Data Analytics: Insights from Tomorrow's Experts
General
- Graduate; Capstone
- 25 learners; teams of 2
- 90 hours per learner
- Dates set by experience
- Learners apply to projects
Preferred companies
- 12/11 project matches
- Canada
- Academic experience
- Any company type
- Any industries
Categories
Skills
Project timeline
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December 16, 2023Experience start
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December 16, 2023Identify a Client
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January 29, 2024Mission Statement & Stakeholder Analysis
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February 8, 2024Project Scope Statement
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February 14, 2024Written Proposal
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February 16, 2024Approval to Proceed
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April 26, 2024Experience end
Timeline
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December 16, 2023Experience start
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December 16, 2023Identify a Client
Prior to the December holiday break all students will have been paired with a suitable client.
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January 29, 2024Mission Statement & Stakeholder Analysis
With the data in hand, the focus shifts to framing the right questions that the analysis seeks to answer. This phase mandates the crafting of a concise mission statement and a rigorous stakeholder analysis. Furthermore, there's a direct interaction with instructors to ensure the data's aptness and its alignment with the chosen organization's requirements.
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February 8, 2024Project Scope Statement
Following the groundwork, students delve into the project's specifics by delineating its scope. This clarifies the project's boundaries, objectives, and ensures it aligns with the client's expectations. Here, the emphasis is on understanding the intended audience, ensuring data access, and evaluating if the data supports the chosen analysis techniques.
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February 14, 2024Written Proposal
Just prior to the February Break, students devise a detailed written proposal, outlining their analytical objectives and the envisioned benefits. Tailored for the organizational audience, this proposal acts as a project blueprint, explaining the methods, intended processes, and the data's applicability. Those collaborating with real-world clients will need their proposal vetted by both their instructor and the client.
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February 16, 2024Approval to Proceed
All students will receive approval to proceed with their proposal just prior to the February break. If approval cannot be granted, students are given the break period to rewrite their proposal.
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April 19, 2024Solutions are Built
With the green light, students embark on their analytical tasks. Using appropriate tools and techniques, they meticulously analyze the data, ensuring that the solution aligns with the objectives detailed in the proposal. This phase demands rigorous data analysis, critical thinking, and the creation of a solution that serves the organization's needs.
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April 26, 2024Presentations
The capstone's climax is a comprehensive presentation. Designated as the "Capstone Showcase" day, students present their solutions to peers, instructors, and clients. This presentation not only encapsulates their analytical journey but also demonstrates the depth of their findings. Feedback loops are essential here, with peer reviews playing a pivotal role in assessing each presentation's quality and impact.
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April 26, 2024Experience end
Overview
- Details
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Our data analytics students harness advanced tools and methodologies to dissect complex datasets, offering actionable insights tailored to your business needs. By collaborating with these future experts, companies gain a fresh perspective, innovative solutions, and a detailed analytical roadmap to drive strategic decision-making.
- Learner skills
- Sql, Python, Data prep, Stats, Ml, Coding, Ethics, Prediction, Etl
- Deliverables
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Format of Deliverables:
Deliverables may include, but are not limited to, some or all of the following:
- Written Proposal: Before embarking on the analysis, employers receive a detailed written proposal. This document outlines the scope, intended benefits, critical processes affected, resources to be used, and a comprehensive data overview. It sets the stage and expectations for the project.
- Interactive Dashboards: Students utilize tools like Power BI, Office, or Python to develop dynamic and intuitive dashboards. These offer an at-a-glance view of the key metrics, historical trends, and insights, tailored to the specific needs and objectives of the project.
- Detailed Reports: Alongside dashboards, students prepare comprehensive written reports. These delve deep into the analysis, providing context, interpretation, and actionable recommendations. Each report is structured to begin with an executive summary, followed by a detailed analysis, and concluding with proposed next steps.
- Data Models & Pipelines: For projects involving data infrastructure setup, students provide structured data models with documentation and a schematic view of proposed data pipelines. These elements ensure seamless data flow and advanced analysis.
- Presentation Slides: At the end of the project, employers receive a slide deck summarizing the project's objectives, methodologies used, key findings, and recommendations. This format is ideal for sharing insights with broader teams or stakeholders.
Expected Outcomes:
- Clear Understanding: Employers will gain a deeper understanding of their data, the insights it hides, and the opportunities it presents. This knowledge will enable more informed decision-making.
- Actionable Recommendations: Each project culminates with actionable steps that businesses can undertake, based on the insights derived from the data. These recommendations can range from operational changes to strategic shifts.
- Enhanced Decision-making: With the combination of interactive dashboards and detailed reports, businesses will be equipped to make data-driven decisions swiftly and with greater confidence.
- Skill Transfer: Employers will benefit from the latest methodologies and tools used in the realm of data analytics. Interactions with students can lead to skill transfer, enhancing the internal capabilities of the company.
- Future Roadmap: Beyond immediate insights and actions, employers will also receive suggestions for future data projects or areas worth exploring, providing a roadmap for continuous data-driven improvement.
Project Examples
Projects from prior years:
- End to end reporting of sales data for a client in a regulated industry, including automation of sales metrics reporting dashboards in PowerBI.
- Modelling of lab testing procedures for a pharmaceutical CDMO leading to pipeline developments, scheduling process improvements and predictive modelling using AI of sample testing times.
- Metric development, KPI reporting and automation of quality operations reporting for a pharmaceutical CDMO.
- Reporting & Data Maturity Model research , selection, and recommendations for a national not for profit.
- Labour market data modelling and analysis, design recommendations for a Data-warehouse to support online applications.
- Analyzing student retention data for a provincial community college
- Written report analyzing national demographic trends with projections over 25- and 50-year periods.
- Designing and building a data collection pipeline for Academic Alerts at a provincial community college
Projects Ideas:
- Historical Data Dashboarding and Metric Development: Students can visualize historical trends and data points using interactive dashboards, making complex datasets more digestible. Alongside this, they're skilled in metric development, ensuring that businesses measure what truly matters to them. Together, these tools enable businesses to track their performance effectively over time and make data-informed decisions.
- Customer Segmentation and Sales Forecasting: Leveraging data, students categorize customers into distinct segments and predict future sales trends, aiding targeted marketing and strategic planning.
- Churn Analysis and Customer Sentiment: Analyzing customer behaviours helps in predicting potential churn while gauging sentiment from platforms can determine perceptions towards a brand, aiding retention and reputation management.
- Supply Chain Optimization and Operational Efficiency Analysis: Through data, students pinpoint and rectify inefficiencies in the supply chain and broader business operations, leading to cost savings and streamlined processes.
- Maturity Model Application: Students apply tailored maturity models, like BI maturity or Data Fluency Inventory, helping businesses chart their growth and sophistication journey.
- Website Traffic Analysis and Product Recommendation Systems: Evaluating web traffic and user behaviours, students can suggest enhancements for website design and develop algorithms for tailored product recommendations.
- Fraud Detection and Business Security: Identifying unusual transaction patterns, students can propose models to detect potential fraudulent activities, strengthening business security.
- Data Modelling and Pipeline Design: Skilled in building data models for complex scenarios, students can also set up efficient data pipelines, ensuring seamless data flow and advanced analysis.
- Market Basket Analysis and Cross-Selling Strategies: Recognizing products often purchased together, businesses can be advised on promotions or placements to boost sales through effective cross-selling.