Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics

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    Business Intelligence

    A tantárgy neve magyarul / Name of the subject in Hungarian: Üzleti intelligencia

    Last updated: 2019. március 6.

    Budapest University of Technology and Economics
    Faculty of Electrical Engineering and Informatics
    MSc, Applied Informatics Main specialization
    Course ID Semester Assessment Credit Tantárgyfélév
    VIAUMA02 2 2/1/0/v 4  
    3. Course coordinator and department Dr. Ekler Péter,
    4. Instructors

    Name:

    Status:

    Department:

    Dr. Péter Ekler

    Associate Professor

    Department of Automation and Applied Informatics

    Dr. Ákos Dudás

    Associate Professor

    Department of Automation and Applied Informatics

    5. Required knowledge Databases, Computer networks, Object oriented programming
    6. Pre-requisites
    Ajánlott:

    BMEVITMA311 Databases

    BMEVIIIMA04 Service oriented system integration

    7. Objectives, learning outcomes and obtained knowledge

    The goal of the subject is to give a current knowledge to the students about modern data warehouse building, business intelligence system design, data transformation, reporting, charts, dashboards, data visualization, location based data processing, KPI discovery and churn and fraud detection.

    8. Synopsis

     

    Lectures

    1.

    Introduction, basic terminology of business intelligence. Data warehouses, data marts, the process of decision support and decision support systems from the perspective of applied informatics.

    2.

    Process of creating a business intelligence system; architectures, major components. Review of current leading business intelligence softwares.

    3.

    Data storage methods and their applicability in various application fields. Relational and NoSQL databases (Mongodb, Redis, Elasticsearch), data warehouses, typical data layers. Connection to data bases from various clients.

    4.

    ELT/ELT processes, creating such processes, customizing them. Common data collection, data cleansing methods, normalization, discretization, KPI selection.

    5.

    Complex event processing; connection various data sources, including complex event sources and fraud detection.

    6.

    Modern solutions to visualization, including responsive UI design. Customizable dashboards, filtering and embedding possibilities; Kibana for visualization over Elasticsearch.

    7.

    Summary of the presented solutions and techniques, comparison of commonly used business intelligence software, their advantages and disadvantages and integration solutions.

    8.

    SDK of modern business intelligence systems for implementing and customizing BI solutions; applicability of the SDK-s in practice.

    9.

    Statistical software, use cases and integration with other system. Basic time series analysis and its applicability. Pandas & Jupyter and the toolset of data scientists.

    10.

    Big Data introduction, definitions and terminology, software tools. Application area of Big Data technologies and Big Data systems.

    11.

    Introduction to Hadoop and commonly used extensions, such as Hive and Impala. Basics of developing for Hadoop.

    12.

    Practical application of Hadoop presented through case studies. Data loading, storage, data management, visualization techniques, interoperability with clients and mobile environments.

    13.

    Cloud technologies and Big Data. Commonly used Big Data cloud providers and services and their comparison.

    14.

    Case study review.

     

     

    Seminars

    1.

    Creating a relational data base, using modern data base management software, programmability of data bases.

    2.

    NoSQL data bases, creating data bases, loading data into data bases, querying data bases.

    3.

    ETL subsystems of business intelligence systems. Creating a complex ETL process; moving, cleansing, aggregating data.

    4.

    Data visualization methods and tools, creating reports and dashboards.

    5.

    Development and customization of business intelligence systems using their SDK through examples.

    6.

    Hadoop in practice, a complex tutorial of Hadoop tools.

    7.

    Using cloud services for Big Data through an example.


    9. Method of instruction Lectures and seminars.
    10. Assessment

    a. During mid-term: one mid-term exam

    b. During exam period: written exam

    c. Pre-exam: possible

      

    For getting the signature the mid-term exam must be at least 40%. For getting mark from the subject signature and passed exam are needed.

    11. Recaps It is possible to write the mid-term exam again on the supplement week.
    12. Consultations It is possible at the office hour of the lecturer.
    13. References, textbooks and resources
    Ralph Kimball, Margy Ross - The Data Warehouse Toolkit
    Ralph Kimball, Joe Caserta - The Data WarehouseETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data
    David Haertzen - Data Warehousing and Business Intelligence Tutorials
    (http://infogoal.com/datawarehousing/)
    Jiawei Han, Micheline Kamber – Adatbányászat: Koncepciók és technikák
    Hadoop, MapReduce tutorial: http://hadoop.apache.org/docs/r1.2.1/mapred_tutorial.html
    Tom White - Hadoop: The Definitive Guide
    Pramod J. Sadalage, Martin Fowler - NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence
    Stephen Few - Information Dashboard Design: Displaying data for at-a-glance monitoring
    John Russel: Cloudera Impala, 2013.

    14. Required learning hours and assignment
    Lectures42
    Prepare for lectures14
    Prepare for mid-term exam20
    Prepare for exam44
    Sum120
    15. Syllabus prepared by

    Name:

    Status:

    Department:

    Dr. Péter Ekler

    Associate Professor

    Department of Automation and Applied Informatics

    Dr. Ákos Dudás

    Associate Professor

    Department of Automation and Applied Informatics