Course Syllabus - Summer 2021
Data Processing at Scale (CSE 511)
Contact Information
Instructor:
Sohini Roy
Teaching Assistants:
Keerthi Reddy Ravula and Rachana Basabathini
Content Questions:
Weekly discussion forums
Slack Channel:
NOTE:
Direct Link: https://asu-2214-cse511-44819.slack.com
You must join/access this workspace using your ASURITE
credentials.
Content Issues:
Course “Feedback” tool
Technical Support:
NOTE:
Coursera Learner Help Center
Please make sure you are logged in so that support personnel
recognize you as a degree student.
General Support:
NOTE:
When sending an email about this class, please include the
prefix and number “CSE 511” in the subject line of your
message.
Please use this email address for questions that are private in
nature. If it is a question that would benefit your classmates, and
is not private in nature, please post in the weekly discussion
forums.
Course Description
Database systems are used to provide convenient access to disk-resident data through efficient
query processing, indexing structures, concurrency control, and recovery. This course delves into
new frameworks for processing and generating large-scale datasets with parallel and distributed
algorithms, covering the design, deployment and use of state-of-the-art data processing systems,
which provide scalable access to data.
CSE 511 Syllabus
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Specific topics covered include:
● Efficient query processing
● Indexing structures
● Distributed database design
● Parallel query execution
● Concurrency control in distributed parallel database systems
● Data management in cloud computing environments
● Data management in Map/Reduce-based
● NoSQL database systems
Learning Outcomes
Learners completing this course will be able to:
● Differentiate among major data models such as relational, spatial, and NoSQL
● Perform queries (e.g., SQL) and analytics tasks in state-of-the-art database systems
● Apply leading-edge techniques to design/tune distributed and parallel database systems
● Utilize existing NoSQL database systems as appropriate for specified cases
● Perform database operations (e.g., selection, projection, join, and group by) in
state-of-the-art cluster computing systems such as Hadoop/Spark
● Perform scalable data processing operations (e.g., selection, projection, join, and group by)
in cloud computing environments, including Amazon AWS
Estimated Workload/ Time Commitment Per Week
Average of 18 - 20 hours per week
Required Prior Knowledge and Skills
● Basic computer science knowledge including computer organization and architecture,
discrete mathematics, data structures, and algorithms
● Knowledge of high-level programming languages (e.g. Java) and scripting language (e.g.
Python)
● Knowledge of relational database structures
Technology Requirements
Hardware
Standard with major OS
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Software and Other
To complete course projects, some of the following software may be required: Hadoop/Spark and
PostgreSQL. You will access all technology tools through Coursera, there will be nothing additional
to download, except for PostgreSQL.
Textbook and Readings
There is no required textbook for this course. Required course readings will be provided within each
week these are assigned.
Course Content
Instruction
Video Lectures
Live Sessions (office hours, webinars, etc.)
Assessments
Knowledge Checks (auto-feedback, ungraded)
Discussion Prompts (instructor-graded)
Practice and Graded Quizzes (auto-graded)
Programming Assignments (auto-graded, instructor-graded)
Midterm Exam (proctored, auto-graded)
Group Project (instructor-reviewed)
Details of the main instructional and assessment elements this course comprises follow:
Lecture videos. In each module, the concepts you need to know will be presented through a
collection of short video lectures. You may stream these videos for playback within the browser by
clicking on their titles or download the videos. You may also download the slides that go along with
the videos.
Knowledge Checks. Designed to support your learning, knowledge checks are short quizzes to test
your knowledge of the concepts presented in the lecture videos. You may take your time, review
your notes, and learn at your own pace because knowledge checks are untimed. You may retake
knowledge checks 3 times every 8 hours. You are encouraged to read the feedback, review your
answer choices, and compare them to the correct answers. With the feedback as your guide, you
may use knowledge checks as opportunities to study for other assessments and tasks in the course.
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Practice Quizzes. Each module will include one practice quiz, intended for you to assess your
understanding of the topics. You will be allowed unlimited attempts at each practice quiz. Each
attempt may present a different selection of questions to you. There is no time limit on how long you
take to complete each attempt at the quiz. These quizzes do not contribute toward your final grade in
the class.
Graded Quizzes. Each module will include one graded quiz. You will be allowed one attempt per
quiz. To ensure academic integrity and graduate-level rigor, please be advised, unless otherwise
noted, that there is a time limit to complete graded quizzes and tests and it may be different per
assessment or course. Once you open a graded quiz or test, the timer will start and you are to
complete the assessment in a single session. Resets will not be granted. For those of you who have
taken other courses in the MCS program, this may be different from your previous learning
experiences, so we wanted to make you aware of this.
Graded Discussion Prompts. Each module will include one graded discussion prompt. You will see
the discussion prompt alongside other items in the lesson. Each prompt provides a space for you to
respond. After responding, you can see and comment on your peers' responses. All prompts and
responses are also accessible from the general discussion forum and the module discussion forum.
Programming Assignments. This course will include six (6) individual programming assignments.
The assignments are provided to students in the first week of the course, so you can review what is
expected and design your own learning schedules to complete the assignments on time. At the
beginning of specific weeks when assignments are due, the assignments will be re-introduced and
any additional materials will be provided. A submission area is provided at the end of these weeks.
Group Project. Groups will be formed in the first week of class based on a required Team
Formation Survey located in the Welcome and Start Here section. You are expected to collaborate
with your teammates to complete a final project. There will be six (6) project milestones to submit
throughout the course to help your group make consistent progress on the project until it is ready to
submit at the end of the course. At the ends of Week 4 and 8 you will be asked to complete a
Teammate review. In these reviews you will assess your teammates on their contribution to the
project.
Proctored Exam: You will have one (1) proctored exam, the midterm exam. ProctorU is an online
proctoring service that allows students to take exams online while ensuring the integrity of the exam
for the institution. Additional information and instructions are provided in the Welcome and Start Here
section of the course. You must setup your proctoring 72 hours prior to taking your exams, so
complete this early.
Proctored Exam Allowances: There will be no midterm exam allowances for this course, meaning:
● No notes, materials, resources, technologies, or communication is permitted during the
exam.
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● Students are to take the exam in a single session with one attempt and without leaving the
testing space (e.g. no bathroom or water breaks).
● Whiteboard, scratch paper, writing utensils, erasing resources: Students are strongly
encouraged to use the whiteboard option instead of scratch paper.
If using a whiteboard, students may have erasable whiteboard markers and what is needed
to erase writing on the whiteboard; please have extra whiteboard markers and eraser
resources in your testing area.
If using scratch paper, students may have an unlimited amount of blank scratch paper of any
size, writing utensils (e.g., pens, pencils, markers, and/or highlighters) and erasers; please
have extra ones in your testing area should you run out of ink, the pencil breaks, etc.
All scratch paper must be destroyed at the end of the exam and all whiteboard markings
must be erased.
Course Grade Breakdown
Course Work
Quantity
Team or
Individual
Percentage of
Grade
Discussions (participation)
7
Individual
10%
Auto-graded quizzes
7
Individual
15%
Auto-graded programming
assignment(s)
6
Individual
15%
Major Group Project - 1
(human-graded or combo
human- and auto-graded)
Milestone 1: Summary of
Group Discussion
Team
ungraded
Milestone 2: Introduction to
Course Project: Spatial
Queries
Team
ungraded
Milestone 3: Team Progress
Check-in
Team
ungraded
Milestone 4: Spark SQL
Individual and
Team
10%
Milestone 5: Hot Spot
Analysis
Individual and
Team
20%
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MIlestone 6: Individual
Contribution Report
Individual
10%
Project Total =
40%
Midterm exam
1
Individual
20%
Grade Scale
NOTE: You must earn a cumulative grade of 70% to earn a “C” in this course. Grades in this
course will not include pluses or minuses.
A
90% - 100%
B
80% - 89%
C
70% - 79%
D
60% - 69%
E
<60%
Course Schedule
Live Events - Weekly (meet with the course instructor and your classmates to learn more about
course topics and discuss assignments):
Tuesdays from 3:00 - 4:00 PM AZ time (Except for the first week - see note below)
Please Note:
● Week 1’s Live Event will take place on the first day of the course, Monday May 17, from 3:00
- 4:00 PM AZ time instead of Tuesday. This applies to Week 1 only.
● These events will be recorded and uploaded to the course.
Virtual Office Hours - Weekly (another chance to get your questions answered from the course
instructor and/or teaching assistants):
Wednesdays from 2:00 - 3:00 PM AZ time and Thursdays 3:00 - 4:00 PM AZ time (Check the Live
Events page in the course for your local time and access details.)
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Please Note:
● There will be no office hours during Week 8.
Assignment Deadlines
Important Note: Unless otherwise noted, all graded work is due on Sunday 11:59 pm Arizona
time for the week it is assigned. A late penalty of 10% for each day late will be applied for work
submitted after the scheduled due date and time.
Programming Assignments will be due on Fridays at 11:59pm as these assignments will be
reviewed during the Weekly Live Events. Late assignments will have a -10% penalty per day late.
You will be able to quickly identify these types of assignments within the Coursera platform.
Unit, Quizzes and Discussions
Open (Monday)
Close (Sunday)
Unit 1: Basic Data Processing Concepts
Week 1 Graded Quiz
5/17 12:01AM MST
5/23 11:59PM MST
Unit 2: Data Storage and Indexing
Week 2 Graded Quiz
Week 2 Graded Discussion
5/24 12:01AM MST
5/30 11:59PM MST
Unit 3:Transactions and Recovery
Week 3 Graded Quiz
Week 3 Graded Discussion
5/31 12:01AM MST
6/6 11:59PM MST
Unit 4: Principles of Distributed and Parallel
Week 4 Graded Quiz
Week 4 Graded Discussion
MidProject Teammate Review
6/7 12:01AM MST
6/13 11:59PM MST
Unit 5: NoSQL Database Systems
Week 5 Graded Quiz
Week 5 Graded Discussion
6/14 12:01AM MST
6/20 11:59PM MST
Unit 6: Big Data Tools
Week 6 Graded Quiz
Week 6 Graded Discussion
6/21 12:01AM MST
6/27 11:59PM MST
Unit 7: Data Management in the Cloud
Week 7 Graded Quiz
Week 7 Graded Discussion
6/28 12:01AM MST
7/4 11:59PM MST
Unit 8: Final Wrap-Up
End of Project Teammate Review
7/5 12:01AM MST
7/11 11:59PM MST
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Exams
Opens
Closes
Midterm Exam
6/4 12:01 AM MST, Friday
6/6 11:59 PM MST, Sunday
Programming Assignments
Due Date (Friday)
Assignment 1: Create Movie Recommendation Database
5/28 11:59 PM MST
Assignment 2: SQL Query for Movie Recommendation
6/4 11:59 PM MST
Assignment 3: Data Fragmentation
6/11 11:59 PM MST
Assignment 4: Query Processing
6/18 11:59 PM MST
Assignment 5: Parallel Sort and Parallel Join
6/18 11:59 PM MST
Assignment 6: NoSQL Assignment
6/25 11:59 PM MST
Project Milestones
Submission Type
Due Date
1: Summary of Group Discussion
Team
5/30 11:59 PM MST, Sunday
2: Spatial Queries
Individual
6/6 11:59 PM MST, Sunday
Mid-Project Teammate Review
Individual
6/13 11:59 PM MST, Sunday
3. Project Check-In
Team
6/20 11:59 PM MST, Sunday
4: Spark SQL: Programming Assignment
Individual
6/27 11:59 PM MST, Sunday
4: SPARK SQL: Source Code
Team
6/27 11:59 PM MST, Sunday
5: Hot Spot Analysis: Programming
Assignment
Individual
7/4 11:59 PM MST, Sunday
5: Hot Spot Analysis: Source Code
Team
7/4 11:59 PM MST, Sunday
5: Hot Spot Analysis: System
Documentation Report
Team
7/4 11:59 PM MST, Sunday
6: Individual Contribution Report
Individual
7/9 11:59 PM MST, Friday
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End of Project Teammate Review
Individual
7/7 11:59 PM MST, Wednesday
Optional: MCS Portfolio Inclusion Report
Individual
7/24 11:59 PM MST, Saturday
*Grades are due July 11, 2021 (Please see the ASU Academic Calendar for additional
information.)
Course Outline with Assignments
(see Course Timeline for Due Dates)
Unit 1: Basic Data Processing Concepts
Learning Objectives
1.1 Explain Data Models and Data Processing Concepts
1.2 Utilize Relational Model and Relational Algebra
1.3 Utilize SQL Query Language
1.4 Describe the major components of Database Management Systems
1.5 Define spatial data.
1.6 Explain how SQL is used to support spatial data.
Unit Structure
Unit Introduction
Lesson 1: Welcome and Start Here
Lesson 2: Big Data and Data Processing
Lesson 3: Basic Data Concepts
Lesson 4: Database Design: Entity Relationship Model to Relational Model
❏ Programming Assignment 1: Create Movie Recommendation Database
Lesson 5: Relational Model and Relational Algebra
Lesson 6: Introduction to SQL Query Language
❏ Programming Assignment 2: SQL Query for Movie Recommendation
Lesson 7: Spatial Database
Assignments
❏ Course Project Introduction: Team Formation
❏ Week 1 Discussion:
❏ Project Milestone 1: Summary of Group Discussions
❏ Week 1 Practice Quiz
❏ Week 1 Graded Quiz
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Unit 2: Data Storage and Indexing
Learning Objectives
2.1 Recognize major data storage layouts
2.2 Identify major indexing schemes in Database Systems
Unit Structure
Lesson 1: Major Storage Layouts
Lesson 2: Major Indexing Schemes in Database Systems
Assignments
❏ Weekly Graded Discussion: Database Indexes
❏ Week 2 Practice Quiz
❏ Week 2 Graded Quiz
❏ Project Milestone 2: Spatial Queries
Unit 3: Transactions and Recovery
Learning Objectives
3.1 Examine the ACID properties
3.2 Explain Transactions and Concurrency Control concepts
3.3 Describe how recovery from failures happens in database systems
Unit Structure
Lesson 1: Transactions/ACID Properties
Lesson 2: Concurrency Control Concepts
Lesson 3: Lock-based Concurrency Control and Recovery from Failures
Assignments:
❏ Weekly Graded Discussion: Concurrency vs. Queuing
❏ Week 3 Practice Quiz
❏ Week 3 Graded Quiz
Midterm Exam
❏ Midterm Exam - Proctored
Unit 4: Principles of Distributed and Parallel Database Systems
Learning Objectives
4.1 Describe data fragmentation and replication models
4.2 Describe the components of a distributed database
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4.3. Apply skills learned to complete an assignment using data partitioning
4.4 Illustrate how query processing is achieved in distributed databases.
4.5 Explain the total cost of query execution plans in distributed data processing systems.
Unit Structure
Lesson 1: Distributed Databases: Why, What?
Lesson 2: Data Fragmentation and Replication Model
❏ Programming Assignment 3: Data Fragmentation
Lesson 3: Advanced Distributed Database Systems
❏ Programming Assignment 4: Query Processing
Lesson 4: Parallel Database Systems
❏ Programming Assignment 5: Parallel Sort and Parallel Join
Assignments
❏ Weekly Graded Discussion: Data Replication
❏ Week 4 Practice Quiz
❏ Week 4 Graded Quiz
Unit 5: NoSQL Database Systems
Learning Objectives
5.1 Describe NoSQL Database Systems
5.2 Define scalability
5.3 Recognize the importance of the CAP Theorem in NoSQL databases
5.4 Differentiate between classifications of NoSQL Databases
5.5 Describe components of Key-Value Stores
5.6 Describe components of Graph Databases
5.7 Describe components of Document Databases
Unit Structure
Lesson 1: What is NoSQL?
Lesson 2: Classifications of NoSQL Databases
❏ Assignment 6: NoSQL Assignment
Assignments
❏ Weekly Graded Discussion: Emerging Trends and Challenges in Data Processing
❏ Week 5 Practice Quiz
❏ Week 5 Graded Quiz
❏ Project Milestone 3: Check-In
Unit 6: Big Data Tools
Learning Objectives
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Summer 2021
6.1 Describe how data in managed in MapReduce Systems
6.2 Outline Hadoop file structures
6.3 Explain MapReduce programming Model
6.4 Diagram MapReduce Systems
6.5 Identify how execution is handled in MapReduce
6.6 Describe how common operators are handled in MapReduce
6.7 Describe Apache Spark data system
6.8 Explain RDD data processing system
6.9 Describe several common components that are part of the Apache Hadoop Ecosystem
6.10: Explain the techniques used to optimize query processing and indexing/storage layers from
spatial data.
Unit Structure
Lesson 1: Data Management in MapReduce Systems
Lesson 2: Data Management in Apache Spark and Apache Hadoop
Assignments:
❏ Weekly Graded Discussion: What is right for a startup?
❏ Project Milestone 4:
❏ Programming Assignment: Spark SQL
❏ Systems Documentation Report
❏ Source Code
❏ Week 6 Practice Quiz
❏ Week 6 Graded Quiz
Unit 7: Data Management in the Cloud
Learning Objectives
7.1 Explain data processing in the cloud
7.2 Evaluate service models
7.3 Evaluate deployment models
Unit Structure
Lesson 1: Introduction to Cloud Computing
Lesson 2: Cloud-Based Data Management
Lesson 3: Amazon Web Services
Assignments
❏ Weekly Graded Discussion: AWS in a Large Corporation
❏ Project Milestone 5:
❏ Project Hotspot Analysis: Programming Assignment
❏ Project Hotspot Analysis: Source Code
❏ Project Hotspot Analysis: System Documentation Report
❏ Week 7 Practice Quiz
❏ Week 7 Graded Quiz
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Unit 8: Project Milestone 6 and Final Wrap-Up
Learning Objectives
8.1: Apply course content to final course projects.
Unit Structure
Lesson 1: Project Milestone 6: Individual Contribution Report
❏ Project Milestone 6: Individual Contribution Report
❏ End of Project Teammate Review
Lesson 2: Course Survey and MCS Portfolio Inclusion Report
❏ Optional: Portfolio Report for ASU MCS Degree
Slack Channel
Each course offering will have a unique Slack channel created where you can communicate with
your classmates. The format will be similar to the following, but aligned with your course details
(spring or fall, a/b/c, course prefix, course number, course title).
For example: #spring_a_2021_cse_535_mobile_computing
Slack is intended to provide a space to create community with your classmates. Please
remember to follow the communication protocol pinned in your Slack channel to ensure that
any questions or concerns you have are addressed in a timely manner. Also, please
remember ASU’s Academic Integrity policy, and please refrain from sharing assessment
questions, answers or solutions
Policies
All ASU and Coursera policies will be enforced during this course. For policy details, please
consult the MCS Graduate Handbook and the MCS Onboarding Course.
Absence Policies
There are no required or mandatory attendance events in this online course. Live Events, both
Live Sessions hosted by the faculty and Virtual Office Hours hosted by the course team do not
take attendance.
Students are to complete all graded coursework (e.g., projects and exams). If exceptions for
graded coursework deadlines need to be made for excused absences, please reach out to the
course team using the [email protected] email address. Review the exam availability
windows and schedule accordingly. The exam availability windows allow for your own flexibility
CSE 511 Syllabus
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and you are expected to plan ahead. Personal travel does not qualify as an excused absence
and does not guarantee an exception.
Review the resources for what qualifies as an excused absence and review the late
penalties in the Assignment Deadlines section of the syllabus and the course:
a. Excused absences related to religious observances/practices that are in accord with
ACD 304–04, “Accommodation for Religious Practices”
b. Excused absences related to university sanctioned events/activities that are in accord
with ACD 304–02, “Missed Classes Due to University-Sanctioned Activities”
c. Excused absences related to missed class due to military line-of-duty activities that are
in accord with ACD 304–11, “Missed Class Due to Military Line-of-Duty Activities,” and
SSM 201–18, “Accommodating Active Duty Military”
Live Events
Live Sessions - Weekly
Live Sessions are a valuable part of the learning experience because students can meet with
the course instructor and fellow classmates to learn more about course topics, special topics
within the field, and discuss coursework. The official weekly schedule for these events will be
announced once the course starts. If you are able to attend these Live Sessions, you are
strongly encouraged to do so. If you have specific questions or topics of interest to be discussed
during the live events, please indicate your request in your discussion forum post. Although it
may not be possible to address all requests live, the instructor is interested in tailoring the live
events to your questions and interests. The instructor will be following a set agenda, so please
be mindful of that when engaging in the live session.
Live Sessions hosted by the faculty will be recorded and uploaded to the course.
Live Sessions Expectations
The environment should remain professional at all times. Inappropriate content/visuals,
language, tone, feedback, etc. will not be tolerated, reported and subject to disciplinary action.
Review the Policy Regarding Expected Classroom Behavior section of the syllabus and the
Student Code of Conduct for more detailed information.
Virtual Office Hours - Weekly
Virtual Office Hours offer a chance for students to get their questions answered from the course
team. The official weekly schedule for office hours will be announced once the course starts.
Virtual office hours are recorded, but not uploaded into the course.
Virtual Office Hour Expectations
CSE 511 Syllabus
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Although the course team is responsive to trends in the discussion forums and mcsonline
emails, these sessions focus on addressing students’ specific questions related to content:
clarifications, reteaching, assessment review, etc. These sessions are specific to helping
students learn materials and understand various course assessments and are not intended to
address program or course design questions or feedback. Teaching assistants do not have the
authority to weigh in or make decisions regarding those items, so please do not include those at
this time. These sessions are specific to helping students learn materials and understand
various course assessments. Feedback of that nature is best addressed in the communication
channel: [email protected] and please include it in your course survey.
The environment should remain professional at all times. Inappropriate content/visuals,
language, tone, feedback, etc. will not be tolerated, reported and subject to disciplinary action.
Review the Policy Regarding Expected Classroom Behavior section of the syllabus and the
Student Code of Conduct for more detailed information.
Policy Regarding Expected Classroom Behavior
The aim of education is the intellectual, personal, social, and ethical development of the
individual. The educational process is ideally conducted in an environment that encourages
reasoned discourse, intellectual honesty, openness to constructive change, and respect for the
rights of all individuals. Self-discipline and a respect for the rights of others in the university
community are necessary for the fulfillment of such goals. An instructor may withdraw a student
from a course with a mark of “W” or “E” or employ other interventions when the student’s
behavior disrupts the educational process. For more information, review SSM 201–10.
If you identify something as unacceptable classroom behavior on the class platform (e.g.,
Coursera discussion forum) or communication channels (e.g., Zoom, virtual live session, virtual
office hours, Slack, etc.), please notify the course team using the [email protected] email. In
the discussion forums, you can also flag the post for our attention. For more specifics on
appropriate participation, please review our Netiquette infographic.
Our classroom community rules are to:
● Be professional
● Be positive
● Be polite
● Be proactive
Academic Integrity
Students in this class must adhere to ASU’s academic integrity policy. Students are responsible
for reviewing this policy and understanding each of the areas in which academic dishonesty can
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occur. In addition, all engineering students are expected to adhere to both the ASU Academic
Integrity Honor Code and the Fulton Schools of Engineering Honor Code. All academic integrity
violations will be reported to the Fulton Schools of Engineering Academic Integrity Office (AIO).
The AIO maintains record of all violations and has access to academic integrity violations
committed in all other ASU college/schools.
Copyright
All course content and materials, including lectures (Zoom recorded lectures included), are
copyrighted materials and students may not share outside the class, upload to online websites
not approved by the instructor, sell, or distribute course content or notes taken during the
conduct of the course (see ACD 304–06, “Commercial Note Taking Services” and ABOR Policy
5-308 F.14 for more information).
You must refrain from uploading to any course shell, discussion board, or website used by the
course instructor or other course forum, material that is not the student's original work, unless
the students first comply with all applicable copyright laws; faculty members reserve the right to
delete materials on the grounds of suspected copyright infringement.
Policy Against Threatening Behavior (SSM 104-02)
Students, faculty, staff, and other individuals do not have an unqualified right of access to
university grounds, property, or services. Interfering with the peaceful conduct of
university-related business or activities or remaining on campus grounds after a request to leave
may be considered a crime. All incidents and allegations of violent or threatening conduct by an
ASU student (whether on- or off-campus) must be reported to the ASU Police Department (ASU
PD) and the Office of the Dean of Students.
Disability Accommodations
Suitable accommodations will be made for students having disabilities. Students needing
accommodations must register with the ASU Student Accessibility and Inclusive Learning
Services. Students should communicate the need for an accommodation at the beginning of
each course so there is sufficient time for it to be properly arranged. These requests should be
submitted through Connect. See ACD 304-08 Classroom and Testing Accommodations for
Students with Disabilities. ASU Student Accessibility and Inclusion Learning Services will send
the instructor of record a notification of approved accommodations and students are copied on
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these letters. It is recommended that students reply to the faculty notification letters, introduce
themselves to their instructor, and share anything they might want to disclose.
Harassment and Sexual Discrimination
Arizona State University is committed to providing an environment free of discrimination,
harassment, or retaliation for the entire university community, including all students, faculty
members, staff employees, and guests. ASU expressly prohibits discrimination, harassment,
and retaliation by employees, students, contractors, or agents of the university based on any
protected status: race, color, religion, sex, national origin, age, disability, veteran status, sexual
orientation, gender identity, and genetic information.
Title IX is a federal law that provides that no person be excluded on the basis of sex from
participation in, be denied benefits of, or be subjected to discrimination under any education
program or activity. Both Title IX and university policy make clear that sexual violence and
harassment based on sex is prohibited. An individual who believes they have been subjected to
sexual violence or harassed on the basis of sex can seek support, including counseling and
academic support, from the university. If you or someone you know has been harassed on the
basis of sex or sexually assaulted, you can find information and resources at
https://sexualviolenceprevention.asu.edu/faqs.
Mandated sexual harassment reporter: As a mandated reporter, I am obligated to report any
information I become aware of regarding alleged acts of sexual discrimination, including sexual
violence and dating violence. ASU Counseling Services, https://eoss.asu.edu/counseling, is
available if you wish to discuss any concerns confidentially and privately.
Course Faculty
The following faculty member created this course.
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Dr. Mohamed Sarwat
Mohamed Sarwat is an Assistant Professor of Computer Science and the director of the Data
Systems (DataSys) lab at Arizona State University (ASU). He is also an affiliate member of the
Center for Assured and Scalable Data Engineering (CASCADE). Before joining ASU, Mohamed
obtained his MSc and PhD degrees in computer science from the University of Minnesota. His
research interest lies in the broad area of data management systems.
Dr. Ming Zhao
Ming Zhao is an associate professor of the ASU School of Computing, Informatics, and Decision
Systems Engineering. Before joining ASU, he was an associate professor of the School of
Computing and Information Sciences (SCIS) at Florida International University. He directs the
Research Laboratory for Virtualized Infrastructure, Systems, and Applications (VISA). His research
interests are in distributed/cloud computing, big data, high-performance computing, autonomic
computing, virtualization, storage systems and operating systems.
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