Riipen | Business Intelligence & Data Analytics

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Business Intelligence & Data Analytics

DAT 103

Fermé

Contact principal

McMaster University Continuing Education

Hamilton, Ontario, Canada

Guido DiCesare

Instructor

(22)

Chronologie

juin 11, 2023

Début de expérience
juin 17, 2023

Project Scope Meeting (TBD)
juillet 18, 2023

Midway Check-in (TBD)
août 14, 2023

Fin de expérience

Expérience

1/2 match de projet

Dates fixées par le expérience

Entreprises privilégiées

N'importe où

Any

N'importe qu'elle industrie

Portée de Expérience

Catégories

Analyse des données Étude de marché Stratégie de vente

Compétences

business analytics storytelling and data visualization data analysis, data science concepts, text analytics business and analytical problem framing model development deployment and documentation

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 course explains how to apply data analytics skills to the area of business intelligence (BI). Focus is placed on the components of the business intelligence project lifecycle such as project planning, BI tool selection, data modeling, ETL design, BI application design and deployment and reporting. The project(s) will allow the students to apply BI practices and analysis.

Apprenant.es

Formation continue

Tout niveau

24 apprenant.es dans le programme

Projet

40 heures par apprenant.e

Les apprenant.es s'auto-attribuent

Équipes de 3

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

juin 11, 2023

Début de expérience
juin 17, 2023

Project Scope Meeting (TBD)
juillet 18, 2023

Midway Check-in (TBD)
août 14, 2023

Fin de expérience

Exemples de projets

Exigances

The project provides an opportunity for businesses and students to identify and translate a real business problem into an analytics problem(s). The projects will allow the students to apply the acquired data analytics skills to the area of business intelligence (BI). The projects can be short and based on the information provided the students will apply their learnings to address the sponsors business problem. Some examples are:

Perform data visualization
Perform various types of analysis: Descriptive, Predictive and Prescriptive, which will be performed based on the provided business problem
Implement key processes including: data hygiene, ETL, modeling and reporting
Explain the forecasting modules used in developing the solution(s)

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:

Customer acquisition and retention
Quantifying Customer Lifetime Value
Cross-sell and upsell opportunities
Develop high propensity target markets
Customer segmentation (behavioral or transactional)
New Product/Product line development
Market Basket Analysis to understand which items are often purchased together
Ranking markets by potential revenue
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.

Critères supplé mentaires pour entreprise

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

Q1 - Case à cocher

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.

Q2 - Texte court

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

Q3 - Case à cocher

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

Q4 - Case à cocher

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).

Q5 - Case à cocher

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

Q6 - Case à cocher

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.