Introduction to Data Science (CS 210)

2021 Summer
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
3
6
Özgür Asar ozgur.asar@sabanciuniv.edu,
Click here to view.
English
Undergraduate
IF100 MATH203
Formal lecture,Interactive lecture,Recitation
Interactive,Project based learning
Click here to view.
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

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. The assignments and term project will involve Python, JavaScript languages and open source tools such as R.

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

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 30
Group Project 20
Homework 40
Other 10

RECOMENDED or REQUIRED READINGS

Readings

There will be weekly papers as readings distributed.