This course introduces business intelligence as computerized support for managerial decision-making. It concentrates on the theoretical and conceptual foundations of business intelligence as well as on commercial tools and techniques available for effective decision-support. This is a course intended to familiarize students with business analytics concepts, fundamentals, tools and applications. The course place special emphasis on working through applications and examples of analytics in the real world. It also presents some of the fundamental and most important techniques in business intelligence, analytics and data mining.

On successful completion of the course the students should be able to: • Demonstrate knowledge of key principles and techniques of business intelligence and business analytics. • Identify the right business intelligence and analytics tools/techniques for various business problems. • Categorize and carefully differentiate between situations for applying different data-mining techniques (association, correlation, classification, prediction, and cluster analysis). • Demonstrate knowledge and understanding the different of algorithms and quantitative techniques suitable for data analysis and mining in a broad range of application areas. • Design and implement systems for data mining and evaluate the performance of different data-mining algorithms. • Use a wide range of publicly available data mining tools. • Evaluate the effectiveness of these data mining tools based on different performance measures. • Make data driven decisions to optimize the business process according to the data analysis results and interpret the obtained results. • Use practical knowledge and skills in developing and using modern application software solutions for business decision support. •Develop the ability for team work.

Business analytics overview: descriptive, predictive and prescriptive analytics. Business intelligence: data, information and knowledge, architectures. Data preprocessing: data visualization, cleaning, integration, reduction, transformation and discretization. Prediction and classification methods: сimple linear and multiple linear regression, classification and regression trees, Bayesian methods, rule-based classification, neural networks, support vector machines, associative classification, k-nearest-neighbor classifier, case-based reasoning, genetic algorithms, and fuzzy set approaches. Cluster analysis: the partitioning, hierarchical, density-based, grid-based, and model-based methods data clustering (k-means, agglomerative hierarchical methods, divisive hierarchical methods, fuzzy c-means). Forecasting time series: еvaluating time series models, analysis of the components of time series, exponential smoothing models, and autoregressive models. Еvaluating predictive performance.

Practical teaching: The subjects of course are treated both theoretically and practically through laboratory sessions where selected methods are implemented and tested on typical amounts of data from real word. 1. Laboratory work - exercise 1: Design of classification models using Bayesian method, rule-based classification, k-nearest-neighbor classifier and decision trees: testing of created models, performance evaluation and comparison of results. 2. Laboratory work - exercise 2: Design of classification models using neural networks and support vector machines methods: testing of created models, performance evaluation and comparison of results. 3. Laboratory work - exercise 3: Design of prediction models using linear regression methods, neural networks, support vector machines methods: testing of created models on unknown samples, performance evaluation and comparison of results. 4. Laboratory work - exercise 4: Application of various clustering methods (k-means, aglomerative and divisive hierarchical methods, fuzzy c-means) on selected data set and comparison of obtained results. 5. Laboratory work - exercise 5: Forecasting time series using autoregressive and exponential smoothing models.

Defined by curriculum of the study programme.

• Radiša Jovanović, Business intelligence and business analytics, Lecture notes in electronic form. • Radiša Jovanović, Mаtlab and Simulink in Automatic Control, Faculty of Mechanical Engineering, Belgrade, 2021. • Modular educational real time control system with various control plants (DC servo motor, inverted pendulum, double inverted pendulum, heat flow experiment, coupled water tanks experiment), with acquisition hardware and software. • Intelligent Control Systems Laboratory, Control Systems Laboratory.

Total assigned hours: 90

New material: 26
Elaboration and examples (recapitulation): 4

Auditory exercises: 30
Laboratory exercises: 15
Calculation tasks: 0
Seminar paper: 0
Project: 0
Consultations: 0
Discussion/workshop: 0
Research study work: 0

Review and grading of calculation tasks: 0
Review and grading of lab reports: 5
Review and grading of seminar papers: 0
Review and grading of the project: 0
Test: 5
Test: 0
Final exam: 5

Activity during lectures: 5
Test/test: 30
Laboratory practice: 35
Calculation tasks: 0
Seminar paper: 0
Project: 0
Final exam: 30
Requirement for taking the exam (required number of points): 35

E. Turban, R. Sharda, D. Delen, (2014), Business Intelligence and Analytics: Systems for Decision Support, 10th edition, ISBN 0133401936, Pearson Education Limited.ROBOTS, 2nd Edition, The MIT Press.; P. C. Bruce, R. R. Patel, G. Shmueli, M. L. Stephens, (2017), Data mining for business analytics : concepts, techniques, and applications in JMP Pro, ISBN 1118729277, John Wiley & Sons.