DMK 105 - Data Management: Digital Metrics and Measurement C21
Required Course Materials:
Optional Course Materials:
The expansion of e-commerce, web analytics and business analysis drives the need to stay current and relevant specific to theories and principles of digital data management practices. Examine data management technologies, tools, processes and analysis for decision making. Explore theories and examples of predictive analytics and Big Data and the impact on business, business intelligence systems and strategies for employing data to digital marketing.
Upon completion of this course, students will:
- Classify various analytical tools based on their relevance to digital marketing.
- Integrate data management frameworks into digital marketing campaigns.
- Summarize ethical and legal frameworks that apply to digital data management.
- Perform basic analysis of data sets to inform marketing decisions.
- Determine the success of a marketing campaign through key performance indicators.
The final grade is calculated based on the following components:
Case Studies: 50%
Digital Marketing Analytics Plan: 30%
This course is designed to present the fundamental concepts and theories in data management, digital metrics and measurement and promote the application to the workplace and professional practice. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, group discussions, projects, etc.).
Course assignments are submitted to the appropriate A2L Assignment folder by the specified due date
Late assignments will be subject to a 2% per day late penalty (includes weekends and holidays) for up to seven (7) days. After this date, no assignments will be accepted and a grade of zero (0) will be applied. Extensions for course work must be approved by the instructor before the due date (see Academic Regulations below), and will be granted for illness or emergencies only. Students may be asked to submit supporting documentation for an extension request.
Policy & Procedures:
Academic Regulations (Attendance, Coursework, Tests/Exams):
In accordance to McMaster University’s General Academic Regulations, “it is imperative that students make every effort to meet the originally scheduled course requirements and it is a student’s responsibility to write examinations as scheduled.” Therefore, all students are expected to attend and complete the specific course requirements (i.e. attendance, assignments, and tests/exams) listed in the course outline on or by the date specified. Students who need to arrange for coursework accommodation, as a result of medical, personal or family reasons, must contact the course instructor within 48 hours of the originally scheduled due date. It is the student’s responsibility to contact the Program Manager to discuss accommodations and procedures related to deferred tests and/or examinations within 48 hours of the originally scheduled test/exam, as per policy. Failure to contact the course instructor, in the case of missed coursework, or the Program Manager, in the case of a missed test/examination, within the specified 48-hour window will result in a grade of zero (0) on the coursework/exam and no further consideration will be granted.
*Note: Supporting documentation will be required but will not ensure approval of accommodation(s).
You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity. Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university.
It is your responsibility to understand what constitutes academic dishonesty. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at http://www.mcmaster.ca/academicintegrity/
The following illustrates only three forms of academic dishonesty:
- Plagiarism, e.g. the submission of work that is not one’s own or for which other credit has been obtained.
- Improper collaboration in-group work.
- Copying or using unauthorized aids in tests and examinations.
Students with disabilities who require academic accommodations must contact the Student Accessibility Centre (SAS) to meet with an appropriate Disability Services Coordinator. To contact SAS, phone 905-525-9140 ext. 28652, or email firstname.lastname@example.org. For further information, consult McMaster University’s Policy for Academic Accommodation for Students with Disabilities.
In this course, we will be using on-line elements, which may include email, Avenue to Learn, WebEX, and external web sites. Students should be aware that, when they access the electronic components of this course, private information such as first and last names, user names for the McMaster e-mail accounts, and program affiliation may become apparent to all other students in the same course. The available information is dependent on the technology used. Continuation in this course will be deemed consent to this disclosure. If you have any questions or concerns about such disclosure please discuss this with the course instructor.
The instructor reserves the right to modify elements of the course and will notify students accordingly.
Course Withdrawal Policy:
Policies related to dropping a course and course withdrawals are posted to the Centre for Continuing Education’s program webpage, FAQs & Policies (https://www.mcmastercce.ca/cce-policies#Dropping).
| Topic & Readings
|| Assignments/Graded Components
| Module 1: Data in Business
| Module 2: Data Measurement Principles
| Module 3: Part 1: Data and Digital Marketing
|| Assignment 1: Case Study
| Module 4: Part 2: Data and Digital Marketing
| Module 5: Market Research
|| Assignment 2: Case Study
| Module 6: Big Data
| Module 7: Data Bias, Vanity and Corruption
|| Assignment 3: Case Study
| Module 8: Data Visualization
| Module 9: Working With Deeper Data
|| Assignment 4: Case Study
| Module 10: Marketing Funnel Data
| Module 11: Marketing Automation and
| Module 12: Law and Ethics Surrounding Data
|| Assignment 5: Digital Marketing Analytics Plan