Tasks and Duties
Task Objective
The objective of this week is to focus on data cleaning, preprocessing, and preliminary analysis using R. You will work with a publicly available dataset of your choice, perform data cleaning, handle missing values, and generate summary statistics and initial insights. This task is designed to take approximately 30 to 35 hours of work.
Expected Deliverables
- A comprehensive DOC file that includes an overview of your chosen dataset, data cleaning process, transformation steps, and initial analysis results.
- R code snippets and outputs embedded in the document.
- Screenshots or visual aids explaining key steps.
Key Steps
- Dataset Selection: Identify and download a publicly available dataset relevant to data analysis. Ensure the dataset has complexities such as missing values and a mix of categorical and numerical variables.
- Data Cleaning Process: Describe your method for cleaning the dataset. This must include handling missing values, outlier detection, normalization, and encoding categorical variables using R.
- Exploratory Analysis: Perform exploratory analysis using R functions such as summary(), str(), and various visualization techniques. Document descriptive statistics, correlations, and relevant initial insights.
- Documentation: Organize the analysis in a DOC file with detailed explanations, R code, and output evidence. Ensure the final document is well structured and professional.
Evaluation Criteria
- Clarity and comprehensiveness of the data cleaning methodology.
- Depth of exploratory analysis and rationalization for chosen methods.
- Organization and readability of the DOC file submission.
- Correct use of R coding practices and documentation of code outputs.
Task Objective
This week's task focuses on creating dynamic and informative visualizations using R. You will be expected to select a publicly available dataset (or continue with the one from Week 1 if desired) and use R visualization libraries such as ggplot2, lattice, or base plotting systems to communicate key insights through graphs and charts. The work should span 30 to 35 hours.
Expected Deliverables
- A DOC file that includes a clear narrative explaining the significance of each visualization.
- Detailed descriptions of the visualizations created with embedded R code and outputs.
- High-quality images of graphs/charts in the document along with interpretation of the analysis.
Key Steps
- Data Overview: Briefly summarize the dataset selected for visualization and define key variables.
- Visualization Design: Develop multiple visualizations to showcase trends, patterns, and anomalies. Use various chart types such as bar graphs, scatter plots, histograms, and line charts to effectively represent the data.
- Code and Interpretation: Embed R code that generates the visualizations. Explain each visualization, detailing what insights were gained and why each graph was selected.
- Documentation: Ensure the final DOC file submission is structured, with clear headings, descriptions, and visuals that assist a non-technical audience in understanding your analysis.
Evaluation Criteria
- Creativity and relevance of chosen visualizations.
- Clarity of explanations and narrative in the DOC file.
- Technical accuracy and quality of the R code used.
- Effectiveness in communicating key insights drawn from the data.
Task Objective
This task is centered on performing in-depth statistical analysis and developing a predictive model using R. You will be required to choose a publicly available dataset, perform hypothesis testing, and apply regression or classification models to predict outcomes. This exercise is tailored to utilize 30 to 35 hours of focused work.
Expected Deliverables
- A DOC file detailing your approach to statistical analysis and model building.
- Screenshots of R code, outputs, model diagnostics, and summary statistics.
- A summary interpretation of the model’s performance, including potential improvements.
Key Steps
- Dataset Identification: Select a dataset that is suitable for predictive modeling. Include a brief rationale for choosing your dataset.
- Exploratory Statistical Analysis: Conduct hypothesis testing and analyze data distribution. Use relevant R functions to test for correlations, normality, and other assumptions.
- Model Building: Develop either a regression or classification model in R. Include steps to split the data using cross-validation and assess model performance metrics.
- Diagnostic Analysis & Optimization: Present model diagnostics such as residual plots or confusion matrices. Provide insights into the model’s strengths and identify potential areas for improvement.
- Documentation: Prepare a DOC file that documents the entire process from hypothesis formulation to model evaluation, including comprehensive explanations and R code excerpts.
Evaluation Criteria
- Comprehensiveness and clarity of the statistical analysis performed.
- Technical rigor in implementing the predictive model.
- Quality of documentation and clarity of presenting analytical insights.
- Overall coherence and justification of methods used in the analysis.
Task Objective
This final week’s task requires you to create a comprehensive report that integrates all previous analyses into a cohesive presentation. You will consolidate your data cleaning, visualization, and modeling work into one detailed DOC file. Beyond summarizing your processes, this task emphasizes your ability to communicate data-driven insights effectively to a diverse audience. Dedicate approximately 30 to 35 hours to produce a polished, articulate report.
Expected Deliverables
- A detailed DOC file that serves as a final report including introduction, methodology, analysis, and impacts.
- Sections that showcase data cleaning, visualization, and predictive modeling results, with annotated R code, output snapshots, and graphical visuals.
- A conclusion section that outlines lessons learned, challenges overcome, and potential future directions.
Key Steps
- Outline and Structure: Create a structured outline with sections such as Introduction, Data Preparation, Analysis, Results, Discussion, and Conclusion. Ensure that each section transitions smoothly into the next.
- Integration of Previous Work: Summarize and integrate work from the previous three weeks. Include descriptions of data cleaning methodology, key visualizations, and the predictive model’s insights.
- Detailed Analysis & Discussion: Provide a detailed narrative for each section, explaining the statistical significance, patterns observed, and business implications of your analysis.
- Visual Aid Integration: Embed images, graphs, and code snippets to visually support your findings.
- Final Presentation: Write a comprehensive summary that underlines the importance of a data-driven approach, the challenges encountered during analysis, and recommendations for further analysis.
Evaluation Criteria
- Overall cohesion and clarity of the comprehensive report.
- Depth of analysis with critical insights and well-supported conclusions.
- Effective integration of technical R code with narrative explanations.
- Professional presentation, formatting, and adherence to the DOC file submission format.