Tasks and Duties
Task Objective
The objective for Week 1 is to define a clear strategy and plan for an automotive data visualization project using Python. You will identify key performance indicators and outline a comprehensive plan to analyze automotive trends. This task is designed to help you develop a robust project plan and establish the foundations required for insightful data visualization.
Expected Deliverables
- A detailed DOC file containing a project plan.
- A section on mapping out potential automotive datasets available publicly.
- A written explanation of the data visualization techniques you plan to use.
Key Steps to Complete the Task
- Project Planning: Write a detailed project plan outlining objectives, methodologies, and expected outcomes.
- Data Source Identification: List and critically analyze publicly available automotive datasets or data articles, emphasizing relevance to trends like sales, performance, or consumer behavior.
- Data Visualization Strategy: Justify which Python libraries (e.g., Matplotlib, Seaborn, Plotly) you will use and explain your approach.
- Timeline and Milestones: Develop an estimated timeline with key milestones to guide the eight-step project development process.
Evaluation Criteria
Your submission will be evaluated on the depth and clarity of your strategy, the coherence in planning, the feasibility of the project timeline and milestones, and the quality of writing. The task should be thoroughly detailed and exceed 200 words, ensuring you provide a comprehensive plan that can be implemented within 30-35 hours of work.
Task Objective
This task focuses on data cleaning and preprocessing for automotive datasets. You will work on converting raw data into a structured format fit for analysis and visualization. With a focus on Python, this task requires you to employ data science libraries to clean, manipulate, and prepare data for insightful visualizations.
Expected Deliverables
- A DOC file detailing the data cleaning process.
- Descriptions of techniques and Python code snippets used for data preprocessing (include pseudo-code explanations if necessary).
- Summary of challenges encountered and how you overcame them.
Key Steps to Complete the Task
- Dataset Simulation: Choose a public automotive dataset or create a synthetic dataset that simulates real-world scenarios such as fuel consumption figures or maintenance records.
- Data Cleaning: Document each step of cleaning the data including handling missing values, data type conversions, and outlier analysis.
- Preprocessing Techniques: Outline methods such as normalization, scaling, or encoding categorical variables.
- Documentation: Write detailed explanations for each applied technique, linking back to relevant Python libraries like pandas and numpy.
Evaluation Criteria
Your submission will be assessed on clarity, detail of documentation, the soundness of methodologies used, and the practical relevance of your explanation. Ensure the DOC file is comprehensive, detailed, and exceeds 200 words. This task should be completed within 30-35 hours of focused work.
Task Objective
This week’s focus shifts to exploratory data analysis (EDA) and visualization. Your aim is to analyze automotive data trends through statistical summaries and create visual representations using Python. The exercise underscores the application of visualization techniques to derive insights from data and draw actionable conclusions regarding automotive trends.
Expected Deliverables
- A DOC file summarizing your EDA findings and visualization insights.
- Annotated code snippets or pseudo-code for reproducible Python analysis.
- Charts, graphs, and explanations of the insights derived.
Key Steps to Complete the Task
- Conducting EDA: Utilize Python libraries to compute summary statistics and identify trends within your automotive dataset.
- Data Visualization: Develop visualizations such as histograms, bar charts, scatter plots, or box plots using libraries like Seaborn and Matplotlib. Explain the significance of each chart.
- Insight Generation: Provide interpretations of the visualizations that highlight key trends or anomalies.
- Documentation: Document the entire process, including an explanation of the chosen visual techniques and any challenges encountered.
Evaluation Criteria
Your DOC file will be evaluated on the quality and depth of EDA performed, the clarity of your visualizations, and the insights derived. Your report should be detailed, exceeding 200 words, and closely tie the analysis to practical automotive data trends. Completeness, clarity, and the accuracy of your analysis are crucial. This task is expected to require 30-35 hours of comprehensive work.
Task Objective
The final week emphasizes the development of an interactive dashboard using Python to present automotive data in a user-friendly manner. This task will have you integrate your previous work into an interactive visualization dashboard that makes the data accessible and actionable. The goal is to create a presentation-ready document that transforms static data insights into interactive visual narratives.
Expected Deliverables
- A DOC file which includes the conceptual design of the interactive dashboard and the rationale behind each component.
- An explanation of Python frameworks (e.g., Plotly Dash, Streamlit) intended for dashboard creation.
- Wireframes or mockups (described in text) detailing the dashboard’s layout and functionality.
Key Steps to Complete the Task
- Conceptual Design: Describe a comprehensive plan for an interactive dashboard designed to visualize automotive data trends. Define user interaction flow, key charts, and functionalities.
- Tool Selection and Justification: Provide a detailed explanation for your choice of Python frameworks, discussing their advantages in the context of interactive visualizations.
- Dashboard Components: Identify various components that will be included such as filters, dynamic graphs, and key performance indicators related to automotive metrics.
- Challenges and Future Steps: Write a section on potential challenges you foresee in the implementation and propose possible solutions or further improvements.
Evaluation Criteria
Your submission will be judged on the creativity, feasibility, and clarity of your dashboard design. The DOC file should be detailed and exceed 200 words. Make sure there is a clear connection between the interactive dashboard concept and realistic automotive data visualization needs. Completeness, clarity, and the practical interpretation of the design are essential, and the entire task is expected to be completed in 30-35 hours.