This repository contains a collection of solutions developed for Forage virtual internships. Each project folder documents a systematic approach to solving a specific business problem, moving from initial scoping to the delivery of actionable recommendations.
The objective of these simulations is to apply analytical frameworks to practical, real-world business challenges. This portfolio emphasizes the core methodology: defining the business problem, conducting a focused analysis, developing a scalable solution, and communicating insights effectively to stakeholders.
My approach to problem-solving follows a structured, four-phase process designed to ensure that analysis is always aligned with business objectives.
Before any technical work begins, the primary objective is to frame the business context. This involves a thorough analysis of the scenario to understand stakeholder needs and define the key questions that must be answered.
- Stakeholder Analysis: Identify the primary decision-makers (e.g., CEO, CMO, Operations Manager) and their strategic priorities.
- Objective Setting: Formulate precise, non-overlapping questions tailored to each stakeholder. The goal is to move beyond generic KPIs and establish a clear scope for the analysis that will drive strategic decisions.
With clear objectives defined, the next step is to conduct a targeted data analysis. The emphasis is on efficiency and relevance, avoiding analysis paralysis by focusing only on the data points and metrics that directly address the stakeholder questions.
- Data Exploration: Identify relevant data segments, variables, and potential proxies.
- Hypothesis-Driven Approach: Select metrics and visualizations that either prove or disprove a business hypothesis, ensuring every analytical output serves a distinct purpose in the decision-making process.
Analytical models and dashboards are constructed with sustainability and reusability in mind. The goal is to create solutions that are not only accurate but also efficient, transparent, and adaptable for future use.
- Modularity: Develop code and models in a structured manner, avoiding hard-coded values to allow for easy updates and application to new datasets.
- Documentation: Ensure the process is well-documented so that other stakeholders can understand the logic and replicate the results without extensive rework.
The final and most critical phase is translating complex analytical findings into a clear, concise narrative for a non-technical audience. An insight is only valuable if it is understood and acted upon.
- Clarity and Brevity: Synthesize results into key takeaways that directly answer the initial business questions.
- Actionable Recommendations: Present findings in the context of business impact. The communication is always business-facing, free of technical jargon, and focused on providing a clear path forward.
- Client Research And Problem Identification
- Data Analytics Virtual Internship Program
- Nordics Consultant
- Project Management
- Strategy Consulting
- Strategic Consultant Virtual Internship Program
- Climate & Sustainability
- Data Science & Advanced Analytics Virtual Internship
- Introduction To Strategy Virtual Internship Program
- Social Impact
- Cyber Security Consulting
- Digital Assurance
- Digital Intelligence
- Management Consulting
- Technology Consulting
- Switzerland Power BI in Data Analytics Virtual Case Experience
If you're here to learn what actually works in the data world — not just what looks good on LinkedIn - dig in.