Introduction to Programming for Data Science (201-0-20)
Instructors
Emre Besler
Meeting Info
Swift Hall 107: Mon, Wed, Fri 1:00PM - 1:50PM
Overview of class
This course covers essential programming concepts and best practices needed to implement Data Science and Statistical methods as effectively and efficiently as possible. Students will develop the ability to write codes in both Python and R languages.
Learning Objectives
Write, execute, debug and test code in Python and R
Use conditional statements and loops to implement various tasks
Create user-defined and recursive functions to create specialized code blocks
Incorporate the appropriate data structures of a programming language to handle data
Translate a problem from layman terms to a coding problem in Python and R
Incorporate best programming practices for writing efficient code in Python and R
Incorporate best practices for code reproducibility (version control, style guides, and commenting)
Teaching Method
There will be three 50-minute lectures per week. The lectures will mostly include in-class coding with explanatory notes as comments on the script; along with some diagrams to visualize some coding concepts better.
Evaluation Method
Students will be assessed on the learning objectives with:
1) Homework assignments: Students will have six homework assignments to practice and demonstrate the coding techniques taught during class hours.
2) Mid-term exam: Students will have a mid-term exam, where they will be assessed on their Python coding proficiency.
3) Final exam: Students will have a final exam, where they will be assessed on both their Python and R coding proficiencies.
Class Materials (Required)
A Practical Introduction to Python Programming by Brian Heinold
An Introduction to R by Alex Douglas
Class Attributes
Empirical and Deductive Reasoning Foundational Dis
Formal Studies Distro Area