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Big Data Capstone Project - October 2019

CSDA 150
Closed
York University - School of Continuing Studies
York University - School of Continuing Studies
James M.
Lead, DSA
(4)
5
Timeline
  • October 24, 2019
    Experience start
  • December 19, 2019
    Experience end
Experience
1/1 project matches
Dates set by experience
Preferred companies
Anywhere
Any
Any industries
Categories
Data analysis Market research Sales strategy
Skills
business analytics storytelling and data visualization data analysis business and analytical problem framing model development deployment and documentation
Learner goals and capabilities

In the final course of the Advanced Data Science and Predictive Analytics Certificate, students spend 8 weeks creating an analytics solution/model for your organization.

This capstone project includes analysis of a real-life scenario, including business problem framing, translating to an analytical problem statement, data collection, preparation, integrating, modelling and analyzing and will result in a final report/ presentation that outlines recommendations and a solution deployment plan.

Learners
Any level
14 learners
Project
40 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

*We recommend that the datasets are at least 100k+ rows in size. Furthermore, the datasets do not have to be ‘clean’ or complete (in fact, we would encourage datasets to be as realistic as possible in order to allow students to conduct the appropriate data preparation steps).

The datasets do not need to be combined or joined beforehand. For example, you may share several separate datasets (for example as .CSVs) that contain customer demographic data, transactional data, and product data. Students will be responsible for determining how to integrate these datasets, both to support their learning objectives and to reduce the data preparation work from your end.

This project can encompass a wide range of topics that require data-driven decision making.

Deliverables may be shared in the form of:

  1. Project Proposal
  2. Sprint 1: Data Exploration, Data Preparation and Modelling
  3. Final Project Report
  4. Presentation
Project timeline
  • October 24, 2019
    Experience start
  • December 19, 2019
    Experience end
Project Examples

Your organization will need to provide relevant datasets*, background information, and a high-level business question, opportunity, or challenge. Although it is the responsibility of the students to develop an appropriate analytical solution to the business problem you provide, it would be helpful if you select a business question, opportunity, or challenge is amendable to a data-driven solution (to the best of your knowledge)

Project Examples

Students can create data analytics solutions and models to assist with:

  1. Forecasting (sales, demand, market conditions)
  2. Developing a dashboard or reporting solution to provide actionable insights
  3. Improving customer retention
  4. Quantifying Customer Lifetime Value
  5. Predicting various events of interest (fraud, misdiagnosis)
  6. Getting customers to purchase more premium (up-sell) products
  7. Getting customers to purchase across multiple categories (cross-sell)
  8. Finding the best customers for a Direct Marketing initiative
  9. Customer segmentation (behavioural or transactional)
  10. Social Network Analysis (understand influencers, customer relations)
  11. Understanding customer sentiment and what they are talking about (topic modeling)
  12. Recommender systems for various items (movies, products, etc.)
  13. Market Basket Analysis to understand which items are often purchased together
  14. Predicting or forecasting a numeric value of interest (home prices, population)
  15. Visualize buyers and buyers habits over time
Companies must answer the following questions to submit a match request to this experience:

Commit to providing a dedicated contact to meet with students at the indicated milestone check in dates

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.