I analyzed Amazon's operational challenges related to employee retention, focusing on the factors that lead to high turnover rates within the first 90 days of employment.

I collected and cleaned employee voice data from various online platforms to ensure accurate insights into workforce sentiments and experiences.

I identified sentiment and keyword patterns within the employee feedback data, revealing insights into common themes and concerns among the workforce.

In this project, you’ll use a mix of AI tools to uncover major themes in employee feedback and dig into the root causes behind key frustrations. You’ll visualize how sentiment varies by theme and map out cause-effect patterns, just like consultants do when turning noise into strategy.

[Week] 5-6

In this project, you’ll build your first AI agent: a Moodboard Generator that turns simple prompts like “modern living room with warm colors” into visuals. By setting up your workspace in n8n and running your first workflow, you’ll see how ideas can instantly become outputs. What feels like a creative experiment is actually the foundation of Wayfair’s edge: turning abstract trends into concrete visuals fast. This hands-on win sets you up to build the more advanced agents that will track market shifts, monitor competitors, and generate insights that matter for Wayfair..

I developed a Consumer Trend Discovery AI Agent that analyzed real-world data from sources like Amazon and design blogs. This agent identified emerging product trends, enabling Wayfair to detect shifts in consumer preferences faster than traditional methods.

In this project, you’ll shift from trend discovery to competitive intelligence: teaching your agent to think like a market strategist. Your agent will collect and analyze competitor product listings, new launches, price movements and reviews. The goal: to uncover what competitors are doing differently , how their customers are responding, and where Wayfair’s whitespace opportunities might lie. By the end of this project, your Competitor Monitoring AI Agent will surface actionable insights helping teams benchmark Wayfair’s products, spot early shifts in market positioning, and make faster, data-backed merchandising and marketing decisions.

In this project, you’ll evolve your existing agent into a marketing intelligence system, one that uses real trend and competitor data to generate ideas Wayfair’s content team could actually act on. You’ll work with a pre-built AI Insights & Content Agent and focus on refining its logic, creativity, and tone to produce sharper, brand-aligned outputs. By the end, you’ll have an agent that delivers on-brand blogs, captions, and campaign ideas, each grounded in real insights and built to drive marketing impact for Wayfair.

I established a solid foundation in data analytics, exploring its significance and applications within healthcare. I investigated social determinants of health and identified relevant data sources, enhancing my understanding of their role in public health decision-making.

I collected relevant data from public sources and utilized large language models (LLMs) to identify potential analyses. I then cleaned and pre-processed the data, ensuring it was ready for thorough analysis.

I developed my exploratory data analysis (EDA) skills, extracting insights from the datasets. By applying key statistical concepts and Python-based techniques, I created visualizations that effectively communicated my findings, exploring trends and relationships within the data.

I structured and presented my research findings by drafting a comprehensive report, refining it with AI tools, and integrating visualizations generated in Python. I crafted a compelling presentation that enhanced clarity and impact using AI-powered platforms.

I explored the dynamics of online sales, examining how businesses engage customers through e-commerce platforms. This involved analyzing various strategies and identifying best practices to enhance sales performance.

I navigated raw exported data from platforms like Shopify and Google Analytics, familiarizing myself with key metrics such as Sessions, Orders, and Revenue. This foundational work enabled me to filter and sort data effectively, revealing performance trends and insights.