Virtual Data Science with R Apprentice Intern

Objective: Develop a comprehensive project plan for a data science analysis project using R. This task requires you to plan every phase of a project including goal setting, timeline, roles, and resource allocation, ensuring a clear project roadmap.

Expected Deliverables: A DOC file containing a detailed project plan and strategy document. The document should cover the project objectives, scope, timeline, methodology, and risk management plan.

Key Steps:

1. Define the Project Objective: Start by clearly outlining what you intend to achieve from the project. Identify the problem you want to solve or the question you want to answer using data science techniques in R.
2. Research Background: Provide context and rationale for selecting the project topic. Include an overview of relevant literature or previous studies.
3. Plan the Project Phases: Break down the project lifecycle into phases such as data collection (if applicable), data cleaning, analysis, model development, and reporting. Include estimated timelines for each phase.
4. Assign Roles and Responsibilities: Even if you are working alone, define the roles you will assume (analyst, programmer, project manager) and outline tasks accordingly.
5. Risk Management: Identify potential risks and propose mitigation strategies.

Evaluation Criteria: Your submission will be evaluated on clarity, comprehensiveness, organization, and the practicality of the plan. Ensure that your document is well-structured and logically organized. Use tables, bullet lists, and diagrams where applicable to enhance clarity. The task should be thorough and demonstrate your understanding of project planning in a data science context using R. This task is expected to take approximately 30 to 35 hours of work.

Objective: Conduct an exploratory data analysis (EDA) and create a series of visualizations using R. The goal is to uncover patterns, trends, and insights from publicly available datasets while demonstrating your ability to manipulate, analyze, and visualize data.

Expected Deliverables: A DOC file that includes a detailed explanation of your exploratory data analysis process using R, accompanied by screenshots or snippets of code and visualizations. Each visualization should be explained in context.

Key Steps:

1. Select a Public Dataset: Choose a dataset that is publicly available. Provide a brief description of the dataset and why you selected it.
2. Data Cleaning and Preparation: Document the steps you took to clean and prepare the dataset for analysis. Explain any transformations or handling of missing values.
3. Exploratory Analysis: Utilize R to compute summary statistics and perform exploratory analysis. Highlight key trends, distributions, and any anomalies in the data.
4. Visualization Creation: Create at least three different types of visualizations (e.g., histograms, scatter plots, box plots, heat maps) using R libraries. Include code snippets and visual outputs in your document.
5. Insight Interpretation: Discuss what each visualization reveals about the dataset and how these insights can guide further analysis.

Evaluation Criteria: Your submission will be assessed based on analytical depth, clarity of explanation, quality of visualizations, and the relevance of your interpretations. The document should be detailed, clearly organized, and reflective of approximately 30 to 35 hours of dedicated work.

Objective: Focus on data wrangling and preprocessing using R. This task is designed to enhance your skills in cleaning, transforming, and preparing data for advanced analysis. Emphasize reproducible code and meticulous documentation of each step.

Expected Deliverables: A DOC file containing a comprehensive report of your data wrangling process. The document should include code snippets, explanations for each preprocessing step, and before-and-after snapshots of the dataset to illustrate the changes made during cleaning and transformation.

Key Steps:

1. Dataset Selection and Introduction: Choose a publicly available dataset. Provide a brief introduction to the dataset including its source and key attributes.
2. Data Quality Assessment: Identify and document issues such as missing values, outliers, and inconsistencies within the dataset.
3. Data Cleaning Techniques: Apply various data cleaning techniques using R (e.g., handling missing values, correcting data types, normalization). Provide detailed code examples and explanations.
4. Data Transformation: Implement data transformation processes such as feature extraction, encoding categorical variables, and data scaling. Document the rationale behind each transformation.
5. Documentation and Reproducibility: Explain how your work can be reproduced by someone else by outlining the necessary packages and steps in a structured manner.

Evaluation Criteria: Submissions will be evaluated on the effectiveness and clarity of data preprocessing steps, the reproducibility of code, and the thoroughness of your documentation. The task should reflect a deep understanding of data wrangling using R and demonstrate approximately 30 to 35 hours of effort.

Objective: Develop and validate predictive models using R. For this task, you will build at least two different models to solve a problem defined by a publicly available dataset. The focus should be on selecting appropriate modeling techniques, training the models, and evaluating their performance.

Expected Deliverables: A DOC file that details your approach to model building, including the rationale behind model selection, the methodology used for training, and an evaluation of the models’ performance. Include clear code excerpts, performance metrics, and visualizations such as ROC curves or model diagnostics.

Key Steps:

1. Problem Definition: Clearly define a predictive problem using a publicly available dataset. Provide background and context.
2. Model Selection: Choose at least two different modeling techniques (e.g., linear regression, decision trees, random forest). Explain the strengths and weaknesses of each method in relation to your problem.
3. Model Training and Validation: Train your models using R and validate them using appropriate metrics (e.g., accuracy, precision, recall, AUC). Include code snippets and explanations for splitting data, training, and testing phases.
4. Performance Evaluation: Analyze and compare the performance of each model through visualizations and statistical metrics. Discuss any observed biases or limitations.
5. Future Recommendations: Suggest potential improvements or next steps for further refinement of the models.

Evaluation Criteria: Your submission will be judged on the clarity of the model building process, the justification of modeling choices, the thoroughness of validation, and overall presentation. Demonstrate your analytical insight and deep expertise in R over an estimated work period of 30 to 35 hours.

Objective: Synthesize your data science work into a cohesive report that highlights key insights, actionable recommendations, and future directions based on your analysis using R. This task emphasizes the importance of communication and documentation in data science practices.

Expected Deliverables: A DOC file presenting your final project report. The document should integrate analysis results, visualizations, code snippets and a well-articulated narrative explaining your findings, methodology, and conclusions.

Key Steps:

1. Executive Summary: Begin your report with an executive summary that provides a high-level overview of your project scope, objectives, and primary findings.
2. Methodology and Analysis: Detail your approach, from data acquisition and cleaning to modeling and evaluation. Include key code snippets and visual outputs to support your narrative.
3. Results and Insights: Summarize the results of your analyses. Explain your findings, supporting them with graphical representations like charts and tables created using R. Discuss the significance of your findings in a broader context.
4. Recommendations: Based on your data analysis, provide well-reasoned recommendations for stakeholders. Address potential actions and improvements based on your insights.
5. Reflection on Learnings: Conclude with a section reflecting on the challenges you faced, lessons learned, and how this experience might inform future projects in data science with R.

Evaluation Criteria: Submissions will be evaluated based on the cohesiveness and clarity of the report, the quality and interpretability of visuals, and how well you articulate your insights and recommendations. The report should demonstrate thorough analysis and communication skills expected from 30 to 35 hours of focused work.