SUMMER SESSION

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STAT 304-0 : Data Structures and Algorithms For Data Science

Description

This course will introduce students how to measure the efficiency of an algorithm using asymptotic analysis. In addition, students will learn a variety of data structures and basic algorithmic design techniques. Python will be used for this course. Students should be prepared for a significant amount of hands-on programming.

Prerequisites/Registration Requirements: STAT 202 (or STAT 210 or STAT 232) and COMP_SCI 110 (or COMP_SCI 111) or consent of the instructor. Students are expected to be comfortable with Python Programming.

Learning Objectives: • ANALYZE the complexity of an algorithm using asymptotic analysis (Big O Notation). • APPLY basic algorithmic techniques such as recursion, binary search, sorting, divide and conquer, and dynamic programming to programming challenges. • UNDERSTAND collection data types in Python deeply. • MASTER the strengths and weaknesses of a variety of data structures, so one can choose the best data structure for real data applications • ORGANIZE, STORE, and PROCESS data efficiently using appropriate data structures and algorithms

Teaching Method: Remote Synchronous, specifically, the class will be taught Tuesday, Wednesday, and Thursday. Lecture, slides.

Evaluation Method: Students will be evaluated by (1) in-class quizzes, (2)Homework assignments, (3)Exams.

Required Class Materials: None required.

Suggested Class Materials: Roberto Tamassia, Michael H. Goldwasser, Michael T. Goodrich Data Structures and Algorithms in Python, 1st Edition (Wiley, ISBN: 9781118290279) • Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein Introduction to Algorithms, 3rd Edition (The MIT Press, ISBN: 9780262033848) • Useful: (i) Data Structure - Python documentation, (ii) Common Python Data Structures (RealPython)

Additional Information:

PLEASE NOTE: The registration period for ALL summer courses is April 8, 2024 through June 16, 2024, even if the course begins later in the summer.