Master's Big Data Analysis
General
- 48 learners; teams of 8
- 20 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 8 projects wanted
- Anywhere
- Academic experience
- Any
- Any company with big data
Categories
Skills
Project timeline
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January 31, 2018Experience start
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February 12, 2018Project Scope Meeting
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March 23, 2018Midway Check In
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March 31, 2018Experience end
Timeline
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January 31, 2018Experience start
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February 12, 2018Project Scope Meeting
Meeting between students and organization to confirm: project scope, communication styles, and important dates.
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March 23, 2018Midway Check In
Meeting between students and organization to ensure that progress is on track halfway through completion.
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March 31, 2018Experience end
Overview
- Details
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Master's-level student-consultants will use data analytics to uncover insights into your organization's challenges or opportunities.
- Learner skills
- Project planning, Algorithms, Project scoping, Project implementation, Data analysis, Big data
- Deliverables
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Phase 1 Project Plan: Students will meet with organization representative(s) to devise the project scope and prepare a detailed plan for completion of the project.
Phase 2 Project Execution: Students will work on deliverables outlined in the project plan. Teams will periodically communicate with organization representative(s) as needed to complete project tasks.
Phase 3 - Outcome - Presentation: Students will submit a 50-slide deck along with excel spreadsheets of algorithms
Project Examples
Starting this January, groups of 6-8 master's-level student-consultants will spend time analyzing applicable data sets to help you make informed decisions for the success of your business.
Based on the information your organization provides and the goals that you've shared with the students, they will provide you with an analysis and recommendations that will help you gain insights into new opportunities or address a specific challenge your organization is facing.
Project examples include, but are not limited to:
- Predictive analytics in ecommerce & retail.
- Predictive analytics in investments & trading.
- Pricing for new technology products & services.
- Assessing credit scores, ratings, and risk.
- Financial fraud likelihood.
- Customer segmentation & targeting.
- Predicting customer churn.
- Sentiment analysis of social media content.
You can find out more about the MBAN program here: http://schulich.yorku.ca/programs/mban/
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.
Provide a dedicated contact who will be available to answer periodic emails or phone calls over the duration of the project to address students questions or provide additional information.
Provide your dataset before the students start the project.
Be available for 3 meetings with your student group between Feb. 1 and March 30 (approximately 1 hour per meeting).