Job Description
Automation Project Management Data Analytics using Power BI, python and excel or other recommendation within company&aposs scope
Main Task 1
Testing and modification on existing descriptive analytics visualization for automation department
Task Elements for Main Task 1
- Testing and Modification on Existing Descriptive Analytics Visualization for the Automation Department
- Current State Assessment: Evaluate the current descriptive analytics visualization tools and dashboards to understand their strengths and weaknesses.
- User Feedback Gathering: Collect feedback from end-users and stakeholders in the automation department to identify pain points and areas for improvement.
- Data Quality Assessment: Assess the quality and accuracy of the data sources used in the existing visualization.
- Benchmarking: Compare the existing descriptive analytics tools with industry best practices to identify areas where enhancements are needed.
- Modification Plan: Develop a plan for modifying the existing visualization, including a roadmap of changes, prioritized enhancements, and estimated resource requirements.
- Visualization Design: Work on redesigning and improving the current visualization to make it more informative and user-friendly.
- Usability Testing: Conduct usability testing with end-users to validate the modifications and gather feedback for further refinements.
- Data Integration: Ensure that the visualization is effectively integrated with various data sources and systems.
- Performance Testing: Evaluate the performance of the modified visualization, including load times and responsiveness.
- Documentation Update: Update documentation and user guides to reflect the changes made to the existing descriptive analytics visualization.
Main Task 2
Design and testing new prescriptive analytics for automation department
Task Elements for Main Task 2
- Requirements Gathering: Collaborate with stakeholders to gather detailed requirements for the new prescriptive analytics solution.
- Data Sources Identification: Identify the relevant data sources and ensure their accessibility for prescriptive analytics.
- Solution Design: Develop a comprehensive design for the new prescriptive analytics system, including data models, algorithms, and user interfaces.
- Prototyping: Create a prototype or proof of concept to validate the design and demonstrate the potential value of the solution.
- Development: Build the prescriptive analytics solution based on the approved design, incorporating machine learning and predictive modeling.
- Testing Framework Setup: Establish a testing framework that includes unit testing, integration testing, and end-to-end testing for the new solution.
- Data Validation: Ensure that data inputs for the prescriptive analytics model are accurate and free from anomalies.
- Algorithm Testing: Test the prescriptive algorithms for accuracy, reliability, and performance.
- User Acceptance Testing (UAT): Involve end-users in UAT to verify that the solution meets their needs and provides actionable insights.
- Documentation and Training: Create documentation for the new prescriptive analytics solution and provide training to users and stakeholders.
- Deployment and Monitoring: Deploy the solution to the production environment and set up monitoring to track its performance and user adoption.
- Feedback Loop Implementation: Establish a process for collecting and incorporating user feedback to continuously improve the prescriptive analytics solution.
