Machine Learning for Big Data Analytics - W25
Chronologie
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janvier 18, 2025Début de expérience
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avril 13, 2025Fin de expérience
Catégories
Apprentissage automatique Visualisation des données Analyse des données Modélisation des données Data scienceCompétences
modern machine learning techniques neural network deep learning reinforcement learning nlp text analysis big data analyticsThis course is part of the Big Data Programming and Analytics certificate programs.
Students in the program are adult learners with a post-secondary degree/diploma in
computer science, engineering, business, etc.
This course builds on the fundamental principles of data analytics, this course advances
to modern machine learning techniques such as neural network, deep learning, and
reinforcement learning as well as NLP and text analysis. Application activities are
structured to provide an introductory level of how machine learning techniques are
applied to big data analytics.
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
Chronologie du projet
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janvier 18, 2025Début de expérience
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avril 13, 2025Fin de expérience
Exemples de projets
The projects will provide an opportunity for businesses and learners to collaborate to
identify and address real business challenges.
The projects, which can be short, will allow the student to apply the data management
concepts and techniques presented in the classes to address the sponsors business
challenges. Some examples are:
- Identify and use various “big data analytics” tools, algorithms, and terminologies
- Apply text analytics, sentiment analysis and NLP
- Identify and apply machine learning algorithms and how to “scale” those to big
- data: trees with ensemble methods, neural networks
- Asist the sponsor in determining if the organization should invest in “big data
- analytics” technologies
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. Demand for social services (healthcare, emergency services, infrastructure, etc.)
2. Customer acquisition and retention
3. Merchandising for trade areas (categories)
4. Quantifying Customer Lifetime Value
5. Determining media consumption (mass vs digital)
6. Cross-sell and upsell opportunities
7. Develop high propensity target markets
8. Customer segmentation (behavioral or transactional)
9. New Product/Product line development
10. Market Basket Analysis to understand which items are often purchased together
11. Ranking markets by potential revenue
12. 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.
Les entreprises doivent répondre aux questions suivantes pour soumettre une demande de jumelage pour cette expérience:
We recommend that your datasets are at least 20,000+ rows in size. Do you confirm?
Is the data "clean"?
If more than one database is provided, which must be conjoined, students will be required to integrate them. Do you agree with it?
Chronologie
-
janvier 18, 2025Début de expérience
-
avril 13, 2025Fin de expérience