Introductory Programming with Python Brendan McCane

Hitchhiker’s Guide to Programming Programming is hard. You just won’t believe how vastly, hugely, mind-bogglingly hard it is. I mean, you may think making a decent sandwich is hard, but that’s just peanuts to programming.

Programming is hard. You just won’t believe how vastly, hugely, mind-bogglingly hard it is. I mean, you may think making a decent sandwich is hard, but that’s just peanuts to programming.

Donald Knuth says: In fact, my main conclusion after spending ten years of my life working on the TEX project is that software is hard.

In fact, my main conclusion after spending ten years of my life working on the TEX project is that software is hard.

CS1 Average failure rate for CS1 courses is 33% (min 0%, max 60%). Most universities teach Java, C# or C++. Industrially relevant and OO is more “natural” right?

Average failure rate for CS1 courses is 33% (min 0%, max 60%).

Most universities teach Java, C# or C++.

Industrially relevant and OO is more “natural” right?

Design Principles As simple as possible Few magical incantations Immediate feedback (no compile/run cycle) A practical and modern language

As simple as possible

Few magical incantations

Immediate feedback (no compile/run cycle)

A practical and modern language

Target Audience Students majoring in computer science Python in semester 1 (optional) Java in semester 2 (compulsory) Students not majoring in computer science Mostly science or technical majors

Students majoring in computer science

Python in semester 1 (optional)

Java in semester 2 (compulsory)

Students not majoring in computer science

Mostly science or technical majors

Curriculum We modified an open source textbook How to Think Like a Computer Scientist You can download our version from: http://www.cs.otago.ac.nz/staffpriv/mccane/teaching.html

We modified an open source textbook

How to Think Like a Computer Scientist

You can download our version from:

http://www.cs.otago.ac.nz/staffpriv/mccane/teaching.html

Curriculum Introduction Variables, expressions Python builtins Functions (2) Conditionals More functions Test Driven Development Files and modules Iteration (2) GUI programming Case study (2) Strings (2) Lists (2) Tuples and sets Dictionaries System Programming OOP Case study

Introduction

Variables, expressions

Python builtins

Functions (2)

Conditionals

More functions

Test Driven Development

Files and modules

Iteration (2)

GUI programming

Case study (2)

Strings (2)

Lists (2)

Tuples and sets

Dictionaries

System Programming

OOP

Case study

Course Structure Tight integration between lectures and laboratories 24 lectures, 21 laboratories Terms requirement – students had to submit 18 labs (not marked) Labs had basic + advanced exercises Lectures were programming demonstrations

Tight integration between lectures and laboratories

24 lectures, 21 laboratories

Terms requirement – students had to submit 18 labs (not marked)

Labs had basic + advanced exercises

Lectures were programming demonstrations

Assessment Mid-semester test (20%) – multi-choice Final Exam (60%) – multi-choice Mastery tests (2 x 10%) Programming problems under test conditions Tests published beforehand 4 sections per test, each worth 2.5% Each section pass/fail Graded based on doctests + code inspection

Mid-semester test (20%) – multi-choice

Final Exam (60%) – multi-choice

Mastery tests (2 x 10%)

Programming problems under test conditions

Tests published beforehand

4 sections per test, each worth 2.5%

Each section pass/fail

Graded based on doctests + code inspection

Mastery Test Example (lab 9) def score(numbers): """ give the average of the numbers excluding the biggest and smallest one >>> score([2, 7, 9, 10, 13, 1, 5, 12]) 7.5 >>> score([3, 7, 2.5, -4]) 2.75 """

def score(numbers):

"""

give the average of the numbers excluding the biggest

and smallest one

>>> score([2, 7, 9, 10, 13, 1, 5, 12])

7.5

>>> score([3, 7, 2.5, -4])

2.75

"""

Final Exam Example def matrix_to_sparse(in_matrix): sparse = {} for row_index,row in enumerate(in_matrix): for col_index,val in enumerate(row): if val != 0: sparse[(row_index,col_index)] = val return sparse matrix = [[0,0,1], [0,2,0], [3,0,0]] sparse = matrix_to_sparse(matrix) print sparse[(2,0)], sparse[(1,1)], sparse[(0,2)]

def matrix_to_sparse(in_matrix):

sparse = {}

for row_index,row in enumerate(in_matrix):

for col_index,val in enumerate(row):

if val != 0:

sparse[(row_index,col_index)] = val

return sparse

matrix = [[0,0,1], [0,2,0], [3,0,0]]

sparse = matrix_to_sparse(matrix)

print sparse[(2,0)], sparse[(1,1)], sparse[(0,2)]

Outcomes

Outcomes 172 students attended first lab 28% A 23% B 22% C 27% failed (19% failed terms)

172 students attended first lab

28% A

23% B

22% C

27% failed (19% failed terms)

Notable Comments Easy topics: first half Hard topics: second half Best aspect: labs I would like to change: “a move away from extensive use of programming” “ F*%! Matrices”

Easy topics: first half

Hard topics: second half

Best aspect: labs

I would like to change: “a move away from extensive use of programming”

“ F*%! Matrices”

Statistical Results Java with Python better than Java without (p=0.001) Java 2009 better than Java 2008 (p=0.0007) Java without Python (2009) not better than Java 2008 (p=0.18)

Java with Python better than Java without (p=0.001)

Java 2009 better than Java 2008 (p=0.0007)

Java without Python (2009) not better than Java 2008 (p=0.18)

Conclusions Python is a joy to teach Learning Python is a good pre-cursor for learning Java Open source textbooks are fantastic Mastery tests are masterful Force students to attend labs

Python is a joy to teach

Learning Python is a good pre-cursor for learning Java

Open source textbooks are fantastic

Mastery tests are masterful

Force students to attend labs