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Choosing a Programming Language for CS1
Fred Annexstein
CS Curriculum Meeting
April 9, 2015

Current State of Language Adoption for
CS1-type Courses in US
The chart below shows how many of the top 39 departments teach either CS0
or CS1 using the seven most common languages. The bar heights add up to
more than 39 since many schools offer both CS0 and CS1.

Rank University Python Java MATLAB C C++ Scheme
1 Carnegie Mellon 15-110, 15-112 15-122
1 MIT 6.00, 6.01
1 Stanford CS106A
1 UC Berkeley CS61A
5 UIUC CS103 CS125 CS101 CS101
6 Cornell CS1110 CS1112
6 U.Washington CSE140 CSE142
8 Princeton COS126
9 Georgia Tech CS1301, CS1315 CS1371
9 UT Austin CS303E CS312
11 Caltech CS1
11 U. Wisconsin Madison CS302
13 UCLA CS31
13 U. Michigan EECS182 EECS182, 183
15 Columbia ENGI E1006 COMS W1004-1 COMS W1005-1
15 UCSD CSE8A CSE7 CSE5A
15 U. Maryland - College Park CMSC 198C,D,E CMSC131
18 Harvard CS50
19 U. Penn CIS 110, 120
20 Brown CSCI0931 CSCI0150 CSCI0040 0170,0190
20 Purdue CS17700 CS18000 CS15900 CS15800, CS15900
20 Rice COMP 140
20 USC CSCI 101 CSCI 103 CSCI 103
20 Yale CPSC112 CPSC201
25 Duke CompSci 101
25 UMass Amherst CMPSCI 119 CMPSCI 121
25 UNC Chapel Hill Comp 110 Comp 401
28 Johns Hopkins EN600.107
29 NYU CSCI-UA.2 CSCI-UA.0101
29 Penn State CMPSC 121
29 UC Irvine CSE 41
29 U. Minnesota CSci 1001,1901 CSci 1103 CSci 1901
29 U. Virginia CS 1120 CS 1110, 1111
34 Northwestern EECS110 EECS110 EECS111
34 Ohio State CSE201 CSE 205 CSE 202
34 Rutgers CS111
34 UC Davis ECS 10
34 UC Santa Barbara CS8
34 U. Chicago CMSC 12100, 12200, 12300 CMSC 12100, 12200, 12300
CMSC 12100, 12200, 12300 CMSC 10500, 11500
Ref: BLOG@CACM

CS1 Assumptions
• Goals of the CS1 instruction
– learn computer programming – no tech background assumed.
• Must provide students with a broad view of the CS field:
– theory and practice,
– academic and industrial perspectives.
• Course Core
– problem-solving
– program design
– programming implementations
• Learning to program
– is a hands-on activity,
– involves programming projects of various sizes in lab setting.

CS1 Ideals
• Expose students to a variety of programming
methodologies
– procedural and functional decomposition,
– object-oriented methods.
• Avoid narrow or complex prog language issues
• Empower students to see the scope and
possibilities of computer programming.
• Super fun and not scary (scary comes later)

Choosing an introductory
programming language
• The hands-on, experimental nature of the course
implies
– Programming language should enable program
designs that are concise and expressed with
minimal overhead.
• To encourage experimentation the language
should enable
– easy modification and much interactivity.
• To encourage computational thinking about
algorithms and design the language expressions
should limit low-level implementation details.

Problems with a C++ only course
• C++ makes experimentation difficult
– long cycle times between compilations,
– long and indecipherable (link) errors,
– mysterious crashes from pointer or array-bounds errors,
increasing frustration.
– No simple, high-quality, platform independent IDE
• Most C++ textbooks focus on dry, simplistic
database programming projects.

Successes with Python
• Python 1.0 1990  2.0 2000  Python 3.0 2008
• Many platform independent high-quality IDEs
• The most widely used language for CS1 in top CS
programs
• Credited with increasing diversity and female
participation in CS
• All large MOOCs (edX, Coursera, Udacity) use it in
Intro to CS courses
• Many Large Software Projects:
http://en.wikipedia.org/wiki/List_of_Python_software

Python-based Courses and Resources
• UC-Berkeley CS61A
– Composing Programs (based on modernizing SICP)
• Rice University and Coursera-Fundamentals of Computing
• MIT OpenCourseware - Introduction to CS and
Programming
• Harvard and Edx: Introduction to Computer Science
• Harvey Mudd CS5
– CSforAll (text developed under NSF CPATH grant)
• Rose-Hulman CSEE 120 (integration with C)
• Paul Talaga’s Shared Doc

Courses Integrating Python-Low-level Constructs
NPR: How One
College Is Closing
The Computer
Science Gender Gap

C++ vs Python: Design and Function
System Prog w/ C++
• Statically typed
• Compiled
• Reflects underlying
machine architecture
• Useful for performance
intensive tasks
• Device drivers, games,
media engines, telecom,
embedded systems
Scripting w/ Python
• Dynamically typed
• Repl - interactive
• Industrial strength for rapid
development
• Web + Internet frameworks
• Scientific, numerical, and
data mining,
• Automated testing and tools
• Desktop GUIs

Compare Codes - Caesar Ciphers
Python
def main():
plain = list(raw_input('Enter msg: '))
alpha = list('abcdefghijklmnopqrstuvwxyz’)
k = input("Enter key: ")
cipher = ''
for c in plain:
c = c.lower()
if c in alpha:
cipher += alpha[(alpha.index(c)+k)
%(len(alpha))]
print “Encrypted message is: “ + cipher
main()
C++
#include <iostream>
#include <string>
using namespace std;
int main() {
string plain;
cout << "Enter msg: " << endl;
getline(cin, plain);
int k; cout << "Enter key"; cin >> k;
string cipher = "";
for(int i = 0; i < input.length(); i++){
char c = plain[i];
if ((c >= 97) && (c <= 122))
cipher += (((c-97)+ k) % 26) + 97;
}
cout << "Encrypted message is: " << cipher
<< endl;
} //end main

Teaching Data Methods:
List Enumeration and Visualization
>>>text = “When in the course of human events it …”
>>>words = list(text.split())
>>>for w in words:
if w not in stopWords:
print w,
=> course human events people …
________________________________________________
>>>noprimes = [j for d in range(2, 8)
for j in range(d*2, 50, d)]
>>>primes = [x for x in range(2, 50)
if x not in noprimes]
>>> print primes
[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47]

Teaching OOP
Concisely
class Stack:
def __init__(self,size):
self.data = [None]*size
self.size = 0
def push(self,item):
self.data[self.size] = item
self.size = self.size + 1
def pop(self):
self.size = self.size - 1
return self.data[self.size]
def is_empty(self):
return self.size == 0
def is_full(self):
return self.size == len(self.data)
from Stackfile import *
S1 = Stack(3)
for i in
[‘one’,True,3.0]:
S1.push(i)
print S1.is_full()

Python Type Safety
• Full dynamic run-time type checking
• Bounds checking on array subscripts.
• Easy to type and namespace check at the repl. Also
docstrings not stripped, useful for interactive help
• Employs garbage collection
– no problem with dangling pointers or memory leaks.
• No segmentation faults.

Good Design == Fun
• With Python
– easier to learn programming
– easier to complete more interesting projects.
• Just four Iterator types unify much of the language –
automatically apply in for-loop:
– immutable strings,
– list (dynamic array) data structure,
– tuples (immutable lists)
– dictionaries (hash tables).
• These can be used to quickly build sophisticated data
structures for interesting projects.

Libraries == More Fun
• Absence of type declarations
– makes for less code and more flexible programming.
• Huge library (standard and contributed modules) provides
components for
– programming GUIs, client-server applications, html viewers,
databases, animations, and much more.
• Interesting projects can be developed with only a fraction
of the code that would be required in a system language.
• Experience shows that students are
– more productive
– less frustrated
– enjoy the repl.

Addressing concerns of
experience in low-level programming
• Python is an opportunity to
– demonstrate, compare, and experiment with
low-level programming constructs
• Example
– Comparison of codes, e.g., Caesar Cipher
– Coverage of the C-reference implementation
of the Python list construct
• Excellent introduction to DS-style topics
involving pointers and arrays

Rank University Python Java MATLAB
1 Carnegie Mellon 15-110, 15-112 15-122
1 MIT 6.00, 6.01
1 Stanford CS106A
1 UC Berkeley CS61A CS10
5 UIUC CS103 CS125 CS101 CS101
6 Cornell CS1110 CS1112
6 U.Washington CSE140 CSE142
8 Princeton COS126
9 Georgia Tech CS1301, CS1315 CS1371
9 UT Austin CS303E CS312
11 Caltech CS1
11 U. Wisconsin Madison CS302 CS202
13 UCLA CS31
13 U. Michigan EECS182 EECS182, 183
15 Columbia ENGI E1006 COMS W1004-1 COMS W1005-1
15 UCSD CSE8A CSE7 CSE5A
15 U. Maryland - College Park CMSC 198C,D,E CMSC131
18 Harvard CS50
19 U. Penn CIS 110, 120
20 Brown CSCI0931 CSCI0150 CSCI0040 0170,0190
20 Purdue CS17700 CS18000 CS15900 CS15800, CS15900
20 Rice COMP 140
20 USC CSCI 101 CSCI 103 CSCI 103
20 Yale CPSC112 CPSC201
25 Duke CompSci 101
25 UMass Amherst CMPSCI 119 CMPSCI 121
25 UNC Chapel Hill Comp 110 Comp 401
28 Johns Hopkins EN600.107
29 NYU CSCI-UA.2 CSCI-UA.0101
29 Penn State CMPSC 121
29 UC Irvine CSE 41
29 U. Minnesota CSci 1001,1901 CSci 1103 CSci 1901
29 U. Virginia CS 1120 CS 1110, 1111
34 Northwestern EECS110 EECS110 EECS111
34 Ohio State CSE201 CSE 205 CSE 202
34 Rutgers CS111
34 UC Davis ECS 10
34 UC Santa Barbara CS8
34 U. Chicago CMSC 12100, 12200, 12300 CMSC 12100, 12200, 12300 CMSC 12100, 12200,
12300 CMSC 10500, 11500

Sample Python Lab #1: Freshman CS1
Lab #1 Algorithm:
0. Create a list of all the words
from web transcript.
1. Remove ‘Stop’ Words.
2. Determine Frequency of
remaining words.
3. Find the word with the largest
frequency.
4. Print it – and its frequency –
and remove it.
5. Prompt the user to continue or
not.
6. If yes then go to step 3. Else
end the program gracefully.
https://compuzzle.wordpress.com/2015/01/08/project-1-what-are-the-
most-significant-words-in-the-declaration-of-independence/

Sample Midterm Python Lab #6:
Freshman CS1
ADTs, OperatorOverloading, Recursion
Lab: Complex Plane drawing of
n-dimensional Hypercubes
nth_roots_of_unity =
[math.e**(complex(0,1)*2*math.pi*k/n)
for k in range(n)]
def binary(i,n):
if (n == 0):
return ''
else:
if (i % 2 == 0):
return binary(i/2,n-1) + '0'
else:
return binary((i-1)/2, n-1)+'1'
https://compuzzle.wordpress.com/2015/03/19/adts-and-operator-
overloading/

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