Statistical Analysis for Data Science - F24

DAT 200
Closed
McMaster University Continuing Education
Hamilton, Ontario, Canada
Instructor
(13)
6
Timeline
  • September 24, 2024
    Experience start
  • November 26, 2024
    Experience end
Experience
3 projects wanted
Dates set by experience
Preferred companies
Anywhere
Any company type
Any industries
Categories
Data visualization Data modelling Sales strategy
Skills
adult education statistical analysis data science computer simulation computer science calculus algebra data analysis statistics probability
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.

This course provides a foundation of exploring data through computing and statistical

analysis. Focus is placed on the structure and applications of probability, statistics,

computer simulation, and data analysis for students exploring the field of data science.

This course builds upon introductory statistics courses and is designed for students with

experience/study in programming, calculus, and algebra. Programming in R will be used

throughout the course.

Learners
Continuing Education
Beginner, Intermediate levels
20 learners
Project
40 hours per learner
Learners self-assign
Teams of 3
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
  • September 24, 2024
    Experience start
  • November 26, 2024
    Experience end
Project Examples

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:

  • Exploit visualization techniques to determine/verify a correlation between multiple quantities
  • Use linear algebra tools to calculate descriptive statistical quantities for multivariate statistical systems
  • Apply statistical sampling techniques to identify probability distributions 
  • Employ the linear regression method to characterize the relationship between dependent variable and independent variables
  • Apply classification and clustering strategies for data structure analysis
  • Use a statistical software package to perform data analysis for qualitative and quantitative problem-solving

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.