Computer Science 135 :: Diving into the Deluge of Data

Particulars

09.00 Section

Professor: Brent Heeringa
Email: heeringa [at] cs [dot] williams [dot] edu
Office: Thompson Chemistry Laboratory 306
Phone: 413.597.4711
Course Classroom: Thompson Chemistry Laboratory (TPL) 205
Lecture Time: MWF - 09.00-09.50
Office Hours: T: 10.00-12.00, W: 10-11

11.00 Section

Professor: Bill Jannen
Email: jannen [at] cs [dot] williams [dot] edu
Office: Thompson Physics Laboratory 204
Phone: 413.597.2309
Course Classroom: Biology (TBL) 202
Lecture Time: MWF - 11.00-11.50
Office Hours: Tu: 14.30-16.30, W: 12.30-13.30

Lab Time: Mon - 13.00-14.25; 14.35-16.00
Teaching Assistants: Nick Post, Ben Solis-Cohen, Kelsie Dalton, Julia Goldman, Nathan Anderson, Vebiyou Metaferia, Julian Vera
Teaching Assistants' Evening Office Hours:
- Nick: Tu 21.30-23, Th 21.30-23
- Ben: M 19-20.30, W 19-20.30
- Julia: Tu 19-20.30, W 21-23
- Nebiyou: Tu 20-21.30, W 20-21.30, Th 19-23
- Miranda: M 20.30-23, Th 20.30-21.30

Honor Code

Please read the honor code for computer science classes.

Grading

10 labs, each worth 5% of your grade, lowest grade dropped.
6 homework assignments, each worth 2% of your grade, lowest grade dropped.
1 final project, worth 20% of your grade.
1 take-home midterm, worth 20% of your grade.
An overall participation score, worth 5% of your grade.

Tools

Python
: Our primary programming language is Python. In particular, we will use Python 3, which is the future of python.
- Python 3.5 Documentation
- Common uses of decorators
- Why Python is Slow
Git
: Source code control via git with lab assignment distribution, collection, and comment done via private repositories on www.github.com.
- think like a git
- git ready: one git command a day
- A good git reference is the pro git book
- John Wiegly's book git from the bottup up has the low-level details.
virtualenv
: Dependency isolation via virtualenv.
- Python guide to virtualenv
pip
: Installing packages via pip
Working Outside the Lab
: Every Mac computer on campus has all of the tools needed to complete the course assignments, but if you want to recreate the same course development environment on your own Mac, we have created a short guide.

Readings

Many of our readings will come from Allen B. Downey's book Think Python: How to Think Like a Computer Scientist. The online version of this text is for Python 2.7, so at times I will refer you to a PDF version, which was adapted by Michael Kart for Python 3.0.

Lectures

Lecture 1 :: 09 September 2016

Topics: Overview of Diving into the Deluge of Data
Goals: explore and experience computer science and problem solving; describe course structure
Readings:
- Whetting your Appetite
Notes
Slides

Lecture 2 :: 12 September 2016

Topics: operating systems, unix, bash, and compiled versus interpretted languages
Goals: gain a rudimentary understanding of the operating system, filesystem, shell, programming languages, and the REPL
Readings:
- Chapter 1 of Think Python.
- A Unix tutorial. Write down questions and bring them to the first lab. The tutorial is probably best read in front of a computer equipped with a unix operating system (like OS X or Linux). To launch a shell on OS X, look for the Terminal application in /Applications/Utilities.
- Bash on Wikipedia
- How does a computer program work?
Notes
Slides

Lecture 3 :: 14 September 2016

Topics: the python interpreter, variables, numbers, and basic string type
Goals: understand variable declaration and assignment, integers, reals, mathematical operations on different numeric types, basic strings
Readings:
- Chapter 2 from Think Python
- Numbers from the Python Tutorial.
Additional Readings and Information:

More information about escaping

Notes

Lecture 4 :: 16 September 2016

Topics: conditionals, iteration, loops
Goals: explore standard looping constructs in python; practice writing sophisticated loops;
Readings:
- Read the while and break statements from Think Python
- Sections 4.1-4.5 in the Python Tutorial
Notes

Lecture 5 :: 19 September 2016

Topics: functions, parameters and arguments, scope, namespaces, modules, pseudocode

Goals: understand function declaration, parameters and arguments; learn about Python modules and packages; use pseudocode to describe language-independent ideas

Readings:
- Chapter 3 from Think Python
- Modules from the Python tutorial
Additional Readings and Information:

All Python objects have boolean values
Where does sys.path get set?

Notes
Slides

Lecture 6 :: 21 September 2016

Topics: strings and string manipulation

Goals:

Readings:
- Strings from the Python Tutorial
- Chapter 8: Strings from Think Python
Notes
Slides

Lecture 7 :: 23 September 2016

Topics: sequences, output, formatting

Goals:

Readings:
- Sequences
- Text Sequence Type
- Format String Syntax -- pay the most attention to the examples.
Notes
Slides

Lecture 8 :: 26 September 2016

Topics: lists and search

Goals:

Readings:
- Lists from Think Python
- Python Tutorial on Lists
Notes
Slides

Lecture 9 :: 28 September 2016

Topics: files and streams

Goals:

Readings:
- Chapter 14 of Think Python
- Reading and Writing files
- Understanding Python's with Statement
- iterable versus iterator
Notes
Slides

Lecture 10 :: 30 September 2016

Topics: Binary strings and Gray codes

Goals: Understand how numbers are represented in binary, fundamental bitwise operations

Readings:
- Binary numbers
- Gray codes part 1
- Gray codes part 2 -- just section titled "Generating a Gray Code" (focus on Figures 2-1 and 2-2)
Notes
Slides

Lecture 11 :: 03 October 2016

Topics: More Binary strings and Gray codes

Goals: Understand how numbers are represented in binary, aliasing (using copy.deepcopy for lists)

Readings:
- Binary numbers
- Gray codes part 1
- Gray codes part 2 -- just section titled "Generating a Gray Code" (focus on Figures 2-1 and 2-2)
Notes
Slides

Lecture 12 :: 05 October 2016

Topics: Comma Separated Values (CSV)

Goals: Understand how CSV Readers and Writers provide a clean interface for interacting with CSV files.

Readings:
- Python CSV Documentation
Notes
Slides

Lecture 13 :: 12 October 2016

Topics: Classes

Goals: Learn how to define new types; understand the syntax and semantics of classes and methods; understand the difference between objects and types.

Readings:
- Classes and objects from Think Python.
- Classes from the Python Tutorial.
Notes
Slides

Lecture 14 :: 14 October 2016

Topics: Binary Trees and Recursion

Goals: Understand how to create a dynamic, inductive structure. Think about thinking inductively and recursively.

Readings:
- Trees from Think like a Computer Scientist.
Notes
Slides

Lecture 15 :: 17 October 2016

Topics: Treemaps

Goals: Develop a greedy algorithm. Learn about Treemaps and why they're useful for visualizing information. Examine the relationship between Treemaps and binary trees.

Readings:
- Treemaps as Art
- Treemap History
Notes
Slides

Lecture 16 :: 19 October 2016

Topics: Midterm Review

Goals: Broadly review course material

Notes

Midterm :: 20 October 2016

Open-book, take-home exam
May be completed during any continuous 2-hour period
Completed exams are due at the start of class on Monday

Lecture 17 :: 21 October 2016

Topics: Problem-solving session

Goals: Practice solving problemw with Python

Notes

Lecture 18 :: 24 October 2016

Topics: Object-oriented programming, Inheritance, Access Control, Static Methods

Goal: Understand classes in the contex of object-oriented programming; how python OOP relates to OOP at large

Readings:
- Classes Tutorial
- Python Docs (inheritance)
Notes
Slides

Lecture 19 :: 26 October 2016

Topics: Dictionaries and Sets

Goals: Understand the benefits of dictionaries; understand when a dictionary is an appropriate choice of data structure

Readings:
- Python Tutorials: Sets and Dictionaries
- Python Docs: set and dict.
Notes
Slides

Lecture 20 :: 28 October 2016

Topics: More Dictionaries, Sets, and JSON

Goals: Understand the difference between dictionaries and sets; learn about JSON

Readings:
Notes
Slides

Lecture 21 :: 31 October 2016

Topics: Representation: String encodings, Unicode, and UTF-8

Goal: Understand Unicode, how encodings and decodings relate to Unicode, and the UTF-8 encoding standard

Readings:
Notes
Slides

Lecture 22 :: 02 November 2016

Topics: Applications of Dictionaries and Sets; A Matplotlib Tutorial

Goals: Gain confidence with dictionaries; understand matplotlib

Readings:
Notes
Slides

Lecture 23 :: 04 November 2016

Working from Examples: Matplotlib

Goals: Gain confidence finding and analyzing new data.

Readings:
Example Code
Example Data

Lecture 24 :: 7 November 2016

Topics: Exceptions and Iterators

Goal: Introduce modern error-handling with exceptions; learn about iterable objects and iterators

Readings:
- Errors and Exceptions
- Built-in Exceptions
- Iterators and Generators
- Iterators and Generators (this is written for Python 2.x so allreferences to the method next should be to the method __next__ in Python 3.x.
Notes
Slides

Lecture 25 :: 9 November 2016

Topics: More Iterators, Generators

Goal: Iterable objects, iterators, and generators

Readings:
- Iterators and Generators
- Iterators and Generators (this is written for Python 2.x so allreferences to the method next should be to the method __next__ in Python 3.x.
- understanding yield and generators
Notes
Slides

Lecture 26 :: 11 November 2016

Topics: Regular Expressions

Goals: Understand the syntax, semantics, power, and traps of regular expressions