I can help you to..

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• Harvest data from different sources like APIs, SQL databases or messy Excel spreadsheets.
• Reformat and reshape data to make further analyses easier.
• Clean datasets removing outliers and abberant data.
• Create reproducible data preparation workflows
• Automatize the creation of data related reports

Example:

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• I collected tweets containing the #surf for 9 months to study how surfers use social media. I used the twitter API and made the data collection automatic thanks to a personnal webserver and a custom R script.
• I created a dashboard for a company called Terres Inovia. It is directly plugged on their SQL database and allows to access its information in real time.

Contact me

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Tell me more about your project at yan.holtz.data@gmail.com to see if we can work together.

Description

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This dashboard, created as part of a course project, provides an in-depth analysis of product sales across various countries. It showcases total revenue, total products sold, and monthly sales trends. The dashboard highlights top and bottom products, offering valuable insights into key performers.

The interactive map allows for a geographical breakdown, enabling users to understand regional sales distributions. This tool is designed to facilitate strategic decision-making and drive data-driven insights.

Description

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This project combines a detailed COVID-19 dashboard with a clustering model and policy recommendation system, developed as part of a competition. The dashboard provides insights into case distributions and recovery rates, while the clustering model, created using Python and Streamlit, categorizes regions based on COVID-19 data.This helps in identifying zones and formulating effective health policies. The project aims to support data-driven decision-making for pandemic management

Description

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This dashboard is part of my final university project and will be embedded in a web app using the Laravel framework. It provides comprehensive analytics for Posyandu services, focusing on child health metrics. Users can filter by region, health facility, and child conditions to gain insights into total attendance, health status, and trends over time. The visualizations help in tracking and improving child healthcare outcomes, making data-driven decisions easier for healthcare providers.

Description

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This dashboard, developed as part of a research project with my professor at Universitas Airlangga, provides a comprehensive analysis of rice productivity, production, and land area in East Java from 2000 to 2020. It features visualizations of productivity across different cities and regencies, showing trends over the years.

The interactive map highlights regional variations, offering insights into agricultural performance. This tool supports data-driven decision-making in agricultural planning and development.

Description

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This dashboard, created using MicroStrategy, provides a detailed analysis of customer data, focusing on metrics such as total customers, loyalty, and sales distribution. It features interactive elements that allow users to filter by year and discount status. The visualizations include profit by category and region, and sales by category and segment, enabling comprehensive insights into customer behavior and sales trends. This tool aids in making informed business decisions and optimizing customer engagement strategies.

Description

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This project focuses on using Optical Character Recognition (OCR) to extract data from Indonesian election forms for the 2024 elections. The process involves two main steps: first, using object detection with the Detectron model to identify and create bounding boxes around numbers on the forms. Second, a Transformer-based model is employed to perform OCR on the detected areas, accurately extracting handwritten and printed text. This approach enhances the accuracy and efficiency of data processing, supporting comprehensive analysis and reporting. This project was also part of a competition, showcasing innovative solutions in data processing and electoral transparency.

Description

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This project focuses on predicting the likelihood of individuals receiving vaccines for H1N1 and seasonal influenza. The evaluation results are shown through ROC curves for both vaccines, where the AUC (Area Under the Curve) values indicate the model's effectiveness. For the H1N1 vaccine, the AUC is 0.8394, demonstrating good predictive performance. Similarly, the seasonal vaccine model achieves an AUC of 0.8585, indicating slightly better accuracy. These results suggest that the model is effective in distinguishing between vaccinated and non-vaccinated individuals, providing valuable insights for vaccination campaigns and public health strategies.

Description

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This project focuses on Natural Language Processing (NLP) techniques to cluster text from a collection of online news articles. Using TF-IDF for feature extraction, the text data is processed and analyzed to identify underlying patterns. KMeans clustering is then applied, categorizing the articles into three distinct topics: Government, COVID-19, and Natural Disasters. This approach allows for efficient organization and analysis of large datasets, providing valuable insights into prevalent themes in news coverage.