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Data Analysis and Visualization - W25

DAT 104
Open Closing on January 18, 2025 / 4 spots left
McMaster University Continuing Education
McMaster University Continuing Education
Hamilton, Ontario, Canada
Guido DiCesare
Instructor
(15)
6
Timeline
  • January 23, 2025
    Experience start
  • March 21, 2025
    Experience end
Experience
4 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries

Experience scope

Categories
Data visualization Data analysis
Skills
planning adult education project planning communication computer science data visualization data analysis
Learner goals and capabilities

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.

Learners

Learners
Continuing Education
Beginner, Intermediate levels
20 learners
Project
40 hours per learner
Learners self-assign
Teams of 4
Expected outcomes and deliverables

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


Project timeline
  • January 23, 2025
    Experience start
  • March 21, 2025
    Experience end

Project Examples

Requirements

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.