Data Structures, Algorithms, and Their Applications Within Computer Systems

"Peeling the layers of how computers work" -- Your Instructor

Course Information

• Course Number: Data Structures, Algorithms, and Their Applications Within Computer Systems
• Semester: Spring 21
• Piazza: Forum Board
  Use piazza instead of e-mail for all course related matters
  • This way all course staff can answer questions
  • Class as a whole can participate and learn in discussions
  • Code also formats better nicely pasted in
  • (Linking your github is also easy for private posts)
• Section 1: CS 5008/5009 (Mike's Section)
• Location: Online
• Schedule: Module and lab released Wednesday by midnight, Thursday 1:35-4pm EST live session on teams (review lecture, lab, and group discussion)
• Teams Link for Live Classes: Microsoft Teams Link
• Section 2: CS 5008/5009 (Vidoje's Section)
• Location: Richards Hall 236
• Schedule: 6-9:15pm EST

Instructor

• Instructor: Mike Shah and Vidoje Mihajlovikj
• E-mail: mikeshah or v.mihajlovikj( a t )Northeastern{dot}edu
Use e-mail primarily for personal matters
• e.g. I have a co-op interview and need to reschedule XYZ
• e.g. I am away for a conference on date X to date Y
• e.g. I am away for a doctors visit.
• (Read How to send an e-mail)
• Office:tbd
• Student Hours: In Location Online
  • Review course material with me and ask questions
  • Date/Time: By appointment
  • Sign up for a 15-30 minute appointments arranged by e-mail on Google Meet here

Course Description

You're going to learn about data structures and algorithms as they are applied in real world computer systems. This course and the examples are applied in computer systems examples so you can understand how foundational data structures are used in nearly every modern machine.

Registrar Description: Presents an integrated approach to the study of data structures, algorithms, and their application within systems topics. Introduces a variety of fundamental algorithmic techniques (divide-and-conquer, dynamic programming, graph algorithms) and systems topics (models of computation, computer architecture, compilation, system software, networking). Demonstrates the integration of topics through programming assignments in the C language that implement fundamental data structures (lists, queues, trees, maps, graphs) and algorithms as they are applied in computer systems. Additional breadth topics include programming applications that expose students to primitives of different subsystems using threads and sockets.

Course Objectives

By the end of this course, you will:

• Navigate the operating system using a terminal
• Compile, debug, and execute source code
• Describe each step of the compilation process for
• Write, compile, link, and run C programs
• Implement common data structures in C
• Write programs with dynamically allocated memory in the C language
• Describe how hardware is used on software to improve performance
• Describe the difference between a process and a thread
• Write a TCP networking application
• Analyze and state the complexity of common sorting and search algorithms.
• Describe specific algorithmic strategies and how each can be used to solve algorithmic problems.
• Implement and use common graph representation structures and trees.
• Implement common graph traversal and search algorithms.
• Solve recurrences by substitution method and using the master theorem in order to prove an algorithm’s asymptotic complexity.
• Design trade-offs given a set of algorithms and data structures regarding their time and space complexity.

Resources

There will be no required textbook for this course. However, these resources are recommended.

• (Strongly Recommended - for learning Algorithms) Bhargava, Aditya (2016). Grokking Algorithms (1st Edition). Manning Publications.
• (Free - for learning C) Essential C
• (Recommended for C help) C Programming Language
• (Free - for Computer Systems Concept help) Ostep book
• (Recommended for Computer Systems Concept help) Computer Systems: A Programmers Perspective
• (Free interactive demoes for learning Algorithms) Visualizing Algorithms
• (Free interactive demoes for learning Algorithms) Data Structure Visualizations

Prerequisites

• MSCS Computer Science - Align

Assessment/Course Polices

Please find below the grading distribution that will be used for this course. You will find the grade you earn in this course on Canvas.

• In-Class Activity: (5%)
• In-Class Labs:     (20%)(Each equally weighted)
  • Labs are due before class of the following week.
• Exam(s):           (20%)
• Assignments:       (55%)(Each equally weighted)

• The grade system follows the University Grading System.
• A  = 95 – 100
• A- = 90 – 94.99
• B+ = 87 – 89.99
• B  = 83 – 86.99
• B- = 80 – 82.99
• C+ = 77 – 79.99
• C  = 73 – 76.99
• C- = 70 – 72.99
• D+ = 67 – 69.99
• D  = 63 – 66.99
• D- = 60 – 62.99
• F  =  0 – 59.99

• In the event of a sick/snow day (i.e. we miss a lab or in-class activity) the weight of each assignment increases (There may also be shuffling of course material if we are interrupted).
• The expectation is that the assignments are fair but difficult, so you should start early!
• Late Submissions of Assignments receive 10% off per day submitted late (up to 3 days max, then 0% received)
  • Unfortunately, with large classes I cannot make individual exceptions (i.e. "special deals") fairly to your classmates who are likely making other personal sacrifices to complete work on time.
  • Come speak to me about your wellness if something is otherwise impeding your progress so we can provide you available resources to succeed.
• Assignments that do not compile/open receive no credit Simply put, programs that do not compile do not do anything.
• There are no "re-grades" or points awarded one week after your grade is posted. "re-grades" may result in a higher, equal, or lower score.
  • There are no "re-grades" after the semester is over.
  • Do not ask multiple members of the course staff for "re-grades"
• If you are currently waitlisted, you must submit your homework on time. That is the gamble! If you do not have blackboard access, you will submit by e-mail or other course mechanism (e.g. github).
• There are no extra credit assignments just for you. I reserve the right to add points to assignments that do go above and beyond however. I reserve the right to give an extra credit assignment to the entire class, though this is highly unlikely.
• I reserve the right to modify the grading scale in your favor if you show exemplary proficiency in any of the catagories. I will never modify the scale to lower a students grade.
• In class work cannot be made up at a later date unless otherwise arranged with the instructor well in advance.
  • Course work completed after the date cannot be graded, as solutions will have been discussed (this includes if taking this course for an Incomplete).
  • Once again, "in-class" work must be completed in-class unless there is a documented emergency or you have prearranged with the instructor a make-up well in advance.
• Lab time is meant for helping students with the lab, not completing homework. I have to prioritize lab first, then can answer homework questions.
• No Facebook, no cell phones. Not only does it distract you, it distracts others!
• Everyone needs to come see me in office hours (or by appointment) at least one time during the semester to introduce yourself. The purpose is so that you:
• Know where my office is.
• Get used to coming to office hours.
• Let me know how I can help you achieve your goals.