Tasks and Duties
Task Objective
This task requires you to adopt the role of a Data Science Consultant by identifying a critical business problem that can be addressed using data science techniques. You will articulate the problem, its potential business impact, and propose a preliminary strategic plan to tackle it using Python-based tools. The main goal is to demonstrate your understanding of the data science lifecycle and develop a clear roadmap for solving a business issue.
Expected Deliverable
Submit a DOC file that includes a detailed report. Your report should contain the problem definition, strategic planning, literature review, and a preliminary project plan. The document should be well-structured and organized.
Key Steps to Complete the Task
- Identify a real-world business challenge that could benefit from data analytics and machine learning techniques. Explain why this problem is significant.
- Conduct a literature review of similar challenges and solutions in the public domain. Briefly summarize relevant methodologies and technologies.
- Develop a strategic plan outlining how you would use Python-based data science techniques to solve the identified problem. Include methodologies for data collection, pre-processing, modeling, and evaluation.
- Create a timeline and resource plan that covers approximately 30-35 hours of work including research, planning, and document preparation.
- Write a comprehensive report in a DOC file detailing your findings, the problem context, your proposed strategy and project plan.
Evaluation Criteria
Your submission will be evaluated based on clarity of problem definition, depth of analysis in the literature review, completeness and feasibility of the strategic plan, and overall quality of writing and organization of your DOC file.
Task Objective
This task focuses on the early stages of the data science process which include sourcing, cleaning, and performing exploratory data analysis (EDA). Your role as a Data Science Consultant will involve selecting a publicly available dataset (or simulated data if needed) and documenting your process of acquiring the data, cleaning it, and uncovering initial insights. The objective is to demonstrate your technical skills in manipulating and analyzing data with Python.
Expected Deliverable
Create and submit a DOC file that comprises a detailed explanation of your data source selection, preprocessing methods, and EDA steps. Include code snippets (if required, paste them as text) along with visualizations that help elucidate your findings.
Key Steps to Complete the Task
- Identify and select a publicly available dataset relevant to your chosen business problem or another topic of significance in data science.
- Describe the process of data acquisition including how and why you selected the dataset.
- Perform data cleaning and preprocessing. Document any missing values, anomalies detected, and how you resolved these issues.
- Conduct exploratory data analysis using Python. Use visualizations and descriptive statistics to highlight important trends and potential hypotheses.
- Draft a report (DOC file) that details every step of your process along with clear explanations of your findings.
Evaluation Criteria
Submissions will be judged based on the clarity of your data acquisition narrative, thoroughness in data cleaning and analysis, quality of visualizations, and the overall organization and insightfulness presented in your DOC file analysis report.
Task Objective
The focus of this week is on designing and implementing a machine learning model using Python. As a Data Science Consultant, you need to show how to structure, train, and validate predictive models. You will demonstrate your ability to transition from EDA to model building, incorporating theoretical knowledge with practical application.
Expected Deliverable
Submit a DOC file that contains detailed documentation of your model design and implementation. Your report should include the rationale for choosing the specific model, details of the dataset used (referencing public data if applicable), preprocessing steps, parameter tuning, and validation metrics.
Key Steps to Complete the Task
- Select a suitable machine learning algorithm for the problem at hand (e.g., regression, classification, clustering, etc.). Explain why this algorithm is appropriate.
- Detail your model design including feature selection, data splits (training and testing), and any cross-validation procedures applied.
- Implement the model using Python libraries such as scikit-learn, TensorFlow, or PyTorch. Document your process with pseudo-code or actual code segments as necessary.
- Discuss the model evaluation techniques you applied and analyze the results of your model’s performance.
- Compile your findings, including challenges encountered and how you overcame them, into a DOC file report.
Evaluation Criteria
Assessment will be based on the clarity of your model rationale, implementation details, practical application of Python coding practices, the effectiveness of model evaluation, and the comprehensive nature of your DOC file report.
Task Objective
This week emphasizes the importance of data visualization in communicating insights to both technical and non-technical stakeholders. Your task is to create a series of compelling visualizations that tell a story about the data analysis performed in previous weeks. As a Data Science Consultant, you must be able to translate complex data analyses into clear, actionable insights.
Expected Deliverable
Prepare and submit a DOC file that documents your visualization strategy and showcases various charts, graphs, and dashboards developed using Python visualization libraries such as Matplotlib, Seaborn, or Plotly. The report should include a description of the story each visualization tells and how it relates to the broader business objectives.
Key Steps to Complete the Task
- Review your prior analyses and select key metrics or trends that are most impactful and relevant for business decision-making.
- Design a series of visualizations that communicate these results clearly. Provide explanations for each visualization and how they contribute to the overall narrative.
- Utilize Python-based libraries to create your visualizations. Include detailed steps or code snippets (as text) that justify your choices of plotting techniques.
- Develop a narrative for your visualizations, focusing on explaining the implications of your findings in a business context.
- Document your entire journey, including planning, design, and reflective commentary in a DOC file.
Evaluation Criteria
Your submission will be evaluated on the creativity and clarity of your visualizations, the coherence of the narrative linking visuals to business insights, technical execution using Python libraries, and the overall completeness and quality of the DOC file report.
Task Objective
The final week is dedicated to critically evaluating the performance of your machine learning model and proposing optimization strategies that can enhance its outcomes. In this task, you are to step into the role of a Data Science Consultant who not only builds models but also assesses them rigorously. The objective is to explore different evaluation metrics, identify bottlenecks in your current approach, and offer recommendations for further improvements and scalability using Python tools.
Expected Deliverable
Submit a DOC file that offers a detailed report on your model evaluation process, optimization techniques applied, and strategic recommendations for future iterations. The report should cover the evaluation metrics used, analysis of results, error analysis, and potential strategies for model improvement, including tuning parameters and considering alternative algorithms.
Key Steps to Complete the Task
- Review the performance of your model using various evaluation metrics such as accuracy, precision, recall, F1-score, ROC curves, or relevant regression metrics, depending on your model type.
- Identify potential areas for model improvement by conducting error analysis and noting where predictions deviate from expected outcomes.
- Propose at least two optimization strategies. These could include hyperparameter tuning, feature engineering, or exploring advanced ensemble methods.
- Discuss the practical implications of your optimizations in terms of business impact and scalability.
- Document all findings, optimizations, and recommendations in a detailed, well-organized DOC file.
Evaluation Criteria
Submissions will be assessed based on the thoroughness of the model evaluation, clarity in presenting optimization strategies, depth and feasibility of the strategic recommendations, and quality and organization of the written DOC file.