Course Code

AIS-187

Semester

2nd Semester

ECTS Credits

7,5

Type of Course

Mandatory

Data Warehouses and Business Intelligence

Objective

The main goal of the course is to introduce students to modern techniques and methods for efficient data analysis, extracting useful information and presenting it in a way that will help the executives of a company to make useful business decisions. In the course will be studied the main techniques of design and development of data warehouses as well as analysis of multidimensional data models. Students will also be introduced to a wide range 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 gain important technical skills in business data analysis.

Course Contents

Data warehouses

Multidimensional data model, data warehouse architecture, data warehouse design, data export-transformation-loading.

Multidimensional data analysis

OLAP functions, query systems in data warehouses, report generation.

Introduction to recommendation systems

Introduction to the problem and applications of recommendation systems. Presentation of basic techniques for personalized recommendations through content-based approaches, techniques of closest neighbors. User-user collaborative filtering technique, (item-item collaborative filtering) algorithm.

Advanced recommendation techniques

Matrix derivatization methods and hybrid methods of recommendations.

Correlation rules

Methods for finding frequently occurring sets, market basket analysis, Apriori algorithm, metrics for evaluating correlation rules.

Exploratory analytics and visualizations

Univariate and bivariate analysis, visualization, histograms, cumulative distribution function, summary statistics, position and dispersion measures, detection of correlations between two variables, alternative mapping methods using diagrams, use of visualization techniques for multivariate data analysis.

Detailed with visualization

Data visualization tools and techniques, data analytics with visualization, Tableau visualization tool, applications in business intelligence, new user interfaces, advanced visualization techniques, original research systems.

Time series analysis

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

Simulations

The role of simulation to extract 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.

Detailed position

Tags for geographic data, social networks that take into account the position, a combination of spatial, temporal and text data, analytical applications aimed at geographical social content, analytical position on Twitter, Flickr, Foursquare.

Recommended Readings

  • 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.