Data Analytics Capstone Project W22

CSDA1050
Fermé
Chronologie
  • mars 21, 2022
    Début de expérience
  • mai 15, 2022
    Fin de expérience
Expérience
2/5 match de projet
Dates fixées par le expérience
Entreprises privilégiées
N'importe où
Any
N'importe qu'elle industrie
Catégories
Analyse marketing Apprentissage automatique Visualisation des données Modélisation des données Stratégie de vente
Compétences
business analytics storytelling and data visualization data analysis business and analytical problem framing model development deployment and documentation
Objectifs et capacités de apprenant.es

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.

Apprenant.es
Formation continue
Tout niveau
30 apprenant.es dans le programme
Projet
40 heures par apprenant.e
Les apprenant.es s'auto-attribuent
Équipes de 5
Résultats et livrables attendus

Final deliverables will include:

  1. Project Proposal
  2. Sprint 1: Data Exploration, Data Preparation and Modeling
  3. Final Project Report
  4. Presentation
Chronologie du projet
  • mars 21, 2022
    Début de expérience
  • mai 15, 2022
    Fin de expérience
Exemples de projets

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

To ensure students’ learning objectives are achieved, 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).

Lastly, 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, but also to reduce the data preparation work from your end.

This project can encompass a wide range of topics that require data-driven decision making. If you are interested in determining if your use case would be applicable to this project please submit your project proposal to connect with the instructor.

*You can provide either current data from your organization or randomized/anonymized version of your data that is still relevant to your company and the business question at hand.

Les entreprises doivent répondre aux questions suivantes pour soumettre une demande de jumelage pour cette expérience:

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.

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.

Must be able to provide dataset which is reasonable clean and have at least 5000 records (rows)