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.

1. Familiarize themselves with the data and understand its structure and content.
2. Use Python and SQL to extract, manipulate, and analyze the data, possibly using libraries such as pandas or NumPy.
3. Use PySpark or other tools to process and analyze large datasets, if applicable.
4. Use time series analysis techniques to identify trends and patterns in the data.
5. Develop predictive models that can be used to forecast future incidents and accidents, possibly using machine learning algorithms or other statistical methods.
6. Present the findings and recommendations to stakeholders, possibly in the form of a report or presentation.
7. Create visualizations or dashboards to help communicate the results of the analysis.

Our team will provide data, work with the students and meet on a regular basis to provide feedback and guidance