General
- Continuing Education
- 24 learners; teams of 4
- 40 hours per learner
- Dates set by experience
- Learners self-assign
Preferred companies
- 2/2 project matches
- Anywhere
- Academic experience
- Any company type
- Any
Categories
Project timeline
-
May 13, 2024Experience start
-
July 20, 2024Experience end
Overview
- Details
-
The 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 introduces descriptive statistics, basic inferential statistics, linear regression, and probability concepts and calculations. Practical application activities in the course focus on how statistical methods are used in the analysis of data. Common statistical and programming tools will be introduced and employed in order to demonstrate how significant and insightful information is collected, used, and applied to problem-solving processes.
- Learner skills
- Business analytics, Storytelling and data visualization, Data analysis, data science concepts, text analytics, Business and analytical problem framing, Model development deployment and documentation
- 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 Examples
The project provides an opportunity for businesses and students to identify and translate a real business problem into an analytics problem(s). 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:
- Interpret and produce various graphical displays of data and information and learn how to choose the most appropriate technique in a variety of situations
- Interpret and compute confidence intervals and data statistics (mean, median, histograms and significant differences).
- Solve problems with statistical variables that have a binomial, Poisson, normal or other probability distributions
- Use multiple regression to predict a response variable and determine the most significant predictor variables
- Use R and Python to process and analyze data
- Apply all these concepts on a business problem presented by the sponsor
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. Quantifying Customer Lifetime Value
4. Cross-sell and upsell opportunities
5. Develop high propensity target markets
6. Customer segmentation (behavioral or transactional)
7. New Product/Product line development
8. Market Basket Analysis to understand which items are often purchased together
9. Ranking markets by potential revenue
10. 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.
Additional company criteria
Companies must answer the following questions to submit a match request to this experience:
Provide an online video or link to your website to introduce the students to your organization prior to starting 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).
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.
Share feedback and recommendations about the project deliverables with the students and instructor.
What's your dataset size? Please note that ideally the datasets should be at least 20,000+ rows in size.
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.
Do you have a well defined business problem? If so, please briefly explain it and inform the expected deliverables.