Data Analytics and Modelling - Spring 2024
General
- Continuing Education
- 24 learners; teams of 4
- 40 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 5/5 project matches
- Anywhere
- Academic experience
- Any company type
- Any
Categories
Project timeline
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May 13, 2024Experience start
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August 17, 2024Experience end
Overview
- Details
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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.
This course offers an introduction to data science and machine learning paving the way for students to learn data analytics principles. In particular, this course begins with a brief history of data analytics and data science, followed by regression analysis, regression and classification trees, and ends with introductions to K-means clustering, principal component analysis (PCA). Each lecture has associated with it a practical lab session in which students will put "theory into practice" offering students a hands-on approach to learning the material.
- Learner skills
- Business analytics, Storytelling and data visualization, Data analysis, data science concepts, text analytics, Business and analytical problem framing, Model development deployment and documentation
- Deliverables
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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 Examples
The project provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into an analytics problem. The projects can be short, where the students can apply their learnings to address the sponsors business problem. Some examples are:
- Apply linear algebra and matrix computations
- Apply algorithms to solve systems of equations
- Develop optimizations algorithms
- Attribute linear regressions to data
- Attribute nonlinear regression to data
- Implement tree-based methods to datasets
- Visualize data and modelling results
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:
- Demand for social services (healthcare, emergency services, infrastructure, etc.)
- Customer acquisition and retention
- Merchandising for trade areas (categories)
- Quantifying Customer Lifetime Value
- Determining media consumption (mass vs digital)
- Cross-sell and upsell opportunities
- Develop high propensity target markets
- Customer segmentation (behavioral or transactional)
- New Product/Product line development
- Market Basket Analysis to understand which items are often purchased together
- Ranking markets by potential revenue
- 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.
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
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.
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).
Share feedback and recommendations about the project deliverables with the students and instructor.
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.
Provide an online video or link to your website to introduce the students to your organization prior to starting the project.
What's your dataset size? Please note that ideally the datasets should be at least 20,000+ rows in size.