Tasks and Duties
Objective
The goal of this task is to design a comprehensive project plan that outlines the approach and strategy for a data science analysis using Python. You will draft a proposal that includes the project objectives, scope, potential publicly available datasets, and methodologies. This task will help you develop strategic thinking and planning skills crucial for a Python Specialist Intern in the data science field.
Expected Deliverables
- A detailed DOC file containing your project proposal.
- A clear outline of project objectives, goals, and anticipated challenges.
- A thorough description of potential data sources (public datasets) and the rationale behind selecting them.
- A high-level workflow of the analytical process including data acquisition, cleaning, analysis, and reporting.
Key Steps to Complete the Task
- Conduct background research on relevant public datasets and identify one that aligns with an interesting problem statement.
- Define the project objectives and specify the scope of the analysis clearly.
- Outline the steps you will follow from data sourcing to the final analysis.
- Draft a plan that includes potential methodologies, tools (including Python libraries), and checkpoints for validation.
- Organize your document with clear headings, bullet points, and diagrams or flowcharts if necessary.
Evaluation Criteria
Your submission will be evaluated based on clarity and organization, completeness of the proposal, quality of research in dataset selection, logical flow of the plan, and adherence to the DOC file format. In addition, creativity and critical thinking in designing a realistic and innovative project approach will be considered.
This task is estimated to require approximately 30 to 35 hours of work. Allocate time for research, draft creation, and revisions to ensure a comprehensive submission.
Objective
This assignment focuses on the critical step of data cleaning and transformation within the data science workflow. You are to simulate a data wrangling process by drafting a detailed plan that explains how you would handle data inconsistencies, missing values, outliers, and data normalization using Python. The emphasis is on planning and documentation, ensuring you understand how to prepare raw data for analysis.
Expected Deliverables
- A DOC file that thoroughly documents your data cleaning and transformation strategy.
- An explanation of the techniques and Python libraries (such as pandas, numpy) used for each step.
- A detailed plan for managing common data issues including duplicates, missing values, and erroneous data points.
- An outline of the transformation process, highlighting scaling, normalization, and feature engineering strategies.
Key Steps to Complete the Task
- Research best practices in data cleaning and transformation, focusing on Python-based solutions.
- Create a structure for your document that includes an introduction to data quality issues, followed by detailed cleaning procedures.
- Discuss potential challenges in data processing and provide pre-emptive solutions or methods to handle them.
- Include pseudo-code or flowcharts to visually represent the cleaning and transformation process.
- Review and revise your document ensuring clarity and logical progression in your explanations.
Evaluation Criteria
Your submission will be assessed based on the thoroughness of your documentation, the clarity of explanations, the appropriateness of the chosen techniques, and the structure of the DOC file. Depth of research and the ability to predict and mitigate common data issues will also be key factors.
The estimated time to complete this task is around 30 to 35 hours, including research, drafting, and final reviews.
Objective
The purpose of this task is to conceptualize an exploratory data analysis (EDA) process that will include generating visualizations using Python. You are expected to describe an EDA strategy which involves examining data distributions, identifying patterns and anomalies, and generating insights through visual representations. This task will strengthen your ability to communicate complex data insights in a clear, concise manner.
Expected Deliverables
- A DOC file outlining your EDA and visualization strategy in detail.
- An explanation of the types of plots and graphs (such as histograms, box plots, scatter plots) you plan to use, including the Python libraries (e.g. matplotlib, seaborn) that will support these visualizations.
- Discussion on how each visualization aligns with the objectives of uncovering insights and potential action points.
- A plan for documenting findings and interpreting the results from the visual analysis.
Key Steps to Complete the Task
- Review standard approaches to EDA, specifically focusing on techniques relevant to Python.
- Draft an introduction explaining the importance of EDA in data science projects and identifying expected outcomes.
- Detail the types of visualizations you intend to learn and use, explaining their relevance to different data scenarios.
- Outline a step-by-step guide for implementing these visualizations, including data preparation steps.
- Include mock-up diagrams or pseudo-visualizations if needed to enhance your explanation.
Evaluation Criteria
Your task will be evaluated based on the clarity and comprehensiveness of your EDA plan, the logical connection between the selected visualization tools and the data characteristics, and the detailed explanation of each step. A well-structured document, free from ambiguity, and demonstrating deep insight into data visualization strategies will score higher. Ensure the DOC file is easy to navigate, with section headers and bullet points where applicable.
This work is expected to take between 30 to 35 hours, allowing you to conduct extensive research and thorough planning.
Objective
This task involves creating a strategic plan for selecting and evaluating machine learning models using Python. Your role involves understanding different types of machine learning models and articulating why a particular approach is most suited to a given type of data problem. You will develop a comprehensive document that outlines the criteria for model selection, the evaluation metrics to be used, and the process for testing the models, all in a structured plan that can guide the execution phase of a data science project.
Expected Deliverables
- A detailed DOC file outlining your machine learning model selection and evaluation plan.
- An in-depth discussion of various machine learning approaches (e.g. linear regression, decision trees, neural networks) and the criteria for choosing one.
- A well-defined set of evaluation metrics (accuracy, precision, recall, F1 score, etc.) and the rationale behind each metric.
- A flowchart or process diagram that visually represents the process from model selection to evaluation and potential model tuning.
Key Steps to Complete the Task
- Review literature and best practices on machine learning model selection and performance evaluation, with emphasis on Python implementations.
- Write an introduction that discusses the diversity of ML models available and the factors influencing model choice.
- Develop a clear structure that outlines the steps and decision criteria for model selection.
- Describe in detail the evaluation metrics and deconstruct each metric’s role in assessing model performance.
- Outline a strategy for model testing and validation, including any cross-validation techniques you expect to use.
Evaluation Criteria
You will be evaluated on the systematic approach taken in the plan, the depth of technical understanding shown, and the clarity in the articulation of criteria and processes. The DOC file must be well-organized, logically coherent, and should reflect critical thinking regarding the strengths and weaknesses of each model. The evaluation will also consider the practical applicability of your plan in a real-world scenario, as well as the professionalism and detail in the documentation.
It is recommended to allocate around 30 to 35 hours for researching, drafting, and revising the plan to meet the required standards.