Essentials of Cloud Computing - Winter 2024

DAT 304
Fermé
McMaster University Continuing Education
Hamilton, Ontario, Canada
Instructor
(14)
6
Chronologie
  • janvier 15, 2024
    Début de expérience
  • janvier 20, 2024
    Project Scope Meeting
  • mars 12, 2024
    Midway Check-in
  • avril 8, 2024
    Final Presentation
  • avril 9, 2024
    Fin de expérience
Expérience
3 projets souhaités
Dates fixées par le expérience
Entreprises privilégiées
N'importe où
Tout type de entreprise
N'importe qu'elle industrie
Catégories
Technologie de l'information Cloud technologies
Compétences
cloud computing adult education encryption cloud technologies authentications information privacy data visualization computer science data analysis authorization (computing)
Objectifs et capacités de apprenant.es

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.

Students will explore the principles and practices of cloud computing with this introductory course, and discover the importance of cloud computing for today’s business and IT sectors through an examination of the development of cloud technologies over time. 


Common practices for delivery, deployment, architecture and security will be presented.

Students will explore various cloud computing platforms to understand and assess 

current service options and to discuss future developments for cloud computing


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The project provides an opportunity for businesses and learners to collaborate to identify and translate a real business problem into an analytics problem. 

The projects, which can be short, will allow the student to apply the skills acquired on to address the business problem.  Some examples are:

  • Determine the characteristics of the collection system and select a collection system that handles the large data set
  • Identify the right storage solution for analytics
  • Design and implement a solution for transforming and preparing data for analysis
  • Select the right data analysis and data visualization solution for a given scenario
  • Apply the right authentication and authorization mechanisms
  • Apply data protection and encryption techniques
  • Manage and monitor data solutions

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 course requirements.



Apprenant.es
Certificat
Tout niveau
20 apprenant.es dans le programme
Projet
40 heures par apprenant.e
Les apprenant.es s'auto-attribuent
Équipes de 4
Résultats et livrables attendus
  • A report on students’ findings and details of the problem presented
  • Future collaboration ideas will be identified based on current project outcomes
Chronologie du projet
  • janvier 15, 2024
    Début de expérience
  • janvier 20, 2024
    Project Scope Meeting
  • mars 12, 2024
    Midway Check-in
  • avril 8, 2024
    Final Presentation
  • avril 9, 2024
    Fin de expérience
Exemples de projets

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. 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.