Business Intelligence

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.