Tasks and Duties
Task Objective
Your objective for Week 1 is to develop a comprehensive data collection strategy for automotive data quality assurance. This task is aimed at planning the ways to collect and review automotive data sources using techniques and tools taught in your Data Science with Python course. You will focus on identifying potential public data sources, evaluating their reliability, and drafting guidelines for data extraction and ingestion.
Expected Deliverables
- A DOC file containing a detailed Data Collection Strategy Report.
- The report must include your rationale for selecting specific data sources, criteria for data quality evaluation, and a list of potential public datasets.
Key Steps to Complete the Task
- Research Phase: Spend time researching automotive industry data available in the public domain. Identify at least five datasets and document their sources.
- Evaluation Phase: Outline a set of quality criteria (e.g., timeliness, accuracy, consistency) to evaluate each dataset.
- Strategy Formulation: Draft a strategy document that describes collection methods and tools (Python libraries such as Requests, BeautifulSoup, or Pandas for data handling) and includes potential data pipelines.
- Documentation: Prepare a DOC file that compiles your findings, strategy, and recommendations. Make sure it is well-organized and clearly written.
Evaluation Criteria
Your submission will be evaluated based on clarity of the strategy, depth of research, relevance and detail in the evaluation criteria, and correct usage of Python data collection techniques. It should reflect a deep understanding of data quality principles and provide actionable guidelines for a data quality assurance process.
Task Objective
This week, you will design an automated data validation pipeline tailored to automotive datasets. The goal of this task is to demonstrate your ability to conceptualize and design a pipeline that leverages Python programming to check data correctness, completeness, and consistency. The task focuses on planning, strategy, and architectural design.
Expected Deliverables
- A DOC file that describes the complete pipeline architecture.
- A detailed description of each component, including data ingestion, transformation, error detection, and logging mechanisms using Python libraries and tools.
Key Steps to Complete the Task
- Define Requirements: List the data quality checks and validations required (e.g., missing values, data type mismatches, range checks).
- Design Pipeline Architecture: Create a detailed design that includes flow diagrams and pseudocode. Discuss your choice of Python libraries (e.g., Pandas, NumPy, logging) for each task.
- Simulate Data Flow: Explain how data would traverse the pipeline, including processing steps and error handling routines.
- Document Everything: Write a DOC file report that captures your design decisions, the structure of the pipeline, and your rationale behind each component. Ensure the document is well-organized and illustrative.
Evaluation Criteria
Your work will be graded on clarity, technical correctness, depth of detail in the design, feasibility of the pipeline, and the effective use of Data Science with Python principles in your validation strategy.
Task Objective
For Week 3, your focus is on developing a data quality analysis framework and incorporating an anomaly detection mechanism. Using your knowledge from the Data Science with Python course, devise an approach to identify, analyze, and document anomalies in automotive datasets. The purpose is to ensure data integrity and reliability through systematic detection of inconsistencies.
Expected Deliverables
- A DOC file that outlines the framework and includes detailed documentation of anomaly detection methods.
- Incorporate sample Python pseudocode or conceptual code snippets that explain how data anomalies would be detected and flagged.
Key Steps to Complete the Task
- Define Anomalies: Start by identifying what constitutes an anomaly in automotive data, elaborating on data quality issues such as out-of-bound values and unexpected gaps.
- Create a Framework: Develop a theoretical framework that uses techniques from statistics and machine learning as taught in your coursework. Explain the rationale behind method choices.
- Develop Pseudocode/Code Samples: Draft pseudocode or code snippets highlighting how Python libraries (e.g., Scikit-learn, Pandas) can be used to detect anomalies.
- Compile Documentation: Prepare a comprehensive DOC file that contains all details, framework diagrams, methodology, and theoretical results.
Evaluation Criteria
Your submission will be evaluated based on the conceptual soundness of the framework, clarity of your documentation, the technical quality of pseudocode or code excerpts, and how well the documentation communicates the process of anomaly detection.
Task Objective
Your final week task is to produce an exhaustive report on data quality analysis. This task integrates your strategic planning, pipeline design, and anomaly detection efforts into an end-to-end report that communicates insights and recommendations for improving automotive data quality. You will employ Python-based data visualization techniques to present your findings in a clear, concise, and actionable format.
Expected Deliverables
- A final DOC file comprising a comprehensive report that collates all previous work findings, analysis methods, and recommendations.
- The report should include sections dedicated to visualization of data quality metrics (using libraries like Matplotlib or Seaborn), detailed descriptions of each analysis phase, and final recommendations for process improvements.
Key Steps to Complete the Task
- Integrate Findings: Consolidate your work from prior weeks including data collection, pipeline design, and anomaly detection into one cohesive narrative.
- Design Visualizations: Identify key metrics from your data quality framework and design sample visualizations. Provide conceptual examples or sketches of how these graphs would look.
- Report Structuring: Divide your DOC report into clear sections: Introduction, Methodology, Findings, Visualizations, Recommendations, and Conclusion.
- Final Documentation: Write an elaborate DOC file report covering all elements. Emphasis should be on clarity, thorough explanation of technical processes, and strong visual support.
Evaluation Criteria
The evaluation will focus on the comprehensiveness of the report, clarity of data visualization explanations, the integration of previous work, and the actionable nature of your recommendations. Your ability to communicate complex data quality issues with clarity and effectiveness using Python-based tools is key to success.