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Data Analytics and Modelling - F24

DAT 201
Fermé
McMaster University Continuing Education
McMaster University Continuing Education
Hamilton, Ontario, Canada
Guido DiCesare
Instructor
(14)
6
Chronologie
  • septembre 17, 2024
    Début de expérience
  • décembre 7, 2024
    Fin de expérience
Expérience
6/2 match de projet
Dates fixées par le expérience
Entreprises privilégiées
N'importe où
Tout type de entreprise
N'importe qu'elle industrie
Catégories
Analyse des données Étude de marché Stratégie de vente
Compétences
adult education regression analysis data science machine learning principal component analysis paving computer science k-means clustering data analysis decision tree learning
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.

This course offers an introduction to data science and machine learning paving the way for students to learn data analytics principles. In particular, this course begins with a brief history of data analytics and data science, followed by regression analysis, regression and classification trees, and ends with introductions to K-means clustering, principal component analysis (PCA). Each lecture has associated with it a practical lab session in which students will put "theory into practice" offering students a hands-on approach to learning the material.

Apprenant.es
Formation continue
Niveau Débutant, Intermédiaire, Avancé
20 apprenant.es dans le programme
Projet
40 heures par apprenant.e
Les apprenant.es s'auto-attribuent
Projets individuels
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
  • septembre 17, 2024
    Début de expérience
  • décembre 7, 2024
    Fin de expérience
Exemples de projets

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 can be short, where the students can apply their learnings to address the sponsors business problem. Some examples are:

  • Apply linear algebra and matrix computations
  • Apply algorithms to solve systems of equations
  • Develop optimizations algorithms
  • Attribute linear regressions to data
  • Attribute nonlinear regression to data
  • Implement tree-based methods to datasets
  • Visualize data and modelling results

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. 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. 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.

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 organizer to initiate your relationship and confirm your scope is an appropriate fit for the experience. Advise the instructor if students will be required to sign an NDA prior to beginning the project.

Provide a dedicated contact who will be available to answer periodic emails or phone calls over the duration of the project to address student’s questions or provide additional information. Minimum of 2-4 interactions with each student group leader (approximately 4-6 hours over the duration of the project). Let the students/instructor know if you will be away for an extended time (e.g., vacation).

Share feedback and recommendations about the project deliverables with the students and instructor.

There will be several student groups participating in the Riipen Assignment. 2 - 3 web conferences may be scheduled in advance with the lead of the participating organization. The Instructor may ask that you participate in an Instructor-led webinar session for students at the beginning of the project by providing an overview of your organization, project and desired/expected outcomes.

Provide an online video or link to your website to introduce the students to your organization prior to starting the project.

What's your dataset size? Please note that ideally the datasets should be at least 20,000+ rows in size.