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95-869 Big Data and Large Scale Computing
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Assignments

COURSEWORK:

Coursework consist of Coursework consist of 5 homework assignments, 1 final exam, and after-class quizzes that will determine your class participation (grading in parentheses):

IMPORTANT DATES:
Assignment Note Out Due Weight
Homework 0
 Installation, Set up
9/1
--
0%
Homework 1
 pySpark and RDDs
9/10
9/17
11%
Homework 2
 Regression in Spark
9/17
9/24
12%
Homework 3
 Classification in Spark
9/24
10/1
12%
Homework 4
 Data Analysis with PCA in Spark
10/1
10/8
12%
Homework 5
 Hands-on with ML-lib and SparkSQL
10/8
10/15
13%
Progress
 Quizzes (posted on Canvas)
after each class
wihtin 2 days
15%
Final Exam
 on Canvas
TBD
--
25%

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 11:59PM EDT OF THE DUE DATE.
  • All assignments are to be done in groups. Please see the collaboration policy.
  • Submission (only electronically):
    • Submit all of your source files on Canvas.
    • Also submit your print out/pdf with answers on Canvas.
    • Make sure that your answers are legible and coding is clear.
    • See course policies regarding questions about the assignments, late submissions, etc.


EXAM:

There will be a final exam. It will be posted on Canvas. Decision regarding students taking the exam synchronously or asynchrounously will be made later in the semester.

PROGRESS:

Progress and participation will be quantified via quizzes. Each quiz will be a list of multiple-choice questions, to be posted on Canvas after each class -- with a due date and time.
There will be a total of 10-12 such quizzes during the semester, 1 point each. We will select the highest-scoring 10 out of those for each student, for a total of 15% of the final grade. Students who attempt all the questions in the quiz will get 0.5 point even if they answer all these questions wrong. Students who do not attempt to answer any questions in a quiz will receive 0 points.