Data Visualization and Analysis (IE 451)

2021 Fall
Faculty of Engineering and Natural Sciences
Industrial Engineering(IE)
3
6.00 / 6.00 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Kemal Kılıç kkilic@sabanciuniv.edu,
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English
Undergraduate
MATH306
Formal lecture,Interactive lecture
Interactive
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CONTENT

Introduction to styles of data analysis techniques and information visualization; Histograms, stem and leaf diagrams, boxplots, quantile plots; assessing distributional assumptions about data; Plotting high- dimensional data with special emphasis on parallel coordinate plots, scatter matrices, star glyphs, treemaps; Class project with real-world data-set provided by the instructor or developing information visualization software program.

OBJECTIVE

The course will address unsupervised learning, supervised learning, association rule mining and feature subset selection problems and introduce various techniques proposed as solutions and present their implementation particularly in the context of operations management. Data Visualization will also be introduced as part of the curriculum.


Among others, probabilistic and statistical methods, clustering algorithms, classification algorithms, multiple linear regression, a priori algorithm, metaheuristics (such as genetic algorithms, simulated annealing, etc.) in the context of feature subset selection will be covered as part of the toolbox that are widely utilized in data mining.

LEARNING OUTCOME

Apply the basic concepts of existing methodologies for data visualization and analysis, as well as machine learning


Model and interpret data, by applying statistics, information visualization, and machine learning techniques.
Extract and clean data from diverse domains
Explore data through visualizations
Conduct challenging technical projects that involve intense data analysis and interpretation.
Apply their knowledge on the best-practices in the real world, regarding the application of theory.
Work independently, as well as in a team, in completing challenging data analysis projects.

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 30
Midterm 50
Assignment 20

RECOMENDED or REQUIRED READINGS

Readings

Readings are posted to SuCourse