Data Analytics Capstone Project

DAB402
Fermé
St. Clair College
Windsor, Ontario, Canada
Instructor
(4)
3
Chronologie
  • février 1, 2022
    Début de expérience
  • avril 21, 2022
    Fin de expérience
Expérience
12/15 match de projet
Dates fixées par le expérience
Entreprises privilégiées
N'importe où
Any
N'importe qu'elle industrie

Portée de Expérience

Catégories
Technologie de l'information Apprentissage automatique Intelligence artificielle Analyse des données
Compétences
business analytics data analysis, data science concepts, text analytics model development deployment and documentation data analytics, machine learning, deep learning, ai python
Objectifs et capacités de apprenant.es

This capstone course is aimed at enriching student success by combining the knowledge skills and tools students have learned throughout their program. The skills are used to complete a project on a real-world challenge presented by an instructor or an industry partner. Students will use appropriate analytics techniques and tools and best practices to present results to stakeholders. Students are familiar with Excel, Python, R, SQL and variety of other tools from prior coursework.

Apprenant.es

Apprenant.es
Diplôme
Tout niveau
30 apprenant.es dans le programme
Projet
150 heures par apprenant.e
Les apprenant.es s'auto-attribuent
Équipes de 4
Résultats et livrables attendus

The project will provide:

  • A final report detailing the problem, attempted solutions, and results, including benchmarks, if applicable.
  • Ideas for next steps in the process based on project outcomes.
Chronologie du projet
  • février 1, 2022
    Début de expérience
  • avril 21, 2022
    Fin de expérience

Exemples de projets

Exigances

The course provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into a data analytics problem. The project can be an end-to-end application, including data collection & preparation, analysis, visualization, evaluation against success criteria and possible methods for deployment. Project results/ recommendations will be communicated in a final report.

You should submit a high-level proposal/business problem statement including a clear connection to data analysis, 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 instructor will review the documents to confirm the scope and timing of the proposed problem and its alignment with the course requirements.

Critères supplé mentaires pour entreprise

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

  • Q - Case à cocher
  • Q - Case à cocher
  • Q - Case à cocher
  • Q - Case à cocher