Cyprus International University Course Portal

Construction projects are risky. Things can and do go wrong
regularly. In this course, we explore the fundamentals of risk
management in construction projects. The purpose of this course is to
learn the tools and techniques to manage uncertain events and
circumstances that can influence the project. The course is designed for
engineers, quantity surveyors, and construction management
professionals looking to further develop their learning.

By the end of this course, you should be able to:

• Understand the fundamentals of risk management including key terms and definitions

• Apply and use the risk management process to properly identify, analyze and manage uncertain events and circumstances

• Understand
  the specific risks applicable to construction projects including
  technical, construction, health and safety, environmental, commercial,
  and external

This course provides a comprehensive understanding of transforming data into visuals by introducing participants to important principles of analytical design and practical data visualization techniques for the exploration and presentation of univariate and multivariate data. Data visualization is covered as one of the most effective tools to explore, understand, and communicate patterns in quantitative information. The course provides a broad understanding of techniques and algorithms of turning data into readable visuals. Upon completion of the course, students learn about data visualization processes including data modeling, data aggregation and filtering, mapping data attributes to graphical attributes, and visual encoding. Students also learn to assess the effectiveness of different visualization designs, and critically evaluate each design decision.

Bu derste, kökleri bilgisayar bilimi, yapay zeka ve istatistik olan istatistiksel makine öğrenimi işlenir ve bilgisayarların 'öğrenme' süreciyle performanslarını iyileştirmelerine, karar vermelerine ve tahmin etmelerine olanak tanıyan geniş bir algoritma anlayışı sağlanır. Derste temel yöntemler öğretilir ve gerçek verilere uygulanır. Başlıktaki istatistiksel terimi, makine öğrenimine baskın yaklaşımlar oluşturan istatistiksel teknikleri vurgular. Ders, metodolojiyi teorik temeller, hesaplama unsurları ve istatistiksel teori konularıyla bütünleştirir ve öğrencilere modern istatistiksel makine öğrenimi yöntemlerinin ardındaki temel fikirleri ve sezgileri sağlar. Bu dersi tamamlayan öğrencilerin, konuşma tanıma, internet arama, biyoinformatik, görüntü ve ses sinyali analizi, veri madenciliği ve keşifsel veri analizi konularında denetimli ve denetimsiz öğrenme yaklaşımlarını öğrenmeleri beklenmektedir.

In this course, statistical machine learning which has roots in computer science, artificial intelligence and statistics is covered and a broad understanding of algorithms that allow computers to improve their performance through the process of ‘learning’ and enable them to make decisions and predictions is provided. Fundamental methods are taught and applied to real data. The term statistical in title emphasizes the statistical techniques, which form dominant approaches to machine learning. The course integrates methodology with theoretical underpinnings, computational elements, and statistical theory issues. By completion of this course, students are expected to learn about supervised and unsupervised learning approaches to speech recognition, internet search, bioinformatics, image and audio signal analysis, data mining and exploratory data analysis.

This course covers big data, which becomes increasingly available with digitalization of many aspects of human life and with increasingly growth of social media platforms. The course presents a broad understanding of big data and different approaches of big data manipulation. Students will explore how terabytes and petabytes of storage space can be occupied by big data. In this course, the Hadoop software, the Hadoop Distributed File System (HDFS) and the MapReduce technique as well as supervised and unsupervised machine-learning approaches are introduced and artificial neural networks are thoroughly investigated. By completion of the course, students are expected to be able to harvest big data sets from the internet and process by using different methods and tools.

This course covers the algorithmic techniques and approaches required to handle various types of structured, semi-structured and unstructured data. The goal of the course is to teach algorithmic methods that serve as the cornerstones for handling and analyzing large datasets in a variety of formats. The course specifically covers how to pre-process big datasets, store big datasets effectively, design quick algorithms for big datasets, and evaluate the performance of designed algorithms. Algorithms for sorting, searching and matching as well as graph and streaming algorithms will be introduced. Upon completion of this course, students will have a broad knowledge of different algorithms for pre-processing, organizing, manipulating and storing different data types. Students will also be able to carry out performance analysis of each algorithm.

The objective of this course is to provide the necessary knowledge of probability and statistics theory to solve a variety of data science problems. Fundamental concepts such as random variables, independence, expected values, standard errors and central limit theorem are covered. The tools that are frequently used in data science, like Bayesian inference and maximum likelihood estimation, are highlighted. Upon completion of this course, students will be capable of employing probabilistic and statistical models for data manipulation and using the statistical programming language R to perform statistical data analysis. Students will be able to suggest statistical techniques to estimate the parameters of probabilistic models that represent randomness in life. Students will also be prepared to use the new techniques in later machine learning courses.

The growth of digital marketing is the most important yet unpredictable trend in marketing today. How can the online world be harnessed by the companies of today and tomorrow to grow their marketing impact? What role do information and databases have to play in this system? And why do some non-digital means of direct marketing still remain so powerful?  this course brings great expertise across direct, database and digital marketing to provide comprehensive, compelling coverage of the key theory and debates of the fields.