The course review made a strong recommendation to introduce a unit on algorithms and data structures into the MIT course structure. We already had FIT5211, but that unit had FIT9133 as a prerequisite so it wasn't accessible for many MIT students, and according to the CE the material in FIT5211 was very introductory (more at undergrad level than masters level). The rationale for FIT9136 was therefore to combine an introduction to Python with an introduction to algorithms, and move it to level 9. Since there is already a similar undergraduate unit, FIT1045, the idea was to exploit synergies and base FIT9136 on FIT1045.

Define here the role, relationship and relevance of the unit

Upon successful completion of this unit, students should be able to:

1. design, construct, test and document Python programs;
2. demonstrate how basic data types/structures function;
3. evaluate different algorithms and analyse their complexity;
4. translate problems into algorithms with appropriate implementations by investigating different strategies for the algorithm development

020103

This unit introduces the Python programming and the basics of data structure and algorithms including their design, analysis and implementation in Python.

Students will experience working with Python implementation of data structures and algorithms widely used in modern programming language to solve simple problems. Topics covered in this unit are the programming basics including IOs, control structures, and concepts of object-oriented programming; data structures and algorithms including lists, stacks, queues, trees, recursion and searching/sorting algorithms.

Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers (2012). How to Think Like a Computer Scientist: Learning Python 3 (The Rhodes Local Edition). Available at: http://openbookproject.net/thinkcs/python/english3e/

Brad Miller, David Ranum, Jeffrey Elkner, Peter Wentworth, Allen B. Downey, Chris Meyers and Dario Mitchell (2012). How to Think Like a Computer Scientist (Interactive Edition). Available at: http://interactivepython.org/runestone/static/thinkcspy/index.html

Brad Miller and David Ranum (2014). Problem Solving with Algorithms and Data Structures using Python (Interactive Edition). Available at: https://runestone.academy/runestone/books/published/pythonds/index.html

Technological requirements

Software required for practical exercises and assignments: Python 3.5 or 3.6, Anaconda (Jupyter Notebook)

Additional software: Integrated development environments for Python (e.g. Python IDLE, PyCharm, Spyder)

Software available for download from the official websites: https://www.python.org/downloads/ https://www.anaconda.com/distribution https://www.jetbrains.com/pycharm/download/

Students are encouraged to bring their own devices to the lectures that can access the web (e.g. laptop, tablet, smartphone, etc).

On-campus

Peer assisted learning

Examination (2 hours and 10 minutes): 50%; In-semester assessment: 50%

1. Assignment 1 - 20% - ULO: 1, 2, 3
2. Assignment 2 - 30% - ULO: 1, 2, 3, 4
3. End of semester examination - 50% - ULO: 1, 2, 3, 4

Minimum total expected workload equals 12 hours per week comprising:

• Contact hours for on-campus students:
  • Two hours/week lectures
  • Two hours/week laboratories
• Additional requirements:
  • A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision.

FIT