Smart Infrastructure Solutions: UrbanTech Case Study
Case Study Overview
UrbanTech Solutions, based in Melbourne, specializes in smart infrastructure projects aimed at enhancing urban mobility, energy efficiency, and sustainability. Over the past decade, the company has collaborated with local governments and private organizations to implement projects like smart traffic systems, energy-efficient lighting, and intelligent waste management solutions.
Recently, Emma Carter, the COO of UrbanTech, has expressed interest in understanding how data analytics can optimize project planning, reduce costs, and improve stakeholder satisfaction. A dataset containing past project data—including costs, projected vs. actual timelines, efficiency metrics, funding sources, project scales, and stakeholder ratings—has been provided for analysis.
Assignment Overview
In this assignment, students will apply data analytics techniques to assess project performance, explore key trends, and derive actionable insights for UrbanTech. The assignment consists of four key components:
Part 1: Data Exploration and Preparation
- Clean the dataset by:
- Handling missing values
- Checking for inconsistencies in categorical data
- Ensuring numerical fields are formatted correctly
- Perform. an exploratory analysis by calculating summary statistics (e.g., mean, median, distribution) and identifying patterns across project types, funding sources, and performance metrics.
- Identify key attributes such as project type, location, funding source, project scale, complexity level, cost, timeline, efficiency indicators, and stakeholder ratings.
- Create at least three visualizations that highlight trends in:
- Project cost variations
- Efficiency metrics
- Stakeholder satisfaction
Part 2: Project Performance Analysis
- Compare projects based on the following performance indicators:
- Cost-effectiveness: Calculate and compare cost per unit impact across different project types.
- Timeline adherence: Compute deviations between actual and projected timelines.
- Stakeholder satisfaction: Analyze satisfaction scores in relation to project attributes.
- Environmental impact: Assess trends in environmental impact scores.
- Analyze whether funding sources and project complexity levels influence:
- Cost-effectiveness
- Timeline adherence
- Stakeholder satisfaction
- Perform calculations to identify trends in:
- Project cost variations
- Efficiency metrics
- Technology adoption rates
Part 3: Hypothesis Testing and Insights
Use statistical methods (e.g., correlation analysis, regression, t-tests) to test the following hypotheses:
1. Projects that stay within projected timelines have higher stakeholder satisfaction.
2. Smart traffic management projects yield better cost-effectiveness than other project types.
3. Larger-scale projects experience higher deviations from projected costs.
4. Government-funded projects show better adherence to projected costs and timelines.
5. Higher technology adoption scores correlate with improved environmental impact.
Additionally, based on initial analysis, define and test one additional hypothesis. Ensure the hypothesis is measurable using the dataset’s attributes and explain the rationale behind selecting it.
Part 4: Recommendations and Strategic Insights
- Summarize key findings and provide actionable recommendations, categorized into:
- Cost optimization: Suggestions for reducing cost overruns.
- Project efficiency: Methods for improving project timelines.
- Sustainability & technology adoption: Strategies to enhance environmental impact and stakeholder satisfaction.
- Discuss the implications of the findings and how UrbanTech can leverage data-driven insights to enhance project planning, efficiency, and sustainability.
Submission Requirements
- Submit a structured report (5–6 A4 pages, 1500–2000 words, excluding figures, tables, references, and appendices).
- Provide an Excel file containing the cleaned dataset, calculations, and visualizations. If using Python or R, include a CSV of the final dataset along with the code used for analysis.