Automotive Data Science Analyst

Task Objective: In this first week, your challenge is to design a comprehensive research plan and strategy for an automotive data science analysis project. The focus is on clearly defining a relevant automotive problem that can be addressed using data science techniques in Python. You will outline objectives, potential data sources (publicly available), analysis methodology, and expected outcomes. Your final deliverable is a DOC file containing the complete research plan.

Expected Deliverables:

• A DOC file detailing the research plan.
• A clear problem statement related to the automotive industry.
• An outline of analysis techniques and potential Python libraries that could be relevant.

Key Steps to Complete the Task:

1. Problem Identification: Research current trends and common challenges in the automotive industry, e.g., vehicle performance analytics, predictive maintenance, or customer behavior profiling. Formulate a clear problem statement.
2. Planning Your Approach: Identify data sources (from public repositories) and propose a methodology using Python for data cleaning, preprocessing, analysis, and visualization.
3. Tool and Library Selection: List Python libraries (such as pandas, numpy, matplotlib, seaborn, scikit-learn) you plan to use and explain their role in addressing the problem.
4. Timeline and Milestones: Create a timeline highlighting key milestones for the project execution phase.
5. Documentation: Present your findings and planned approach in a detailed DOC file.

Evaluation Criteria:

• Clarity and relevance of the problem statement.
• Depth of research and justification for chosen techniques.
• Logical and structured planning including a realistic timeline.
• Correct use and explanation of data science techniques and Python libraries.
• Quality and professionalism of the DOC file presentation.

This assignment requires approximately 30-35 hours of work. Your submission must be self-contained, relying only on public data sources and your independent research.

Task Objective: In the second week, your task is to simulate or obtain a publicly available automotive dataset (for instance, related to vehicle performance or sales trends) and perform comprehensive data cleaning and exploratory analysis using Python. The goal is to understand the data, identify patterns, anomalies, and initial insights that could lead to more in-depth analysis in later stages. Your analysis should be thoroughly documented in a DOC file.

Expected Deliverables:

• A DOC file containing the documentation of your exploratory data analysis (EDA).
• A description of the dataset’s source, structure, and key variables.
• An explanation of the data cleaning process including handling missing values, outlier detection, and data transformations.
• Visualizations and initial findings that summarize data trends.

Key Steps to Complete the Task:

1. Data Source Identification: Locate a publicly available automotive dataset or generate/simulate one that is rich enough for analysis.
2. Data Cleaning: Perform a detailed cleaning process by addressing missing or corrupt data and documenting the steps taken.
3. Exploratory Analysis: Use Python (pandas, matplotlib, seaborn, etc.) to explore the dataset. Generate visualizations such as histograms, scatter plots, and box plots to understand distributions and identify patterns or anomalies.
4. Documentation: Write a comprehensive description in a DOC file outlining your methodology, steps, visual representations, and early insights from the data.

Evaluation Criteria:

• Comprehensiveness of the data cleaning process.
• Depth and clarity in the exploratory analysis.
• Quality of visualizations in terms of readability and relevance.
• Accuracy of interpretations supported by data evidence.
• Professional document structure and presentation quality in the DOC file.

This task is designed to require around 30-35 hours of work, ensuring a detailed self-contained analysis that leverages only publicly available resources.

Task Objective: This week’s assignment emphasizes the model development phase using Python to solve a predictive problem in automotive data science. Your task is to choose an appropriate machine learning model based on the exploratory analysis from Week 2, train it, and evaluate its performance. The focus is on applying advanced methodologies and validating the model using standard performance metrics. Your final deliverable is a DOC file that thoroughly documents your modeling process, results, and conclusions.

Expected Deliverables:

• A DOC file report that outlines the entire modeling process.
• An explanation of the machine learning model selected (for instance, regression, decision trees, or clustering algorithms) and justification for your choice.
• Details on data splitting, feature selection, and data preprocessing steps relevant to the modeling task.
• Presentation of model training results, performance metrics, and any encountered challenges.

Key Steps to Complete the Task:

1. Model Selection and Justification: Based on your previous exploratory analysis, select the machine learning model that could best predict or analyze the automotive data issue identified. Explain why the model is suitable.
2. Data Preparation: Describe your process for splitting the dataset into training and testing sets, selecting relevant features, and performing any additional data transformations.
3. Model Training and Evaluation: Train the chosen model using Python libraries like scikit-learn, and evaluate its performance using metrics such as accuracy, F1-score, mean squared error, or RMSE. Provide visualizations of model performance.
4. Documentation: Write a comprehensive report in a DOC file documenting every step from model selection to evaluation, including code snippets (if needed) and interpretation of results.

Evaluation Criteria:

• Soundness of the machine learning model rationale.
• Detailed explanation of data preprocessing and model training steps.
• Effectiveness and clarity of performance evaluation.
• Quality of documentation, including structure, clarity, and presentation in the DOC file.
• Overall depth of analysis and correctness in the application of advanced data science techniques.

The expected effort for this task is approximately 30-35 hours, requiring methodical and self-contained execution using only publicly accessible resources.

Task Objective: In the final week, your task is to consolidate the work done in the previous weeks by evaluating the predictive model and generating actionable insights and strategic recommendations for the automotive industry. This assignment is designed to emphasize the importance of translating data-driven insights into strategic business recommendations. You should produce a detailed DOC file that includes an evaluation of the model, discussions on its limitations, and recommendations for further analysis or business applications.

Expected Deliverables:

• A DOC file containing a comprehensive evaluation report.
• A summary of key insights drawn from your model’s performance and exploratory data analysis.
• A critical discussion on the limitations of your approach and potential areas for improvement.
• Actionable and strategic recommendations that could help solve real-world automotive challenges.

Key Steps to Complete the Task:

1. Model and Results Evaluation: Critically assess the performance of your predictive model. Discuss the accuracy, strengths, and weaknesses based on the evaluation metrics you computed in Week 3.
2. Deriving Insights: Identify and document key insights from the data analysis and model outputs. How do these insights relate to current challenges in the automotive sector?
3. Recommendations and Strategy: Based on your evaluation, propose informed and actionable strategies that stakeholders in the automotive industry might adopt. Highlight any potential improvements or further studies that could yield better insights.
4. Documentation: Develop a final DOC file report that organizes your analysis, evaluation, and recommendations. Ensure the report is structured, detailed, and self-contained.

Evaluation Criteria:

• Insightfulness and relevance of the evaluation and discussion.
• Clarity and quality of the recommendations provided.
• Depth of analysis regarding the strengths and limitations of the model.
• Logical structuring and professionalism in the DOC file presentation.
• Overall integration of data science techniques with practical strategic insights.

This task is anticipated to take approximately 30-35 hours. It is essential that your submission is self-contained, relying solely on your independent research and publicly available data sources, with no requirement for additional resources from any platform.