Master Program in Computer Science with specialization in Data Science
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
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
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
– …...