Fundamentals of Data Analytics

Data analytics is the art and science of teasing meaningful information and patterns out of large quantities of data. It combines statistical methods for identifying patterns in data and making inferences with a number of IT technologies. The course offers broad background to data analytics and data mining methods and their application in practice. It brings together the state-of-the-art research and practice in related areas and provides students with the necessary knowledge and capacity to initiate and lead data analytics projects that can turn company data into commercially valuable information.

The subject of discipline is the fundamental statistical methods of data analysis.

The purpose of discipline is the formation of theoretical knowledge, applied skills and skills in the organization of statistical observations, methods of statistical analysis and prediction of socio-economic phenomena and processes.

Task of the discipline:

• Determination of the principles of observation;
• Enforcing data processing skills;
• Investigation of methods of constructing indexes and their research;
• Formation of the sample and its analysis from the position of representativeness;
• Construction of data research indicators and their analysis.

Upon successful completion of this subject, students should be able to:

• Demonstrate background knowledge about the process of data mining and knowledge discovery;
• Describe the methods involved in data mining, their scope and limitations;
• Apply practical knowledge on data mining and pattern discovery, and analysis skills;
• Apply practical knowledge in visualisation and multimedia support for data mining;
• Organize and implement a data mining project in a business environment;
• Interpret the results of analysis.

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes – competencies:

• Identify, interpret and analyse stakeholder needs;
• Apply systems thinking to understand complex system behaviour including interactions between components and with other systems (social, cultural, legislative, environmental, business etc.);
• Identify and apply relevant problem solving methodologies;
• Synthesize innovative solutions, concepts.

Topic 1. Nature of observation of socio-economic objects
Topic 2. Grouping data and its investigation
Topic 3. World indexes and their research
Topic 4. Data Research Indicators.
Topic 5. Sampling: its construction and verification of its representativeness

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