Assignments

COURSEWORK:

Coursework consist of Coursework consist of 4 homework assignments, 1 take-home course project on big data analytics, and 1 final exam (grading in parentheses):

Homework (55%)
Final Exam (35%)
Class Participation (10%)

IMPORTANT DATES:

Assignment	Note	Out	Due	Weight
Homework 0	Installation, Set up	Oct 28	--	0%
Homework 1	pySpark and RDDs	Nov 1	Nov 7	7%
Homework 2	Regression in Spark	Nov 9	Nov 16	12%
Homework 3	Classification in Spark	Nov 16	Nov 23	12%
Homework 4	Data Analysis with PCA in Spark	Nov 23	Dec 3	12%
Homework 5	Hands-on with ML-lib and SparkSQL	Dec 3	Dec 10	12%
Final Exam	You are allowed to bring a single, A4-size 'cheat' sheet with your notes	Dec 12 6-8:00PM	--	35%
Class participation	Pop-quizzes (any time during class)	--	--	10%

HOMEWORK:

The goal of the homework is to enable the students to practice the concepts learned in class using real-world datasets.

ASSIGNMENTS ARE DUE AT THE BEGINNING OF LECTURE ON THE DUE DATE.
All assignments are to be done individually. Please see the collaboration policy.
To submit:
- Submit your soft-copy (all source files) on Canvas.
- Return your hard-copy (print out) submission in class.
- Make sure that your answers are legible and coding is clear.
- See course policies for assignment questions, late submissions, graded homework pick-up.

EXAM:

There will be a final exam. It will be closed everything -- no books, slides, computers, etc. You will only be allowed to bring along with you 1 A4-size paper of your own notes ('cheat sheet'), you can use both sides. The final date will be announced during the semester.