Big Data Programming and Architecture Capstone - Winter 2025
Chronologie
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janvier 22, 2025Début de expérience
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avril 10, 2025Fin de expérience
Catégories
Analyse des données Stratégie de vente Marketing strategyCompétences
business and analytical problem framing model development deployment and documentation business analytics storytelling and data visualization data analysis, data science concepts, text analyticsThis course covers advanced-level topics in the areas of data science, machine learning, and technical/software applications. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. They will practice their knowledge of using various technologies as part of the course, such as real-time analytics tools (e.g., Kafka and HBase), NoSQL databases, and cloud technologies.
Students will apply analytical models, methodologies, and tools learned in the program to create an analytics solution for your organization, with support from faculty mentors, who will work with students.
The final project deliverables will include:
- A report on students’ findings and details of the analytics solution.
- A final presentation of the solution and recommendations to your organization.
- Future collaboration ideas will be identified based on current project outcomes.
Chronologie du projet
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janvier 22, 2025Début de expérience
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avril 10, 2025Fin de expérience
Exemples de projets
The capstone project provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into an analytics problem. The project also includes data collection & preparation, data modeling, and analysis with the potential to incorporate predictive modeling, machine learning implementation, a solution deployment plan and the results of the deployment. The Capstone project results/ recommendations will be communicated in a report document and a final presentation.
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 capstone 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)
- Reduction of client churn (lower abandonment)
- 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
Data need not be ‘clean’; it is advantageous to the students’ learning experience to require cleansing and profiling of the data prior to analysis. This supports the learning experience and minimizes partner data preparation.
Les entreprises doivent répondre aux questions suivantes pour soumettre une demande de jumelage pour cette expérience:
Provide a dedicated contact who is available to answer periodic emails or phone calls over the duration of the project to address students' questions.
Be available for a quick phone call with the instructor to initiate your relationship and confirm your scope is an appropriate fit for the course.
Chronologie
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janvier 22, 2025Début de expérience
-
avril 10, 2025Fin de expérience