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.
Introduction to Data Science (CS 210)
Programs\Type | Required | Core Elective | Area Elective |
BA- Political Science | |||
BA-Cultural Studies | |||
BA-Cultural Studies | |||
BA-Economics | |||
BA-Economics | |||
BA-International Studies | |||
BA-International Studies | |||
BA-Management | |||
BA-Management | |||
BA-Political Sci.&Inter.Relat. | |||
BA-Political Sci.&Inter.Relat. | |||
BA-Social & Political Sciences | |||
BA-Visual Arts&Visual Com.Des. | |||
BA-Visual Arts&Visual Com.Des. | |||
BS-Biological Sci.&Bioeng. | * | ||
BS-Computer Science & Eng. | * | ||
BS-Computer Science & Eng. | * | ||
BS-Electronics Engineering | * | ||
BS-Electronics Engineering | * | ||
BS-Industrial Engineering | * | ||
BS-Manufacturing Systems Eng. | * | ||
BS-Materials Sci. & Nano Eng. | * | ||
BS-Materials Science & Eng. | * | ||
BS-Mechatronics | * | ||
BS-Mechatronics | * | ||
BS-Microelectronics | |||
BS-Molecular Bio.Gen.&Bioeng | * | ||
BS-Telecommunications | * | ||
Decision and Behavior | |||
Physics |
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 OUTCOME
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 | 30 |
Group Project | 20 |
Homework | 40 |
Other | 10 |
RECOMENDED or REQUIRED READINGS
Readings |
There will be weekly papers as readings distributed. |