Health Dashboard in Python/Flask

The project scope entails the development of a Python/Flask application for a health-related purpose, which will include the implementation of various charts. Learners participating in this project will have the opportunity to engage in a real-world application development scenario, focusing on data analysis and visualization within the context of health data. The dataset analysis will aim to provide insightful information that can significantly impact business outcomes and drive strategic decision-making.Throughout the project, learners will gain practical experience in data handling, analysis, and visualization using Python and Flask. They will be tasked with extracting valuable insights from the datasets, identifying trends, patterns, and correlations that could potentially contribute to improving processes and enhancing overall understanding within the health domain. Furthermore, learners will have the opportunity to contribute to the development of the app, implementing charts and visual representations to effectively communicate the findings derived from the dataset analysis.Participation in this project will provide learners with a comprehensive understanding of data analysis methodologies, emphasizing the significance of accurate interpretation and effective communication of findings. Additionally, learners will acquire valuable experience in utilizing Python and Flask for application development in the health sector, which can serve as a solid foundation for future career prospects in the fields of data science, software development, and health technology. The exposure to practical application development within the context of a health-related project will enable learners to refine their skills and foster a deeper understanding of the intersection between data analysis, technology, and healthcare, thereby preparing them to make meaningful contributions in the evolving landscape of the health industry.

Data to map the energy sector

The main goal for the project is to create an analysis of companies involved in the energy sector using publicly available data. The students are expected to collect and organize data from various sources to create a comprehensive map of the energy sector. The outcome of the project should be a detailed analysis of the companies involved in the energy sector. This data will then be used by the data science and marketing teams. This analysis will provide valuable insights for the company, helping it make informed decisions about investments and partnerships in the energy sector.

Strategic data science software development roadmap - features and ideas

The main goal for this project is to develop a strategic data science software development roadmap for Sofvie, a company that specializes in occupational health and safety. The goal is to identify research questions of interest and set a roadmap for the development of future modules.The problem that students will be solving is to determine how the various elements that Sofvie works with can be integrated to support the data science team in their efforts to improve occupational health and safety. This will involve hypothesis testing to see if variables are related and describing ideas for future modules at the intersection of our data (what we can figure out) and helping build future development plans. This roadmap will include specific modules and features that can be implemented in the future, and will be based on research questions of interest and the integration of various elements that Sofvie works with.

Time series analysis

For this project, students will be tasked with using Python, SQL, and PySpark to analyze occupational health and safety data and make predictions. The goal of the project is to use time series analysis techniques to identify trends and patterns in the data, and to develop models that can be used to make informed predictions about future incidents and accidents.Once the data has been analyzed, students will need to use time series analysis techniques to identify trends and patterns, and to develop predictive models that can be used to forecast future incidents and accidents. These models may be implemented using machine learning algorithms or other statistical methods.Finally, students will need to present their findings and recommendations to stakeholders, possibly in the form of a report or presentation. This may involve creating visualizations or dashboards to help communicate the results of the analysis.