Virtual Data Science Apprentice Intern

Objective

The goal for Week 1 is to design a complete data acquisition and pre-processing pipeline using Python. The task is designed to help you develop skills in gathering data from public sources, cleaning raw data, and preparing it for analysis. You will produce a comprehensive DOC deliverable that explains your approach, methodology, and results.

Expected Deliverables

• A DOC file explaining your data acquisition strategy (select any publicly available data source of your choice).
• An explanation of the data cleaning process, including steps such as dealing with missing values, outlier detection, and normalization.
• Python code snippets embedded or explained within the DOC file that detail each step of the pre-processing pipeline.
• A summary section with conclusions and potential next steps for further analysis.

Key Steps to Complete the Task

1. Data Selection: Choose a publicly available dataset relevant to data science. Describe your choice and its relevance.
2. Data Acquisition: Define the method by which the data is acquired (e.g., via APIs, web scraping, or downloading from public repositories). Include sample Python code.
3. Pre-processing Plan: Outline strategies for cleaning data including missing value treatment, type conversion, normalization/scaling, and potential feature selection.
4. Execution: Execute the plan using Python, and document any challenges and how you resolved them.
5. Documentation: Document each step in a structured manner in your DOC file with code snippets, observations, and plotted outputs if applicable.

Evaluation Criteria

Your submission will be evaluated based on clarity of explanation, the logical flow of data acquisition and cleaning steps, thoroughness of the pre-processing methods, correct usage of Python libraries, and quality of written documentation. The DOC file must demonstrate your problem-solving approach and be well-organized, using sections and subsections to detail each step.

This task is expected to take approximately 30 to 35 hours of dedicated work, including planning, coding, and documentation. Be sure to proof-read your DOC file to ensure it is professional and comprehensive. Good luck!

Objective

The objective for Week 2 is to conduct an in-depth exploratory data analysis (EDA) and visualization using Python. This task will help you utilize Python’s data visualization libraries to reveal patterns, trends, and insights from the dataset selected in Week 1 or a new publicly available dataset. The outcome should be a detailed DOC file that explains your exploratory processes, findings, and visualizations.

Expected Deliverables

• A DOC file that outlines the EDA process using Python.
• A set of visualizations (charts, graphs, plots) created using libraries such as Matplotlib, Seaborn, or Plotly.
• Interpretation and commentary on each visualization produced, discussing insights and emerging data patterns.
• Python code snippets that illustrate how the visualizations were created.

Key Steps to Complete the Task

1. Dataset Description: Provide a brief overview of the dataset, including key variables and their potential relationships.
2. EDA Approach: Describe the EDA techniques to be utilized such as summary statistics, distributions, correlations, and segmentation analyses.
3. Visualization Creation: Generate multiple visualizations using Python libraries to highlight different aspects of the data. Ensure each chart has labels, legends, and captions.
4. Interpretation: Analyze the visualizations and document the insights in the DOC file. Discuss how these insights could drive business decisions or further data analysis.
5. Documentation: Include detailed code snippets, commentary, and method explanations to make your analysis reproducible.

Evaluation Criteria

Your work will be assessed on the clarity and depth of your exploratory analysis, the correctness of the visualizations, and the quality and reproducibility of your code and documentation. The DOC file must be well structured, include substantial explanations, and be written in a professional manner. The task is designed for a 30 to 35 hour engagement period, so thoroughness and clarity are essential.

This task requires no external resources aside from publicly available datasets and documentation. Focus on making your analysis self-contained and comprehensible to peers in a data science context.

Objective

During Week 3, you are tasked with performing feature engineering and developing a basic predictive model using Python. This assignment is intended to test your ability to manipulate and engineer features from raw data, and then implement a simple model such as linear regression, decision trees, or clustering, depending on the nature of your dataset. Your final submission should be a comprehensive DOC file that details every stage of your process.

Expected Deliverables

• A DOC file outlining your feature engineering process, including feature selection/creation and transformation methodologies.
• A description of the predictive model you have built, including Python code that demonstrates model implementation, training, and testing.
• Results of the model's performance with appropriate evaluation metrics.
• Interpretation of the outcomes along with potential improvements for future iterations.

Key Steps to Complete the Task

1. Data Preparation: Revisit the dataset from previous tasks or use another publicly available dataset, and describe the data context.
2. Feature Engineering: Identify and create new features that could improve model performance. Explain the rationale behind each new feature.
3. Model Development: Choose an appropriate algorithm and develop a baseline model using Python libraries like Scikit-Learn.
4. Model Evaluation: Evaluate model performance using relevant metrics (e.g., R-squared, accuracy, precision, recall). Include a discussion around these results.
5. Documentation: Provide well-commented code snippets and clear reasoning for each step in a structured DOC file with sections for problem description, approach, outcomes, and conclusions.

Evaluation Criteria

Submissions will be evaluated based on the creativity and relevance of your feature engineering process, the effectiveness of your model, clarity in documenting code and reasoning, and the robustness of your evaluation approach. The DOC file should be detailed, logically organized, and demonstrate a deep understanding of feature engineering and basic model building using Python. Expect to invest 30 to 35 hours on this task, ensuring you cover each step thoroughly.

Objective

The final week's task focuses on advanced model evaluation and hyperparameter tuning, coupled with presenting your findings in a comprehensive report. This assignment challenges you to refine your predictive model from Week 3 by implementing advanced evaluation techniques, performing hyperparameter tuning, and then preparing a detailed report in a DOC file. Your report should include a complete analysis, tuning results, and a discussion of final outcomes.

Expected Deliverables

• A DOC file that includes a complete evaluation and tuning process.
• A detailed explanation of hyperparameter tuning strategies (such as grid search, random search, or Bayesian optimization) used to improve model performance.
• Python code snippets or pseudocode that illustrate the tuning process.
• Final performance metrics and a discussion on model improvements or trade-offs.
• A comprehensive discussion section reviewing the entire modeling lifecycle, insights gained, limitations, and recommendations for further work.

Key Steps to Complete the Task

1. Review of Initial Model: Begin with an overview of the baseline model developed in Week 3, summarizing its performance.
2. Advanced Evaluation: Apply advanced evaluation techniques such as cross-validation and performance metric analysis. Explain the significance of each metric.
3. Hyperparameter Tuning: Implement tuning techniques using Python libraries like Scikit-Learn. Document your choices in selecting the parameters to optimize and describe the tuning process thoroughly.
4. Result Analysis: Compare the tuned model against the baseline, including visualizations such as performance graphs to illustrate improvements.
5. Final Reporting: Compile all findings, code snippets, and analysis into a well-organized DOC file. The report should include an executive summary, methodology, detailed results, discussion of limitations, and actionable insights for future work.

Evaluation Criteria

The submission will be judged on the clarity and depth of your advanced evaluation methods, soundness and reproducibility of your tuning process, accuracy of the final model, and the coherence and professionalism of your entire written report. The DOC file must be well-structured, achieving a balance between technical details and high-level insights. This task is intended for a 30 to 35 hour duration, so ensure your report is both detailed and reflective of rigorous analysis.

The entire assignment demands a self-contained approach using publicly available knowledge and datasets, without reliance on any proprietary resources. Best of luck on completing your final step as a Data Science Apprentice Intern!