Master Degree in Computer Science with specialization in Data Science

Ukrainian Catholic University, Lviv

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)

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

Two streamsGraduation 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”

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

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

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

Curriculum (with timeline)

Computer Science Courses

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

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

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

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

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

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

Computer Science 2

● Software Optimization, 8 lectures

– Basic compiler optimizations, Data‐flow analysis, Optimization,

– Scheduling, Dynamic compilation, Pointer alias analysis, Parallelism/Locality

Product Development

● Product Life Cycle / Product Management / System Analysis and Design, 12 lectures

● Managing Innovations / Entrepreneurship / Startup Strategies, 8 lectures

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

Data Science Courses

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

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

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

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

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

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

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

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

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

– …...

Self Development ModuleSoft skills

● One lecture per session - “Meet the leader”● Reflexio program

Contacts

For more information:

Oleksii MolchanovskyiAcademic Program Manager at CS@UCU

olexiim@ucu.edu.ua