CSCI 127: The Joy and Beauty of Data

Catalog Description

Credits: 4
Prerequisites: CSCI 107, The Joy and Beauty of Computing OR prior computer science experience
Corequisites: M 151
Description: Provides a gentle introduction to the exciting world of big data and data science. Students expand their ability to solve problems with Python by learning to deploy lists, files, dictionaries and object-oriented programming. Data science libraries are introduced that enable data to be manipulated and displayed.

The online textbook, How To Think Like a Computer Scientist, is freely available.
The current version of Python (version 3.6) is freely available.

Students will extend computational thinking skills learned in The Joy and Beauty of Computing.
Students will expand their abilities to program in Python using the online resource How to Think Like a Computer Scientist.
Students will be introduced to data science and learn why it is important in today's world.
A typical day will include a short introduction to a topic, followed by students learning about the topic on computers.
Students will be evaluated based on programming assignments and in-class exams (practicums).
Guests from the computing industry and data science industry will occasionally visit class.
Students will gain in-demand skills that better enable them to work or continue their studies in the areas of computer science and/or data science.

At the end of the course, students should be able to

Be introduced to the broad area of data science and its relevance.
Utilize arrays, lists, files and dictionaries to solve problems in Python.
Utilize fundamental object oriented principles such as classes, objects, methods and inheritance to solve problems in Python.
Utilize data science libraries to solve data science problems in Python.

Lists: introduction, values, length, accessing elements, membership, concatenation, repetition, slices, mutability, deletion, references, aliases, cloning, methods, append, for loops, lists as parameters, comprehensions, nested lists, strings vs. lists, type conversion, tuples
Files: working with data files, finding a file, reading a file, iterating over lines in a file, alternative file reading methods, writing files
Dictionaries: introduction, operations, methods, aliasing, copying, sparse matrices
Object Oriented Basics: object-oriented programming, changing perspective, user defined classes, constructors, methods, objects as arguments, converting an object to a string
More Object Orientation: fractions, mutability, sameness, arithmetic methods
Modules: NumPy, SciPy, pandas, matplotlib, scikit-learn

At the end of the semester, grades will be determined (after any curving takes place) based on your class average as follows:

Last modified: April 3, 2017.