Week 1 Task: Data Cleaning and Pre-processing:

The first step in any data analysis project is to clean and pre-process the data. For this task, you need to find a publicly available dataset and perform data cleaning and pre-processing tasks on it. You can use any data cleaning tools or programming languages you're comfortable with. The dataset should be large enough to present some challenges (missing values, outliers, etc.). After cleaning the data, write a detailed report that includes the steps you took, why you took them, and any observations or insights you gained during the process. The report should be submitted as a DOC file and should take approximately 30 to 35 hours of work. The evaluation will be based on the appropriateness of the data cleaning and pre-processing techniques used, the quality of the report, and the insights provided.

Week 2 Task: Exploratory Data Analysis:

After cleaning and pre-processing a dataset, the next step is to perform exploratory data analysis. For this task, use the cleaned dataset from Week 1 and perform an exploratory data analysis. Create visualizations, calculate summary statistics, and generate insights about the data. Write a detailed report outlining your process, findings, and any interesting insights you discovered. The report should be submitted as a DOC file and should take approximately 30 to 35 hours of work. The evaluation will be based on the thoroughness of the analysis, the quality of the visualizations, the significance of the findings, and the overall quality of the report.

Week 3 Task: Data Modelling:

This week, your task is to build a predictive model using the cleaned dataset from Week 1. You may choose to predict any variable of interest in the dataset. Your model should take into account the insights you gained during the exploratory data analysis in Week 2. Write a report detailing your model, the techniques used, the model's performance, and any insights or conclusions you can draw from the model. The report should be submitted as a DOC file and should take approximately 30 to 35 hours of work. The evaluation will be based on the soundness of the model, the quality of the report, and the insights provided.

Week 4 Task: Data Visualization:

The ability to communicate complex data insights is a critical skill for a Senior Data Analytics Engineer. This week, your task is to create a set of data visualizations that tell a compelling story about the dataset you've been working with. You can use any visualization tools or libraries you prefer. Write a detailed report that includes your visualizations and a narrative that explains what each visualization shows and why it's meaningful. The report should be submitted as a DOC file and should take approximately 30 to 35 hours of work. The evaluation will be based on the effectiveness of the visualizations, the quality of the narrative, and the overall impact of the data story.

Week 5 Task: Data Strategy:

For the final week, your task is to develop a data strategy for a hypothetical organization that could benefit from the dataset you've been working with. Your strategy should include how the organization could collect, clean, analyze, and visualize the data to make better decisions. Write a detailed report that outlines your data strategy, including specific recommendations and potential impacts. The report should be submitted as a DOC file and should take approximately 30 to 35 hours of work. The evaluation will be based on the feasibility of the strategy, the quality of the report, and the potential impact of the recommendations.