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Business Intelligence & Data Analytics - F24

DAT 103
Closed
McMaster University Continuing Education
McMaster University Continuing Education
Hamilton, Ontario, Canada
Guido DiCesare
Instructor
(13)
6
Timeline
  • September 24, 2024
    Experience start
  • November 26, 2024
    Experience end
Experience
2 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries
Categories
Data analysis Market research Sales strategy
Skills
adult education data modeling project planning extract transform load (etl) computer science business intelligence data analysis systems development life cycle
Learner goals and capabilities

This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. The course explains how to apply data analytics skills to the area of business intelligence (BI). Focus is placed on the components of the business intelligence project lifecycle such as project planning, BI tool selection, data modeling, ETL design, BI application design and deployment and reporting. The project(s) will allow the students to apply BI practices and analysis.

Learners
Continuing Education
Beginner, Intermediate, Advanced levels
20 learners
Project
40 hours per learner
Learners self-assign
Teams of 3
Expected outcomes and deliverables

The final project deliverables will include:

  • A report on students’ findings and details of the problem presented
  • Future collaboration ideas will be identified based on current project outcomes
Project timeline
  • September 24, 2024
    Experience start
  • November 26, 2024
    Experience end
Project Examples

The project provides an opportunity for businesses and students to identify and translate a real business problem into an analytics problem(s). The projects will allow the students to apply the acquired data analytics skills to the area of business intelligence (BI). The projects can be short and based on the information provided the students will apply their learnings to address the sponsors business problem. Some examples are:

  • Perform data visualization
  • Perform various types of analysis: Descriptive, Predictive and Prescriptive, which will be performed based on the provided business problem
  • Implement key processes including: data hygiene, ETL, modeling and reporting
  • Explain the forecasting modules used in developing the solution(s)

You should submit a high-level proposal/business problem statement including relevant data sets and definitions, a list of acceptable tools (if applicable), and expected deliverables. Business datasets could be provided based on a non-disclosure agreement or in an anonymized/synthetic data format that is relevant to your organization and business problem. The course instructors will review the documents to confirm the scope and timing of the proposed problem and its alignment with the capstone course requirements.

Analytics solution may be applicable for (however they are not limited to) the following topics:

  1. Customer acquisition and retention
  2. Quantifying Customer Lifetime Value
  3. Cross-sell and upsell opportunities
  4. Develop high propensity target markets
  5. Customer segmentation (behavioral or transactional)
  6. New Product/Product line development
  7. Market Basket Analysis to understand which items are often purchased together
  8. Ranking markets by potential revenue
  9. Consumer personification

To ensure students’ learning objectives are achieved, we recommend that the datasets are at least 20,000+ rows in size. Data need to be ‘clean’. If more than one database is provided, which must be conjoined, students will be required to integrate them. This supports the learning experience and minimizes partner data preparation.

Companies must answer the following questions to submit a match request to this experience:

There will be several student groups participating in the Riipen Assignment. 2 - 3 web conferences may be scheduled in advance with the lead of the participating organization. The Instructor may ask that you participate in an Instructor-led webinar session for students at the beginning of the project by providing an overview of your organization, project and desired/expected outcomes.

What's your dataset size? Please note that ideally the datasets should be at least 20,000+ rows in size.

Share feedback and recommendations about the project deliverables with the students and instructor.

Provide a dedicated contact who will be available to answer periodic emails or phone calls over the duration of the project to address student’s questions or provide additional information. Minimum of 2-4 interactions with each student group leader (approximately 4-6 hours over the duration of the project). Let the students/instructor know if you will be away for an extended time (e.g., vacation).

Provide an online video or link to your website to introduce the students to your organization prior to starting the project.

Be available for a quick phone call with the organizer to initiate your relationship and confirm your scope is an appropriate fit for the experience. Advise the instructor if students will be required to sign an NDA prior to beginning the project.