Code

ΠΠΣ-187

Semester

2nd

ECTS

7,5

E-Services

Lefkippos

Category

Obligatory

Instructors

Μ. Halkidi
C. Doulkeridis

Objective

The main objective of the course is to present students with modern techniques and methods for the efficient analysis of data, the extraction of useful information and its presentation in a way that will help the executives of a company to make useful business decisions.

In the context of the course, the main techniques of designing and developing data warehouses as well as analyzing multidimensional data models will be studied.

Also, students will get to know a wide set of data analysis techniques that can be used in understanding business data, extracting knowledge from it and in the decision-making process.

Through this course, students are expected to learn techniques that are part of business intelligence and acquire important technical skills in business data analysis.

After successfully completing the course, students will be able to:

  • design and implement data warehouses
  • learn techniques that are part of business intelligence
  • acquire significant technical skills in analysisdata
  • analyze time series data
  • present data analysis results with the most appropriate visualization technique

Learning outcomes

  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Adapting to new situations
  • Decision-making
  • Working independently
  • Production of new research ideas
  • Project planning and management
  • Criticism and self-criticism

Syllabus

  • Data warehouses

    Multidimensional data model, data warehouse architecture, data warehouse design, extract-transform-load data.

     

  • Multidimensional data analysis

    OLAP functions, query systems in data warehouses, creating reports.

     

  • Introduction to Recommender Systems

    Introduction to the problem and applications of recommender systems. Introducing the basic techniques for personalized recommendations through content-based approaches, nearest neighbor techniques. User-user collaborative filtering technique, item-item collaborative filtering algorithm.

     

  • Advanced recommendation techniques

    Matrix factorization methods and hybrid recommendation methods.

     

  • Association rules

    Methods Finding frequent sets, shopping basket analysis, Apriori algorithm, correlation rule evaluation metrics.

     

  • Exploratory analytics and visualizations

    Univariate and bivariate analysis, visualization, histograms, cumulative distribution function, summary statistics, measures of location and dispersion, identifying correlations between two variables, alternative ways of displaying using charts, using visualization techniques for multivariate data analysis.

     

  • Analytics with visualization

    Data visualization tools and techniques, data analytics with visualization, applications in business intelligence, new user interfaces, advanced visualization techniques, research prototypes.

     

  • Time series analysis

    Examples and motivation, trend detection, moving averages, smoothing methods, autocorrelation function.

     

  • Simulations

    The role of simulation for extracting information from data, Monte-Carlo simulation, use of simulation for cases where analytical modeling is complex, development of models with simulation, validation of models with simulation.

     

  • Location analytics

    Geotagging, location-aware social networks, combination of spatial, temporal and text data, analytics applications targeting geographic social content, location analytics on Twitter, Flickr, Foursquare.

Bibliography

  • Han J. & Kamber M. (2006): Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann.
  • Jure Leskovec, Anand Rajaraman, Jeff Ullman. Mining of Massive Datasets. Cam-bridge University Press. 2014 (2nd Edition).
  • Raymond T.Ng et al. (2013): Perspectives on Business Intelligence. Morgan & Claypool Publishers. Synthesis Lectures on Data Management.
  • Philipp K. Janert (2010) Data Analysis with Open Source Tools: A hands-on guide for programmers and data scientists, O’Reilly Media.