Intensive courses

Course in InSIS – 4IT370
Date: 22-24 May 2023, exam – 26 May
3 ECTS
Registrations: are open from March 2 to April 30

 

Dr George Feuerlicht is an Associate Professor at the Department of Information Technologies at the Unicorn University and a visiting lecturer at the Prague University of Economics and Business. George has been involved in database research and teaching for over three decades. He has presented seminars and professional development courses in Australia, Europe, Asia and USA. He is the author of over 100 publications across a range of topics in computer science. He holds a PhD in Electrical Engineering from the Imperial College, London University, U.K.

 

 

Large volumes and complexity of data that organizations manage today is challenging traditional approaches to data management. To address such challenges relational databases have introduced a range of advanced features that support the management of complex data objects at scale. More recently, a new generation of non-relational databases known as NoSQL have emerged. NoSQL databases include a diverse range of products designed to manage large volumes of different types of data using cloud infrastructure. In this 3-day course we discuss the motivation for NoSQL and then cover a range of advanced database techniques with practical demonstrations and hands-on exercises using leading NoSQL databases, including MongoDB and Neo4J.

 

Course Content

• Introduction: Data management challenges, benefits and limitations of relational databases
• Advanced SQL features: User Defined Types, Collections, Object types and methods, etc.
• Management of semi-structured data: XML and JSON data types, XQuery
• Overview of NoSQL databases: Document databases, Column databases, Graph databases, Data Lakes, In-memory databases, etc.
• NoSQL concepts and techniques: horizontal scalability and sharding, schema-less data, CAP theorem, data replication and BASE consistency
• NoSQL databases: MongoDB, Neo4j, , etc.
• AWS database services: Amazon DynamoDB, AWS Athena, etc.
• Summary: SQL vs NoSQL- benefits and drawbacks, future developments
• Practical hand-on exercises using a selection of NoSQL database

 

 

Course in InSIS – 4ST650
Date: 11-14 April 2023
3 ECTS
Registrations: are open from 13 February to 9 April

 

Dr Nasrollah Saebi is Senior Lecturer in Statistics and Statistics Field Leader at the Kingston University, Faculty of Science, Engineering and Computing.

AIMS:

• To forecast future values of economic and business time series data
• To introduce applied non-probabilistic algorithmic methods of forecasting
• To introduce applied probabilistic Box-Jenkins methodology and ARIMA modelling to forecast future behaviour of time series data

PREREQUISITE:

• Basic knowledge of statistical theory

LEARNING OUTCOMES:

On successful completion of the course, students will be able to:

• analyse economic and business time series data and select appropriate forecasting techniques for them;
• suggest suitable non-probabilistic algorithmic models of forecasting for a time series data;
• suggest tentative ARIMA models of forecasting using Box-Jenkins methodology for a non-seasonal time series data;
• evaluate and critically assess the validity of the modelling outcomes from the computer output;
• use appropriate criteria to identify optimal model for forecasting using Box-Jenkins methodology;
• forecast future values for economic and business time series data.

CURRICULUM CONTENT:

• Measures of forecasting errors and their applications.
• Forecasting methods for seasonal data: Additive and Multiplicative Decomposition Methods involving centred moving averages and statistical regression technique.
• Forecasting method for data without a trend: Single Exponential Smoothing.
• Forecasting methods incorporating trend: Brown’s Method, Holt’s Method.
• Box-Jenkins Forecasting Methodology and Modelling Procedures: AR, MA and ARIMA models; stationarity and invertibility; Ljung-Box diagnostic model testing; back-shift operator; Akaike and Schwartz Bayesian model selection criteria.
• Use of appropriate industry standard software packages (e.g. SAS for Box-Jenkins modelling and forecasting methods and MS-Excel and Solver for other methods).

 

TEACHING AND LEARNING STRATEGY:

The theoretical aspects will be delivered through a series of lectures, developing from the basic moving average methods. The lectures will be complemented by practical sessions in which economic and business time series data will be analysed. Here, tentative models are identified and forecasts are made using an appropriate statistical software package.

 

ASSESMENT STRATEGY:

Assessment consists of group modelling assignments. The group assignments are designed to assess understanding of students in selecting appropriate forecasting techniques and models for their time series data, evaluating the validity of their models by interpreting the results from their computer output, conducting relevant tests and performing diagnostic checking and making useful and informative forecasts.

BIBLIOGRAPHY:

• Bowerman B L, O’Connell R T, and Koehler A B (2005), Forecasting, Time Series and Regression – An Applied Approach, Fourth Edition, Thomson
• Box G E P & Jenkins G M, (2016), Time Series Analysis: Forecasting and Control, Fifth Edition, Wiley
• Carter Hill R, Griffiths W E and Lim G C (2018), Principles of Econometrics, Fifth Edition, Wiley
• Enders W (2014), Applied Econometric Time Series, Fourth Edition, Wiley
• Janacek G, (2001), Practical Time Series, Arnold
• Maddala G S, (2009), Introduction to Econometrics, Wiley
• Makridakis S, Wheelwright S C and Hyndman R J, (2008), Forecasting: Methods and Applications, Wiley
• Madsen, H, (2008), Time Series Analysis, Chapman and Hall/CRC
• Wei, W W S, (2018), Time Series Analysis, Univariate and Multivariate Methods, Pearson Addison Wesley

 

COURSE DESCRIPTION:

Cloud computing has become the dominant approach for the implementation of information systems with many government and private organizations migrating their entire IT infrastructure to the cloud. Most experts today recognize the benefits of cloud computing that include fast implementation, cost reduction and a potential for rapid innovation. However, the fast rate of evolution of cloud technologies and the complexity of managing large-scale cloud deployments represent a challenge for organizations making the transition into the cloud. This three-day course aims to provide the attendees with a balanced view of cloud computing covering the basic cloud concepts and terminology and discussing the benefits and challenges of cloud adoption. The course includes demonstrations and practical hands-on exercises using Amazon Web Services, including EC2, S3, Lambda, RDS and NoSQL databases.

 

 

CURRICULUM CONTENT:

• Introduction to cloud computing: current IT technology trends, business motivations and technology drivers, benefits and challenges, cloud vs on premises IT, cloud computing in a historical context, cloud computing case studies
• Cloud computing concepts and terminology: SOA services, APIs, virtual machines and containers, serverless computing, DevOps and microservices, cloud computing service models (SaaS, IaaS, PaaS, etc.), cloud computing deployment models (public, private, and hybrid clouds), multitenancy and polymorphic applications, etc.
• Cloud databases: SQL and NoSQL databases, document databases, column databases, graph databases, etc. CAP theorem and BASE consistency, tunable consistency, NoSQL examples: MongoDB, Amazon DynamoDB, Neo4J, AWS Athena, etc.
• Public cloud platforms: AWS, Azure, Google Cloud Platform. AWS core services: EC2, EBS, S3, RDS (Oracle, DynamoDB, Amazon Aurora), ML, etc.
• Cloud computing architectures and open source frameworks: NIST Reference Architecture, Kubernetes, Cloud Foundry, etc.
• Cloud computing adoption: migration readiness and planning, migration strategies, AWS Adoption Framework
• Future directions: Industrialization of IT, IoT, Machine Learning, etc.