| DA 501 Introduction to Data Analytics |
3 Credits |
|
This course teaches the fundamental ideas to clean,
manipulate, process and analyze data. The students will
work on data analysis problems arising in various data-
intensive applications. The course involves many in-class
coding exercises where the students are expected to work on
several case studies. Through these exercises, the course
shall also serve as an introduction to data analytics and
modern scientific computing.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Fall 2023-2024 |
Introduction to Data Analytics |
3 |
| Fall 2022-2023 |
Introduction to Data Analytics |
3 |
| Fall 2021-2022 |
Introduction to Data Analytics |
3 |
| Fall 2020-2021 |
Introduction to Data Analytics |
3 |
| Fall 2019-2020 |
Introduction to Data Analytics |
3 |
| Fall 2018-2019 |
Introduction to Data Analytics |
3 |
| Fall 2017-2018 |
Introduction to Data Analytics |
3 |
| Fall 2016-2017 |
Introduction to Data Analytics |
3 |
| Fall 2015-2016 |
Introduction to Data Analytics |
3 |
| Fall 2014-2015 |
Introduction to Data Analytics |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 503 Applied Statistics |
3 Credits |
|
This course is an applied statistics course with an
emphasis on data analysis. In this course we will study
several statistical modeling techniques and discuss real-
life problems over which we’ll have a chance to apply
statistical tools to learn from data. We will be covering
some of the fundamental statistical methods like linear
regression, principal component analysis, cross-validation
and p-values. The lectures are designed to help the
participants apply these techniques on data sets using a
statistical programming language.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Fall 2023-2024 |
Applied Statistics |
3 |
| Fall 2022-2023 |
Applied Statistics |
3 |
| Fall 2021-2022 |
Applied Statistics |
3 |
| Fall 2020-2021 |
Applied Statistics |
3 |
| Fall 2019-2020 |
Applied Statistics |
3 |
| Fall 2018-2019 |
Applied Statistics |
3 |
| Fall 2017-2018 |
Applied Statistics |
3 |
| Fall 2016-2017 |
Applied Statistics |
3 |
| Fall 2015-2016 |
Applied Statistics |
3 |
| Fall 2014-2015 |
Applied Statistics |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 505 Introduction to Data Modeling and Processing |
3 Credits |
|
In this course, we will cover fundamental aspects of Data
Management including traditional data management as well
as new models for big data. We will start with conceptual
data modelling (ER and UML models), then study relational
model, and how conceptual models could be converted to
relational model. We will cover SQL language for querying
relational data. We will continue with more recent models
such as key-value stores, document databases and graph
databases. Students will do practical work on relational and
non-relational (NoSQL) database systems.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Fall 2023-2024 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2022-2023 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2021-2022 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2020-2021 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2019-2020 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2018-2019 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2017-2018 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2016-2017 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2015-2016 |
Introduction to Data Modeling and Processing |
3 |
| Fall 2014-2015 |
Introduction to Data Modeling and Processing |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 507 Modeling and Optimization |
3 Credits |
|
The aim of this course is to introduce the concept of
analytical modeling, optimization problems and the
fundamental properties of an optimization problem. Students
will learn basics of transforming problems into
analytical/quantitative/mathematical models, and how to
formulate and solve simple mathematical models that
represent optimization problems. Both exact algorithms and
approximate algorithms, particularly heuristic techniques
will be covered in order to form an understanding of
algorithms and algorithm design to solve optimization
problems. Throughout the course linear, nonlinear and
integer optimization problems, network flow and network
design problems will be the main focus with examples from
the data science and data analytics domain.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Fall 2023-2024 |
Modeling and Optimization |
3 |
| Fall 2022-2023 |
Modeling and Optimization |
3 |
| Fall 2021-2022 |
Modeling and Optimization |
3 |
| Fall 2020-2021 |
Modeling and Optimization |
3 |
| Fall 2019-2020 |
Modeling and Optimization |
3 |
| Fall 2018-2019 |
Modeling and Optimization |
3 |
| Fall 2017-2018 |
Modeling and Optimization |
3 |
| Fall 2016-2017 |
Modeling and Optimization |
3 |
| Fall 2015-2016 |
Modeling and Optimization |
3 |
| Fall 2014-2015 |
Modeling and Optimization |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 512 Big Data Processing using Hadoop |
3 Credits |
|
This course will provide the essential background
to start to develop programs that will run on Hadoop
Distributed File System (HDFS). The course will also show
the students the limitations of traditional programming
techniques and how Hadoop addresses these problems.
After learning the basics of a Hadoop Cluster and Hadoop
Ecosystem, students will learn to write programs using
MapReduce framework and run these programs on
a Hadoop Cluster. There will be introductory level
information about Pig, Hive.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Spring 2021-2022 |
Big Data Processing using Hadoop |
3 |
| Spring 2020-2021 |
Big Data Processing using Hadoop |
3 |
| Spring 2019-2020 |
Big Data Processing using Hadoop |
3 |
| Spring 2018-2019 |
Big Data Processing using Hadoop |
3 |
| Spring 2017-2018 |
Big Data Processing using Hadoop |
3 |
| Spring 2016-2017 |
Big Data Processing using Hadoop |
3 |
| Spring 2015-2016 |
Big Data Processing using Hadoop |
3 |
| Spring 2014-2015 |
Big Data Processing using Hadoop |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 513 Time Series Analysis and Forecasting |
3 Credits |
|
This course will provide a basic introduction to univariate
and multivariate time series analysis and forecasting which
covers a wide range of forecasting methods including
classical (Autoregressive and Moving Average models) and
Machine Learning approaches. Students will learn how to
deal with basic concepts such as stationarity, series
decomposition, trend, seasonality and time series smoothing
to be able to apply different forecasting techniques.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Spring 2023-2024 |
Time Series Analysis and Forecasting |
3 |
| Spring 2022-2023 |
Time Series Analysis and Forecasting |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 514 Machine Learning I |
3 Credits |
|
In this course, we will cover fundamental aspects of
Machine Learning. We will start with fundamentals of
machine learning, including different learning paradigms,
regression and classification problems, evaluation methods,
generalization and overfitting. We will then cover some of
the fundamental machine learning techniques such as
decision trees, Bayesian approaches, Naive Bayes
classifier, and logistic regression, k-Nearest neighbor, and
online learning algorithms. Besides understanding the basic
theory behind the techniques, students are expected to
apply them on different platforms.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Spring 2023-2024 |
Machine Learning I |
3 |
| Spring 2022-2023 |
Machine Learning I |
3 |
| Spring 2021-2022 |
Machine Learning I |
3 |
| Spring 2020-2021 |
Machine Learning I |
3 |
| Spring 2019-2020 |
Machine Learning I |
3 |
| Spring 2018-2019 |
Machine Learning |
3 |
| Spring 2017-2018 |
Machine Learning |
3 |
| Spring 2016-2017 |
Machine Learning |
3 |
| Spring 2015-2016 |
Machine Learning |
3 |
| Spring 2014-2015 |
Machine Learning |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 515 Practical Case Studies in Data Analytics |
3 Credits |
|
This course aims at discussing the key principles of the
knowledge-discovery process through various case
studies arising from different application areas. The
students are expected to learn the main steps to traverse
when they face new data analytics problems. With each
case study, the tools for cleaning, processing and
altering the data shall be visited. A particular attention
shall be given to data inspection, feature reduction and
model selection. Each case study will be completed by a
thorough discussion and interpretation of the results.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Summer 2022-2023 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2021-2022 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2020-2021 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2019-2020 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2018-2019 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2017-2018 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2016-2017 |
Practical Case Studies in Data Analytics |
3 |
| Summer 2015-2016 |
Practical Case Studies in Data Analytics |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 516 Social Network Analysis |
3 Credits |
|
Different types of social networks and connectivity are a
crucial part of the underlying models of the new
generation of applications we use. These connections
include people, places, activities, businesses, products,
social and integrated business processes happening in
personal and business networks or communities. In this
course we will study different applications such as
Facebook, Twitter, Linkedin and Foursquare, and
discover different networks formed by connectivity. We
will introduce tools that will give us insight into how
these networks function: We will introduce
fundamentals of graph theory and discover how these
graphs can be modeled and analyzed (Social Network
Analysis). We will also study the interaction dynamics
using game theory. Learning objectives are: 1. Study
different social applications and how they can be
modeled. 2. Understand the basics of graph theory. 3.
Understand and perform basic social network analysis 4.
Understand the basics of game theory 5. Apply these
concepts to model the Web and new social applications.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Spring 2023-2024 |
Social Network Analysis |
3 |
| Spring 2022-2023 |
Social Network Analysis |
3 |
| Spring 2021-2022 |
Social Network Analysis |
3 |
| Spring 2020-2021 |
Social Network Analysis |
3 |
| Spring 2019-2020 |
Social Network Analysis |
3 |
| Spring 2018-2019 |
Social Network Analysis |
3 |
| Spring 2017-2018 |
Social Network Analysis |
3 |
| Spring 2016-2017 |
Social Network Analysis |
3 |
| Spring 2015-2016 |
Social Network Analysis |
3 |
| Spring 2014-2015 |
Social Network Analysis |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 517 Machine Learning II |
3 Credits |
|
This course covers various supervised and unsupervised
learning algorithms and is intended as a sequel to
Machine Learning I. The first half of the course focuses
on unsupervised learning with an emphasis on clustering
techniques, recommendation systems and
dimensionality reduction. In the second half, supervised
learning methods will focus on text classification and
artificial neural networks. Students are expected to
understand the fundamental theories behind these
techniques and gain the ability to apply these algorithms
to various problems. This is a hands-on course in which
students are expected to work on end-to-end machine
learning solutions.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Spring 2023-2024 |
Machine Learning II |
3 |
| Spring 2022-2023 |
Machine Learning II |
3 |
| Spring 2021-2022 |
Machine Learning II |
3 |
| Spring 2020-2021 |
Machine Learning II |
3 |
| Spring 2019-2020 |
Machine Learning II |
3 |
| Spring 2018-2019 |
Data Mining (DA510) |
3 |
| Spring 2017-2018 |
Data Mining (DA510) |
3 |
| Spring 2016-2017 |
Data Mining (DA510) |
3 |
| Spring 2015-2016 |
Data Mining (DA510) |
3 |
| Spring 2014-2015 |
Data Mining (DA510) |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 518 Exploratory Data Analysis and Visualization |
3 Credits |
|
Exploratory Data Analysis (EDA) is an approach to data
analysis for summarizing and visualizing the important
characteristics of a data set. EDA focuses on exploring data
to understand the data’s underlying structure and variables,
to develop intuition about the data set, and decide how it
can be investigated with more formal statistical methods.
EDA is distinct from Data Visualization in that EDA is
done towards the beginning of analysis and data
visualization is done towards the end to communicate
one’s finding. This course particularly pays attention to
the applied techniques to data visualization narratives.
We will draw on case studies from business world, industry
to news media.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Spring 2023-2024 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2022-2023 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2021-2022 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2020-2021 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2019-2020 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2018-2019 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2017-2018 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2016-2017 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2015-2016 |
Exploratory Data Analysis and Visualization |
3 |
| Spring 2014-2015 |
Exploratory Data Analysis and Visualization |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 519 Causal Data Science |
3 Credits |
|
Causal data science has recently become a sub-discipline
of general data science. The aim of this
area is to draw cause-effect relationships from experimental
and especially observational data. With this,
the possible effects of the plannned interventions will
be better understood. Application areas of causal data
science consist of medicine, economy and finance, marketing,
political sciences, management and tech industry.
The main output of this course will be that the students
will be able to obtain cause-effect relationships
with modern machine learning methods. The course will be
taught with applications in Python.
|
| Last Offered Terms |
Course Name |
SU Credit |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 520 Deep Learning |
3 Credits |
|
Recent advances in deep learning have led to groundbreaking
advances in many fields, including computer vision and
naturallanguage processing. This course aims to equip
students withpractical skills and theoretical knowledge to
leverage cutting-edgedeep neural network architectures and
algorithms to solve real-worldchallenges. Students will
gain a thorough understanding of deeplearning fundamentals
such as network architecture design,
activation functions, loss functions, optimization
algorithms, andregularization techniques that collectively
enable neural networks tolearn complex patterns and
representations from data. Students willthen gain practical
knowledge on deploying deep learning models,conducting exper
experiments, and optimizing model performance through
throughhands-on experience with real-world datasets
using the Pythonprogramming
language and the PyTorch framework.
|
| Last Offered Terms |
Course Name |
SU Credit |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 522 Information Law and Data Ethics |
3 Credits |
|
Given the widespread distribution of data in today’s
business world, the legal and ethical issues related to the
use of data have been, and will be, of critical importance
in establishing a corporate policy. Within the framework
of these legal and ethical issues, students will gain an
understanding of the following concepts: private,
confidential, anonymous and open data; private versus
public data; data ownership and proprietary rights;
intellectual property; overview of existing legal
framework; constraints, rules and legislative procedure in
access and use of data.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Summer 2022-2023 |
Information Law and Data Ethics |
3 |
| Summer 2021-2022 |
Information Law and Data Ethics |
3 |
| Summer 2020-2021 |
Information Law and Data Ethics |
3 |
| Summer 2019-2020 |
Information Law and Data Ethics |
3 |
| Summer 2018-2019 |
Information Law and Data Ethics |
3 |
| Summer 2017-2018 |
Information Law and Data Ethics |
3 |
| Summer 2016-2017 |
Information Law and Data Ethics |
3 |
| Summer 2015-2016 |
Information Law and Data Ethics |
3 |
| Summer 2014-2015 |
Information Law and Data Ethics |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 525 Project Management and Business Communication |
3 Credits |
|
This course is intended to provide industry insight
into the world of project management and business
communication. Upon completion of this course,
students are expected to have a clear understanding
of the tasks and challenges that are fundamental to project
management requirements. The course will also cover
issues on team management and other aspects of project
management on schedules, risks and resources for a
successful project outcome. The second part of this course
will concentrate on effective communication with team
members, presentation techniques for a wide range
of audiences and communicating results and
recommendations to upper management and clients.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Summer 2022-2023 |
Project Management and Business Communication |
3 |
| Summer 2021-2022 |
Project Management and Business Communication |
3 |
| Summer 2020-2021 |
Project Management and Business Communication |
3 |
| Summer 2019-2020 |
Project Management and Business Communication |
3 |
| Summer 2018-2019 |
Project Management and Business Communication |
3 |
| Summer 2016-2017 |
Project Management and Business Communication |
3 |
| Summer 2015-2016 |
Project Management and Business Communication |
3 |
| Summer 2014-2015 |
Project Management and Business Communication |
3 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 6 ECTS (6 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
| DA 592 Term Project |
0 Credit |
|
All graduate students pursuing a non-thesis MSc.
Program are required to complete a project.
The project topic and contents are based on the
interest and background of the student and
are approved by the faculty member serving as the
Project Supervisor. At the completion of the project,
the student is required to submit a final report and
present the project.
This course aims to provide the students with skills
and training to conduct research in a certain area, manage
a project on time and to interpret the outcome of the
research study. In addition, students are expected to gain
experience and further skills in creating a proper project
proposal, identifying and evaluating the principal
components that will establish the project
scope, conducting a literature survey and compiling the
results, deciding on the formal methodology
and analyzing the outcome, gaining experience in teamwork,
cooperation and information sharing, publishing a project
report in a format accepted by the scientific communities,
and finally preparing and executing a
presentation of the project outcome.
|
| Last Offered Terms |
Course Name |
SU Credit |
| Summer 2022-2023 |
Term Project |
0 |
| Summer 2021-2022 |
Term Project |
0 |
| Summer 2020-2021 |
Term Project |
0 |
| Summer 2019-2020 |
Term Project |
0 |
| Summer 2018-2019 |
Term Project |
0 |
| Summer 2017-2018 |
Term Project |
0 |
| Summer 2016-2017 |
Term Project |
0 |
| Spring 2016-2017 |
Term Project |
0 |
| Fall 2016-2017 |
Term Project |
0 |
| Summer 2015-2016 |
Term Project |
0 |
| Fall 2015-2016 |
Term Project |
0 |
| Summer 2014-2015 |
Term Project |
0 |
|
| Prerequisite: __ |
| Corequisite: __ |
| ECTS Credit: 30 ECTS (30 ECTS for students admitted before 2013-14 Academic Year) |
| General Requirements: |
|
|
| |
