Daniel Nuñez Ortiz Email & Phone Number

Berlin, Germany

Berlin, Berlin, Germany

Berlin, Germany

Data Analytics Program1. INSTACART: Grocery Basket Analysis. Exploratory analysis to uncover more information about their sales patterns.Translating business requirements | Data wrangling | Data integration | Data transformation | Statistical hypothesis testing | Visual analysis | Forecasting Storytelling | Presentation | ReportTech Stack: Python | Pandas | NumPy| Matplotlib | Seaborn | Tableau | Excel 2. Intentional Homicides: Illicit Drug Activities (Link)Exploratory Analysis. Understanding the factors that contribute to the level of Intentional Homicides in Europe is a crucial endeavor for policymakers, law enforcement agencies, and researchers alike. Data wrangling | Data cleaning | Data integration | Data transformation | Statistical hypothesis testing | Time-series analysis | Visual analysis | Forecasting | PresentationTech Stack: Python | Pandas | NumPy| Matplotlib | Seaborn | Tableau | Excel 3. Rockbuster Stealth’s Business Intelligence (BI) (link) (Fictional company)Statistical Business Analysis (BI).Statistical Analysis | Data cleaning | Data integration | Data transformation | Visual analysis | PresentationTech Stack: SQL | PostgreSQL | Tableau | Excel | MS Office4. GameCo; (Link)Performed a descriptive analysis of a video game data set to foster a better understanding of how GameCo’s new games might fare in the market.Grouping data | Summarizing data | Descriptive analysis |Visualizing results | Presenting resultsTech Stack: Excel | PowerPoint 5. Influenza Forecasting. (link)Analyzed the Influenza historical data identifying vulnerabilities.Translating business requirements | Data cleaning | Data integration | Data transformation | Statistical hypothesis testing | Visual analysis | ForecastingTech Stack: Tableau | Excel | MS Office

Berlin, Germany

− Utilized data-driven insights to analyze customer preferences, ingredient costs, and seasonal trends, resulting in data-backed menu adjustments that maximized profitability and customer satisfaction. | Menu Performance Optimization− Monitored ingredient usage, predict demand, and optimize inventory levels, minimizing wastage and ensuring efficient resource allocation. | Inventory Management and Forecasting− Monitored food quality, identifying patterns to enhance kitchen processes, leading to improved consistency and adherence to quality standards. | Quality Control Enhancement− Utilized data to create staff schedules, taking into account peak business hours and historical data on customer flow, resulting in better allocation of human resources and smoother operations. | Team Productivity Enhancement− Applied data analysis methodologies to evaluate supplier costs, and portion sizes, leading to informed decisions that optimized expenses while maintaining high culinary standards. | Cost Efficiency Strategies− Developed monthly data report to track key performance indicators (KPIs) such as dish popularity, customer feedback, and sales trends, enabling strategic adjustments for continuous improvement. | Performance Metrics Tracking− Conducted controlled experiments by introducing new dishes, measuring their success using data-driven metrics, and adapting the menu based on customer preferences. | Menu A/B Testing− Extracted valuable insights from customer data, such as dietary preferences and ordering behavior, to tailor menu offerings, resulting in increased customer engagement and loyalty. | Customer Insights Mining− Conducted market research to identify emerging culinary trends, enabling the creation of innovative dishes that aligned with evolving customer preferences. | Trend Analysis and Market Research (*)

Berlin, Germany

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Marbella, Andalusia, Spain

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London, England, United Kingdom

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