Data analysis and visualization

Description: A practice-oriented form of training within the framework of this discipline allows to implement an important stage of data mining. Learn the general concepts of data mining along with basic methodologies and applications. Data visualization will allow to assess the degree of compliance with expectations and the suitability of the data for analysis, hypotheses about the patterns and the necessary procedures for primary processing. The methods of visualization of the initial data, visualization of the results of the primary processing, visualization of the intermediate and final results will be implemented.

Amount of credits: 5

Пререквизиты:

• Analysis and Simulation of Informational Processes

Course Workload:

Component: Component by selection

Cycle: Base disciplines

Goal

• The aims of the course: to introduce PhD students to the concepts and techniques of analysis and visualization methods; to develop skills of using recent data mining software for solving practical problems, to gain experience of doing independent study and research.

Objective

• Formation of the theoretical knowledge and practical basic skills in the collection, storage, processing and analysis of data
• Develop skills and practical skills to analyze data to tackle a wide range of applications, modeling data storage and processing, prediction of complex indicators

Learning outcome: knowledge and understanding

• Understand the theory and fundamentals of storage, processing and analysis of data, advanced tools for collection, storage, transmission and visualization of data

Learning outcome: applying knowledge and understanding

• To be able to choose effective methods of solving applied problems using Data Mining technology in the field of business intelligence and research;
• To be able to process and analyze big amounts of data using modern software

Learning outcome: formation of judgments

• identify the major ethical and legal issues of analytics
• the ability to form idea of the non-standard approaches to solving problems and in looking for new original ideas and design techniques using technology Data Mining in the area of business intelligence and research

Learning outcome: communicative abilities

• the ability to read and translate IT literature, work with software applications in the field of mining with an English interface

Learning outcome: learning skills or learning abilities

• skills to acquire new knowledge in the field of professional and further education

Teaching methods

When conducting training sessions, the use of the following educational technologies is provided: - Technology of research activities - Technology of educational and research activities - Communication technologies (discussions, press conference, brainstorming, educational debates, etc.) - Information and communication (including remote) technologies

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Introduction What Kinds of Data Can Be Mined
• Data Objects and Attribute Types
• Data Visualization
• Data Preprocessing
• Data Reduction
• Classification: Basic Concepts
• Bayes Classification Methods
• Rule-Based Classification
• Model Evaluation and Selection
• Classification: Advanced Methods
• Cluster Analysis Partitioning Methods
• Density-Based Methods
• Mining Frequent Patterns, Associations, and Correlations: Basic Apriori Algorithm: Finding Frequent Itemsets by Confined Candidate Generation Concepts and Methods
• Data Mining Trends and Research Frontier
• Data Mining Applications

Key reading

• Han J. Data Mining: Concepts and Techniques The Morgan Kaufmann Series in Data Management Systems (Selected Titles) 212 y. p.740
• J. Godfrey. Methods for Data Science: III - Data Visualization and R, Lulu.com (November 25, 2015).-362 p. ISBN-13: 978-1329714878

Further reading

• Charles D. Hansen and Chris R. Johnson, Visualization Handbook, Academic Press, 2004.
• V.M. Sue, M.T.Griffin. Data Visualization & Presentation With Microsoft Office, SAGE Publicationc, Inc., 2016.-337p. ISBN-13: 978-1483365152.