Machine Learning for Big Data Analytics - Winter 2024
General
- Certificate
- 20 learners; teams of 4
- 40 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 3/3 project matches
- Anywhere
- Academic experience
- Any company type
- Any industries
Categories
Skills
Project timeline
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January 20, 2024Experience start
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January 28, 2024Project Scope Meeting
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March 3, 2024Midway Check-in
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April 7, 2024Final Presentation
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April 14, 2024Experience end
Timeline
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January 20, 2024Experience start
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January 28, 2024Project Scope Meeting
Conference call/ Meeting between students/instructors
and organization to confirm: project scope, communication styles, and important dates.
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March 3, 2024Midway Check-in
Conference call/ Meeting between students and the organization to ensure that progress is on track halfway through completion.
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April 7, 2024Final Presentation
Be available to attend the final presentation day (in-person or remote), and provide feedback to students on their completed project.
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April 14, 2024Experience end
Overview
- Details
-
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 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.
- Learner skills
- Modern machine learning techniques, Neural network, Deep learning, Reinforcement learning, Nlp, Text analysis, Big data analytics
- 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 Examples
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.
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
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?