Computer Science
– A graduate should fit
Google requirements
for interview
● Data Science
– The program was built
on the basis of “Data
Science Metro Map”

5. Computer Science Graduate Skills
● Coding: C++ or Java, C and Python, “...Object Orientated
Design and Programming, how to test your code...”
● Algorithms: bottom-up and the top-down Algorithms, Sorting
(plus searching and binary search), Divide-and-Conquer,
Dynamic Programming / Memorization, Greediness, Recursion
or algorithms linked to a specific data structure, A*, Dijkstra
● Data structures: Arrays, Linked Lists, Stacks, Queues, Hash-
sets, Hash-maps, Hash-tables, Dictionary, Trees and Binary
Trees, Heaps and Graph
● Mathematics

6. Computer Science Graduate Skills (cont.)
● Graphs: algorithms for distance, search, connectivity, cycle-
detection, the basic graph traversal algorithms, breadth-first
search and depth-first search etc.
● Operating systems: processes, threads, concurrency issues,
locks, mutexes, semaphores, monitors
● System design: features sets, interfaces, class hierarchies,
distributed systems, designing a system under certain
constraints, simplicity, limitations, robustness and tradeoffs

7. Data Science Graduate Skills
● http://nirvacana.com/thoughts/becoming-a-data-scientist/
– Fundamentals
– Statistics
– Programming
– Machine Learning
– Text Mining / Natural Language Processing
– Data Visualization
– Big Data
– Data Ingestion
– Data Munging
– Toolbox

9. Curriculum (with timeline)

10. Computer Science Courses

11. Computer Science 1
● Advanced Programming, 16 lectures
– Messaging concept, Method lookup & dispatch, Principle of
Least Knowledge
– Type systems, Design patterns, Language design
– Testing, Software quality, Refactoring

12. Computer Science 1
● Algorithms and Data Structures, 16 lectures
– Algorithm complexity theory, sorting algorithms (quicksort,
mergesort, heapsort),
– union-find algorithm;
– priority queues; binary search trees; red-black trees; hash
tables;
– graph-processing algorithms (minimum spanning tree,
shortest paths algorithms),
– greedy algorithms, dynamic programming

13. Computer Science 1
● Advanced Database Systems, 12 lectures
– New data types (unstructured, textual), Parallel Databases,
– noSQL, MongoDB, Spark, Streaming Systems,
– Memory Data management, Temporal and spatial
databases,
– Distributed databases, Heterogeneous databases and data
integration
– MapReduce, Hadoop, HBase, HIVE, Association Rules

14. Computer Science 2
● Parallel Computing, 16 lectures
– Implicit vs. explicit parallelism,
– Shared vs. non-shared memory (locks, race conditions,
deadlock),
– Synchronization mechanisms, Parallel programming
models,
– communications and interconnection networks, multicore
caching and memory systems,
– messaging, multicore processor design
– Functional Programming

15. Computer Science 2
● Advanced Algorithms, 12 lectures
– Distributed algorithms: Matrix Factorization, Large
Graph analysis,
– Streaming and online algorithms
– Optimization algorithms: search states,
metaheursitics, genetic algorithms,
– Simulated annealing, tabu search, Monte Carlo

16. Computer Science 2
● Software Architecture, 12 lectures
– The architecture influence cycle, quality attributes,
architecture design using patterns and tactics,
documenting and evaluating software architecture,
architecture reuse, architecture in Agile projects

17. Computer Science 2
● Software Optimization, 8 lectures
– Basic compiler optimizations, Data‐flow analysis,
Optimization,
– Scheduling, Dynamic compilation, Pointer alias
analysis, Parallelism/Locality

18. Product Development
● Product Life Cycle / Product Management / System
Analysis and Design, 12 lectures
● Managing Innovations / Entrepreneurship / Startup
Strategies, 8 lectures

19. Product Development
● Law in IT, 8 lectures
– Trade marks and international trade, Patents
Copyright law,
– License various types
– Introduction to cyberspace and cyberlaw, IP
Protection for software,
– Copyright in cyberspace, Content Liability,
– Trade marks, the Internet & domain names,
– Cybercrime, Online privacy

20. Data Science Courses

21. Mathematical Foundations
● Introduction to Data Science, 4 lectures
– Give a general intro to the Data Science problem
domain and topics: what is machine learning, learning
problem, supervised, unsupervised,
regression,generalization and overfitting, intro to time
series

22. Mathematical Foundations
● Linear algebra, 8 lectures
– Algorithms for eigenvalue and eigenvector computations
– Efficiency and stability of algorithm
– Matrix factorizations
– Solving linear systems and least squares problems
● Numerical optimization, 8 lectures
– Unconstrained optimization: optimality conditions, methods -
steepest descent,
– conjugate gradient, quasi-newton
– Linear optimization: solving LPs graphically, simplex method,
sensitivity
– Linear mixed integer programming: branch-and-bound,
– Elements of constrained optimization

23. Mathematical Foundations
● Applied Statistics and Probabilistic Analysis, 16
lectures
– Statistical inference, decision theory, point and interval estimation,
hypothesis testing, ANOVA,
– Neyman-Pearson theory, maximum likelihood,
– Bayesian analysis, large sample theory
– Simple linear regression, Multiple regression, Polynomial
Regression,
– Analysis of Variance: Fixed Effects, Nonlinear Regression,
Generalized Linear Models,
– Time Series Regression: Correlated Errors

24. Data Science 1
● Machine Learning, 20 lectures
– The Learning Problem, supervised vs. unsupervised
learning,
– Feasibility, Training vs Testing,
– Theory of Generalization, overfitting, validation,
– Linear models, linear regression, logistic regression,
– neural networks, support vector machines, kernel methods,
– Clustering, Bayesian and regularized regression, Naive
Bayes Classifier

25. Data Science 1
● Getting and Cleaning Data, 12 lectures
– Acquisition and cleaning of multisource data sets, types of
data sources and databases, web scraping and APIs, text
parsing and regular expressions
– Dimensionality reduction, normalization, feature extraction,
denoising, sampling, principle component analysis, feature
selection

26. Data Science 1
● Data Visualization, 8 lectures
– Visualization Infrastructure (graphics programming and human
perception),
– Multidimensional Data Visualization
– Basic Visualization: charts, graphs, animation, interactivity,
hierarchies, networks
– Visualization toolkits: ggplot2, d3.js, Tableau
– Exploratory data analysis-Visual analytics

27. Data Science 2
● Data Science Problems, 4 lectures
– Brief introduction to the different data science
domains
● Introduction to Deep Learning, 8 lectures
– Introduction to the main concepts of the Deep
Learning paradigm.
– Description of the general approaches in DL

28. Data Science 2
● Mining massive datasets, 16 lectures
– Introduction to BigData,
– Large scale supervised machine learning
– Link Analysis, PageRank, Distance Measures,
Nearest Neighbors,
– Mining data streams, Analysis of Large Graphs,
Clustering, MapReduce Algorithms

29. Data Science 2
● Application courses, 2 courses x 16 lectures
– Pick any two from the list
– DS Applications in Business Intelligence and Finance
– Computer Vision
– Natural Language Processing
– Bioinformatics
– Recommendation systems
– DS Applications in Medicine
– Network Analysis
– DS for Smart Cities (Energy, Transportation, etc.)
– Reinforcement Learning
– …...

30. Self Development Module
Soft skills
● One lecture per session - “Meet the leader”
● Reflexio program

31. Contacts
For more information:
Oleksii Molchanovskyi
Academic Program Manager at CS@UCU
olexiim@ucu.edu.ua