Data science spans a large variety of disciplines and requires a collection of skills. This course is intended to tour the basic techniques of data science from manipulation and summarizing the important characteristics of a data set, exploratory data analysis, basic statistical modeling and visualization.
Introduction to Data Science (CS 210)
2023 Fall
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
3
6
Onur Varol onur.varol@sabanciuniv.edu,
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English
Undergraduate
IF100 MATH203
Formal lecture,Interactive lecture,Recitation
Interactive,Project based learning
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| Programs\Type | Required | Core Elective | Area Elective |
| Battery Science and Engineering Minor | * | ||
| Computer Science and Engineering | * | ||
| Computer Science and Engineering | * | ||
| Data Science and Analytics | * | ||
| Decision and Behavior Minor | * | ||
| Electronics Engineering | * | ||
| Electronics Engineering | * | ||
| Industrial Engineering | * | ||
| Industrial Engineering (Previous Name: Manufacturing Systems Engineering) | * | ||
| Materials Science and Nano Engineering | * | ||
| Materials Science and Nano Engineering (Previous Name: Materials Science and Engineering) | * | ||
| Mechatronics Engineering | * | ||
| Mechatronics Engineering | * | ||
| Microelectronics | * | ||
| Molecular Biology, Genetics and Bioengineering | * | ||
| Molecular Biology, Genetics and Bioengineering (Pre. Name: Biological Sciences and Bioengineering) | * | ||
| Physics Minor | * | ||
| Telecommunications | * |
CONTENT
OBJECTIVE
Data science spans a large variety of disciplines and requires a collection of skills. This course is intended to tour the basic techniques of data science from manipulation and summarizing the important characteristics of a data set, basic statistical modeling, web programming and visualization.
LEARNING OUTCOMES
- Learning the fundamentals of data science pipeline
- Learning how to explore and experiment with data
- Learn basic statistics (sampling techniques, mean, variance, outliers, Central Limit theorem, distributions) and machine learning techniques (clustering) that are necessary to analyze data: big and small
- Perform a statistical analysis on sample socio-economic data
- Building an understanding of data analytics techniques (data collection, cleaning, exploratory techniques, modeling and presentation)
- Develop competency in the Python programming language within the course project
- Design and run experimental tests to evaluate hypotheses about data
Update Date:
ASSESSMENT METHODS and CRITERIA
| Percentage (%) | |
| Final | 40 |
| Group Project | 30 |
| Homework | 30 |
RECOMENDED or REQUIRED READINGS
| Readings |
There will be weekly papers as readings distributed. |