CAPFO - Consortium Apprentissage Expérientiel Francophone de l'Ontario
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Data Analysis and Visualization - W25

DAT 104
Ouvert Clôture le janvier 18, 2025 / 4 places restantes
McMaster University Continuing Education
McMaster University Continuing Education
Hamilton, Ontario, Canada
Guido DiCesare
Instructor
(14)
6
Chronologie
  • janvier 23, 2025
    Début de expérience
  • mars 21, 2025
    Fin de expérience
Expérience
4 projets souhaités
Dates fixées par le expérience
Entreprises privilégiées
N'importe où
Tout type de entreprise
N'importe qu'elle industrie
Catégories
Visualisation des données Analyse des données
Compétences
planning adult education project planning communication computer science data visualization data analysis
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.


The students learn how to perform exploration of data in order to discover meaningful

information to solve problems, and will allow for the application of analytics life cycle in

the context of planning to solve a business problem. Emphasis is placed on framing the

problem, proposing an analytics solution, communicating with stakeholders, and

establishing an analytics-focused project plan. Common data visualization tools and

techniques are explored and used as students learn best practices for the presentation

and communication of analytical solutions and insights.

Apprenant.es
Formation continue
Niveau Débutant, Intermédiaire
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

The final project deliverables will include:

  • 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 23, 2025
    Début de expérience
  • mars 21, 2025
    Fin de expérience
Exemples de projets

The project should provide an opportunity for the students to collaborate with the project sponsor to identify and translate a real business problem into an analytics problem. The projects can be short, where the students can apply their learnings to address the sponsors business problem. Some examples are:

  • Describe the Data Analytics Lifecycle, its stages how they complement each other and applicability to the business problem
  • Explore, cleanse and extract data to determine the technical and analytical feasibility of presenting viable solutions to address business problem(s)
  • Design viable data analytics solution(s) to a set of specific business needs.
  • Assess data management tools to perform ETL (Extract Transform and Load) activities 
  • Examine and create data visualizations that effectively communicate with audiences at various organizational levels
  • Present data management and project management principles related to the Data Analytics Lifecycle in addressing the business problem


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 capstone course requirements.


Analytics solution may be applicable for (however they are not limited to) the following topics:

  1. Customer acquisition and retention
  2. Cross-sell and upsell opportunities
  3. Develop high propensity target markets
  4. Customer segmentation (behavioral or transactional)
  5. New Product/Product line development
  6. Ranking markets by potential revenue


To ensure students’ learning objectives are achieved, we recommend that the datasets are at least 20,000+ rows in size. Data need to be ‘clean’. 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.