Objective

The goal of this task is to develop a comprehensive plan for executing an exploratory data analysis (EDA) project using Python. You will outline a strategy for gathering publicly available data, cleaning it, exploring insights, and visualizing key trends. This task emphasizes the planning and strategy phase, equipping you with critical skills needed to break down a data science project before coding begins.

Expected Deliverables

• A detailed DOC file outlining the complete strategy
• A clear project timeline with phases and estimated time allocations
• Descriptions of planned data sources, cleaning techniques, EDA methodologies, and visualization tools
• Mock-up sketches or wireframes of proposed visualizations

Key Steps to Complete the Task

• Research: Identify at least two publicly available datasets that could be used for the analysis. Justify your choices based on relevance and quality.
• Plan: Create a detailed workflow for data cleaning, transformation, exploration, and visualization. Outline key questions you intend to answer through the analysis.
• Design: Conceptualize the visualizations you plan to develop. Include sketches or flowcharts to represent your ideas.
• Document: Summarize your findings, approach, and expected challenges within a DOC file format, ensuring rich detail on every step.

Evaluation Criteria

Your submission will be evaluated based on clarity, completeness, and originality in outlining your strategy. Each section of the DOC file should be logically structured, with justifications provided for each decision. Attention to detail, thorough planning, and effective communication of ideas are critical. The final document should read as a professional project proposal capable of guiding a real data science project.

This exercise is designed to take approximately 30 to 35 hours to complete and will prepare you for the dynamic tasks ahead in practical data science scenarios.

Objective

This task focuses on the execution phase of a data science project, specifically on applying statistical analysis and hypothesis testing using Python. You are required to design a framework that addresses real-life data scenarios through hypothesis formulation and predictive statistical experiments, ensuring you consider all aspects of data preprocessing, exploratory analysis, and validation methods. This exercise is practical, requiring you to prepare a comprehensive document as your final deliverable.

Expected Deliverables

• A completed DOC file detailing your statistical analysis framework
• Well-defined hypotheses relevant to the chosen data context
• A step-by-step methodology for executing statistical tests and hypothesis validation
• An explanation of the expected outcome and potential challenges, with solutions proposed

Key Steps to Complete the Task

• Define Hypotheses: Identify one or more research questions relevant to data science. Develop null and alternative hypotheses that can be empirically tested.
• Design Your Experiment: Outline the process for data collection (using publicly available data), cleaning, and preparation to ensure data integrity for statistical testing.
• Testing Methodology: Choose appropriate Python libraries and statistical methods (e.g., t-test, chi-square) and provide rationale for their use.
• Documentation: Collate every step including code snippets, expected challenges, and quality control measures into a DOC file with clear sections, diagrams, and rationale.

Evaluation Criteria

Your DOC file will be evaluated on the thoroughness of hypothesis formulation, the logic behind your experimental framework, and the clarity in the explanation of statistical tests and outcomes. The document should reflect both academic rigor and practical applicability and demonstrate that you have allocated sufficient time to planning as well as potential troubleshooting of experimental challenges in a data science context.

Objective

This task is designed to help you apply your Python skills into the realm of machine learning. You will develop a strategic plan for building, training, and evaluating a machine learning model using publicly available datasets. The focus is on the systematic process of model development, including data preprocessing, feature selection, algorithm choice, and performance evaluation metrics.

Expected Deliverables

• A DOC file that documents your complete model development plan
• A detailed outline of data preprocessing techniques and feature engineering strategies
• Selection of one or two machine learning algorithms with justification
• A comprehensive evaluation plan including accuracy metrics, confusion matrix, ROC curves, etc.

Key Steps to Complete the Task

• Data Preparation: Identify an appropriate publicly available dataset and outline steps required for cleaning and preprocessing the data. Describe any imputation techniques and normalization strategies you plan to use.
• Feature Engineering: Explain methods for feature extraction and selection, detailing why specific features are essential for your chosen model.
• Algorithm Selection: Compare at least two machine learning algorithms, describe their strengths and limitations, and determine the rationale for your final choice.
• Evaluation Strategy: List and describe possible evaluation metrics and validation techniques such as cross-validation, detailing how you will assess performance.

Evaluation Criteria

Your submission will be reviewed based on the comprehensiveness of the model development framework, the depth of technical analysis, and justification of algorithmic choices made. The DOC file should be structured, professional, and provide sufficient detail to demonstrate that you are capable of handling a complete machine learning pipeline. Clear articulation of potential pitfalls and proposed mitigation strategies will be a key factor in the assessment. Ensure your work is self-contained and demonstrates a solid foundation in both strategy and technical execution.

Objective

In this final task, you are to synthesize the core components of a data science project into a professional report. This exercise aims to bridge the gap between technical execution and strategic communication. You will compile a detailed project report that includes an executive summary, methodology, findings, analysis, visualizations, and final recommendations. Emphasis should be placed on how to communicate technical data insights to a non-technical audience.

Expected Deliverables

• A DOC file containing a comprehensive project report
• An executive summary that highlights the core findings and strategic recommendations
• A detailed explanation of the methodologies used including data cleaning, EDA, statistical testing, and machine learning model development
• Visual representations such as charts, graphs, and tables integrated either as screenshots or embedded objects

Key Steps to Complete the Task

• Compile your Previous Work: Use notes and strategies from earlier tasks to consolidate your approach and results.
• Structure the Report: Organize your DOC file into clear sections such as Introduction, Methodology, Results, Discussion, and Conclusions. Include a section for recommendations based on the data analysis performed.
• Visualization and Communication: Explain how you chose specific visualization techniques for clarity and impact. Ensure these visualizations are described in detail and their implications discussed.
• Presentation: Reflect on the limitations of your analysis and provide suggestions for future work or improvements. The report should be well-formatted and professionally written.

Evaluation Criteria

Your report will be evaluated on its clarity, comprehensiveness, and ability to translate technical details into actionable insights for stakeholders. The quality of structure, consistency in formatting, and coherence of the narrative will be critical. Your final DOC file should reflect both a technical mastery of data science methodologies and an ability to communicate findings effectively, catering to both technical and management audiences. Expected time and effort for this task are approximately 30 to 35 hours, ensuring you can balance detail with strategic insight.