teach:fpro:lectures:20

**Master in Informatics and Computing Engineering
Programming Fundamentals
Instance: 2020/2021**

—

By the end of this class, the student should be able to:

- Describe why algorithm analysis is important
- Use “Big-O” to describe execution time
- Describe the “Big-O” execution time of common operations on Python lists and dictionaries

- What is Algorithm analysis
- Order of growth
- Big-O notation

- Performance of Python data structures
- Lists
- Dictionaries

- Allen Downey,
*Think Python — How to Think Like a Computer Scientist*, 2nd Edition, Version 2.4.0, Green Tea Press, 2015 HTML (Annex B)

- J. Correia Lopes,
*Study materials*, GitHub (Notebook 20) - Brad Miller and David Ranum,
*Problem Solving with Algorithms and Data Structures using Python*HTML (Chapter 3) - Brad Miller and David Ranum,
*Problem Solving with Algorithms and Data Structures using Python*HTML (Section 6.3, Section 6.4) - ZOOM lecture's room (requires password): https://videoconf-colibri.zoom.us/j/83800564127
- Moodle, Moodle > Activity LE20

- What is Algorithm analysis. Order of growth. Big-O notation. Performance of Python data structures: Lists, Dictionaries.

— *FPRO, 2020/21*

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teach/fpro/lectures/20.txt · Last modified: 03/01/2021 17:44 by Correia Lopes