|Instructor(s)||Faezeh Ensan [Coordinator]|
Phone: (416) 979-5000 x 4904
Office Hours: Fridays, 1 pm - 3 pm
|Calendar Description||Data engineering is core to the effective development of scalable software applications. Rich data management schemes are needed to handle the sizeable Big Data that is available for processing. This course will cover related topics such as entity-relation diagrams, relational databases, data definition and manipulation languages, structured data representations formats, development of novel vocabularies and semi-structured data and also novel concepts in NoSQL databases.|
|Learning Objectives (Indicators)|
At the end of this course, the successful student will be able to:
NOTE:Numbers in parentheses refer to the graduate attributes required by the Canadian Engineering Accreditation Board (CEAB).
3.0 hours of lecture per week for 13 weeks
|Teaching Assistants||Luzalen Marcos: email@example.com|
Hawre Hosseini: firstname.lastname@example.org
Note: In order for a student to pass a course with "Theory and Laboratory" components, in addition to earning a minimum overall course mark of 50%, the student must pass the Laboratory and Theory portions separately by achieving a minimum of 50% in the combined Laboratory components and 50% in the combined Theory components. Please refer to the "Course Evaluation" section for details on the Theory and Laboratory components.
|Examinations||Midterm exam in Week 7, two hours, closed book (covers Weeks 1-6).|
Final exam, during exam period, two hours, closed-book (covers Weeks 1-13).
|Other Evaluation Information||The written reports will be assessed not only on their academic merit, but also on the communication skills of the author as exhibited through the reports. In order to achieve a passing grade in this course, the student must achieve an average of at least 50% in both theoretical and laboratory components.|
Lab assignments should be submitted before 11:59 pm the day before the scheduled next lab. The penalty for up to 8 hours delay in submission is 20% of the lab mark. More than 8-hours late lab assignments will not be accepted and will receive a mark of 0.
Introduction to relational Database Systems
Entity-Relationship (E/R) Data Model
Relational Database Model Subclass Structures to Relations
Algebra of Relational Operations
Structured Query Language (SQL)
Database Connectivity- Database Modifications Views OOP Access to RDBMS
Data Access and Integrity Models-Database Indices
Semi-structured Data Representation (XML XML Schema DTD)
Project Scope Definition and Project Specification Document Development
E/R Diagram Design
When possible, students are required to inform their instructors of any situation which arises during the semester which may have an adverse effect upon their academic performance, and must request any consideration and accommodation according to the relevant policies as far in advance as possible. Failure to do so may jeopardize any academic appeals.
Ryerson's Policy 60 (the Academic Integrity policy) applies to all students at the University. Forms of academic misconduct include plagiarism, cheating, supplying false information to the University, and other acts. The most common form of academic misconduct is plagiarism - a serious academic offence, with potentially severe penalties and other consequences. It is expected, therefore, that all examinations and work submitted for evaluation and course credit will be the product of each student's individual effort (or an authorized group of students). Submitting the same work for credit to more than one course, without instructor approval, can also be considered a form of plagiarism.
Suspicions of academic misconduct may be referred to the Academic Integrity Office (AIO). Students who are found to have committed academic misconduct will have a Disciplinary Notation (DN) placed on their academic record (not on their transcript) and will normally be assigned one or more of the following penalties:
The unauthorized use of intellectual property of others, including your professor, for distribution, sale, or profit is expressly prohibited, in accordance with Policy 60 (Sections 2.8 and 2.10). Intellectual property includes, but is not limited to:
For more detailed information on these issues, please refer to the Academic Integrity policy(https://www.ryerson.ca/senate/policies/pol60.pdf) and to the Academic Integrity Office website (https://www.ryerson.ca/academicintegrity/).