Exploratory Data Analysis (EDA) is an approach to data analysis for summarizing and visualizing the important characteristics of a data set. EDA focuses on exploring data to understand the data’s underlying structure and variables, to develop intuition about the data set, and decide how it can be investigated with more formal statistical methods. EDA is distinct from Data Visualization in that EDA is done towards the beginning of analysis and data visualization is done towards the end to communicate one’s finding. This course particularly pays attention to the applied techniques to data visualization narratives. We will draw on case studies from business world, industry to news media.
Exploratory Data Analysis and Visualization (DA 518)
2021 Spring
Faculty of Engineering and Natural Sciences
Data Analytics(DA)
3
6
Hasan Alp Boz hasan.boz@sabanciuniv.edu,
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Doctoral, Master
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CONTENT
PROGRAMME OUTCOMES
1. Develop the ability to use critical, analytical, and reflective thinking and reasoning
2. Reflect on social and ethical responsibilities in his/her professional life.
3. Gain experience and confidence in the dissemination of project/research outputs
4. Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments.
5. Communicate effectively by oral, written, graphical and technological means and have competency in English.
6. Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating.
1. Design and model engineering systems and processes and solve engineering problems with an innovative approach.
2. Establish experimental setups, conduct experiments and/or simulations.
3. Analytically acquire and interpret data.
1. To have acquired basic theoretical knowledge and technical infrastructure in the field of cyber security
2. To have developed a deep experience and understanding on the basic methods and human-induced and techinal weaknesses followed by the existing and future cyber attacks, threats and counterfeiting
3. To be able to analyze an IT infrastructure comprehensively and to determine risk by monitoring the existing weaknesses and to determine a cyber security strategy
4. To take the necessary measures to prevent possible costs and destruction during the occurrence of cyber attacks,
5. To be able to use current cyber security software tools and related software for professional purposes,
6. To follow the cyber security intelligence news and to combine and analyze data from different sources to take measures for preventing or reducing the prospective cyber attacks,
7. To be able to take preventive measures to hinder possible drawbacks by creating a deep understanding and awareness in legal, ethical and social aspects of protecting the security and privacy of personal and corporate data.
1. Comprehend the conceptual foundations of analytical methods and techniques within the scope of business analytics,
2. Acquire theoretical and practical knowledge on applied information systems by developing fundamental programming skills,
3. Improve decision making by turning high-volume data into useful information and integrating data analysis tools
4. Turn high-volume data into useful information by using quantitative models and understanding and managing data analysis techniques, communicate and visualize the results for business use
5. Understand the data quality, data integrity and data accuracy concepts, and occupational ethics regarding data privacy and intellectual property