Junior Machine Learning Data Analyst - Automotive

Objective

This task focuses on the preliminary stages of data analysis in the automotive field. You will practice performing an in-depth Exploratory Data Analysis (EDA) using publicly available data related to automotive metrics. The goal is to develop insights regarding trends, anomalies, and relationships present in the data and to document your findings in a well-structured DOC file.

Expected Deliverables

• A DOC file containing a detailed EDA report.
• Steps include data exploration, visualization, and interpretation of results.
• An introduction to the data, summary of findings, and recommendations for further analysis.

Key Steps

1. Choose an open automotive dataset from a public repository.
2. Describe the dataset, including variables, potential issues, and data types.
3. Perform statistical analysis to identify significant patterns and anomalies.
4. Create visualizations (charts, graphs, histograms) to support your findings.
5. Write a detailed report in a DOC file explaining your approach, observations, any limitations encountered, and potential areas for further investigation.

Evaluation Criteria

Your submission will be evaluated based on clarity, depth of analysis, inclusion of relevant visualizations, and structure of the report. Extra points will be given for clear interpretation of the EDA results and thoughtful recommendations for future steps. The report must be self-contained and include explanations of each step, ensuring it is understandable to individuals with basic knowledge of data analysis.

This task is designed to take approximately 30 to 35 hours to complete. Spend time planning your approach before diving into the analysis so that your final DOC file is clear, detailed, and professional. Avoid any need for platform-specific resources; rely solely on publicly available data and documentation tools.

Objective

This task is aimed at enhancing your skills in data preprocessing and feature engineering. In the context of automotive data analysis, you will work on cleaning raw data, handling missing values, and creating new features that can potentially improve machine learning model performance. You will document each step in a comprehensive DOC file.

Expected Deliverables

• A DOC file outlining your data preprocessing workflow.
• A detailed explanation of the feature engineering process including newly created or transformed features.
• Appropriate visualizations and code snippets (as text or pseudocode) where relevant.

Key Steps

1. Select a publicly available automotive dataset or create a simulated dataset if necessary.
2. Identify key challenges in the dataset such as missing values, outliers, or noise, and describe the methods you used to address these issues.
3. Develop at least two new features that aim to capture important relationships within the data and justify why these features were engineered.
4. Include visual illustrations that highlight the impact of your preprocessing and feature engineering steps.
5. Compile your findings, methodologies, and insights into a DOC file that serves as a step-by-step record of your work.

Evaluation Criteria

Your submission will be judged on completeness, methodical approach, and clarity of explanation. Extra credit will be awarded for innovative feature engineering solutions and clear illustrations that justify your choices. The DOC file should be formatted professionally and include section headers, bullet points, and visuals where necessary. This exercise is expected to take around 30 to 35 hours of work.

Objective

This week, your focus will shift to the development and evaluation of a basic machine learning model within an automotive context. Using a publicly available dataset, you will develop a baseline predictive model, evaluate its performance, and discuss potential improvements. All documentation should be compiled into a well-organized DOC file.

Expected Deliverables

• A detailed DOC file outlining your modeling approach.
• A description of how you selected the model, including any preprocessing steps relevant to modeling.
• Interpretation of evaluation metrics and discussion on model performance along with potential steps for further improvement.

Key Steps

1. Select a public dataset relevant to the automotive field that includes both target and predictor variables.
2. Clearly describe your data splitting method (training and testing partitions) and why you chose the method.
3. Develop a baseline model using a regression or classification algorithm as per data relevancy.
4. Evaluate the model using appropriate metrics (such as RMSE, accuracy, or F1-score) and visually represent these evaluations where possible.
5. Discuss the strengths and weaknesses of your chosen model and propose ideas for refining the approach. Ensure you document every experimental step, including any challenges you encountered along the way.

Evaluation Criteria

The evaluation will be based on your ability to articulate the modeling process, effectively evaluate model performance, and propose constructive recommendations for future improvements. Clarity, thoroughness, and the professional presentation of your DOC file are essential. Remember: the work should be fully documented and self-contained, taking approximately 30 to 35 hours to complete.

Objective

This task is designed to enhance your data storytelling and visualization skills. You are required to create a comprehensive visual report of automotive data insights using charts, graphs, and any other visualization techniques. The final report should not only present visual data but also offer clear interpretations and insights to support data-driven decision-making. Your final deliverable will be a DOC file that encapsulates all your work.

Expected Deliverables

• A DOC file that includes multiple visualizations such as bar charts, line graphs, heat maps, or scatter plots.
• Explanatory sections on how each visual was created and what it represents.
• Conclusions and recommendations based on the visualized data.

Key Steps

1. Choose one or more publicly available automotive datasets or use simulated data representative of the automotive industry.
2. Identify key metrics and trends that are significant within the dataset.
3. Create at least three distinct visualizations that clearly display these trends or relationships.
4. Accompany each visualization with a detailed explanation discussing how it was produced, what it indicates about the data, and why the chosen visualization method is appropriate.
5. Include a summary section that discusses the overall insights gathered and how they could guide business or operational decisions in the automotive sector.

Evaluation Criteria

Your work will be evaluated on creativity, clarity, analytical depth, and the overall aesthetics of your visualizations. A DOC file that is professionally formatted, logically structured, and contains detailed descriptions and conclusions is expected. Make sure to illustrate any assumptions made during your analysis. The task is intended to take approximately 30 to 35 hours to complete.

Objective

This week’s task integrates previous technical skills into formulating strategic business insights in the automotive industry. Your role is to analyze trends, evaluate market strategies, and make informed recommendations for data-driven decision-making. This exercise simulates a real-world scenario where you outline potential strategies based on your analytical findings. Your final submission will be a DOC file that contains your entire analysis, data interpretation, and strategic recommendations.

Expected Deliverables

• A DOC file containing a comprehensive strategic report.
• An analysis section that details trends and findings derived from automotive data.
• A recommendations section outlining actionable strategies and potential impact analysis.

Key Steps

1. Research publicly available information and datasets related to automotive trends, consumer behavior, or production metrics.
2. Synthesize your findings into a coherent analysis that highlights key insights and market trends.
3. Develop strategic recommendations that could be implemented to address identified challenges or leverage emerging opportunities.
4. Support your recommendations with visual aids, graphs, or tables that help illustrate the numerical and qualitative rationale behind your strategies.
5. Document all your steps, thought processes, data interpretations, and conclusions in a detailed DOC file.

Evaluation Criteria

Your submission will be assessed based on the depth of your analysis, the logical flow of your arguments, and how well your recommendations are supported by data. Professional formatting, clarity, and adherence to the task requirements in your DOC file will be highly valued. The report should demonstrate your ability to connect data insights to strategic business decisions. This exercise is expected to require 30 to 35 hours of careful analysis, planning, and documentation. It must be a self-contained, finalized document that makes a clear case for your recommendations without the need for additional resources.