Tasks and Duties
Task Objective
For week 1, your task is to simulate the data preparation process essential for any data science project. You will work on acquiring a publicly available dataset from the internet. The focus is on data cleaning, preprocessing, and exploratory data analysis (EDA) using Python. The final deliverable will be a DOC file containing the complete documentation of your process, code snippets, visualizations, and insights.
Expected Deliverables
- A DOC file summarizing your approach, including the rationale behind your data cleaning methods.
- Detailed code snippets or pseudo-code where applicable.
- At least 3 visualizations that highlight trends, missing values, or correlations within your dataset.
- A brief summary of insights drawn from exploratory analysis.
Key Steps to Complete the Task
- Data Acquisition: Identify a publicly available dataset relevant to a domain of interest. Download or use its API to gather the data.
- Data Cleaning: Handle missing values, remove duplicates, and correct data types using Python libraries such as Pandas. Document every step taken and justify your choices.
- Exploratory Analysis: Employ summary statistics and visualizations (such as histograms, scatter plots, or box plots) to explore the data. Discuss observed patterns and potential areas for further analysis.
- Documentation: Prepare a DOC file that includes your methodology, code segments, visual outputs, and a summary of insights. Ensure that the document is clearly structured with headings and subheadings for readability.
Evaluation Criteria
- Clarity and organization of the DOC file.
- Diligence in data cleaning and exploration methodologies.
- Quality and relevance of visualizations.
- Depth of the analytical insights provided.
- Adherence to the estimated time frame of 30-35 hours.
Task Objective
This week, delve deeper into data visualization and the art of storytelling through data. Your objective is to create comprehensive visual narratives that effectively communicate complex datasets to a non-technical audience. All work must be consolidated into a single DOC file that presents your visualizations along with a detailed explanation of your approach and insights.
Expected Deliverables
- A DOC file finalized as your main submission, including well-organized sections for introduction, methods, visual analyses, and conclusions.
- At least 5 sophisticated visualizations (using libraries such as Matplotlib, Seaborn, or Plotly) that convey different aspects of the data story.
- An explanation of how each visualization enhances the overall narrative.
- A written summary of potential business or scientific implications derived from the insights.
Key Steps to Complete the Task
- Select a Domain or Dataset: Choose a publicly accessible dataset or domain of interest for your analysis.
- Visualization Development: Create a series of visualizations that capture key trends, anomalies, and insights. Experiment with different types of charts to find the best representation for your data.
- Data Storytelling: Annotate each visualization with clear captions and descriptions. Explain the reasoning behind each visual choice and discuss how it contributes to a coherent narrative.
- Documentation: Consolidate your work into a detailed DOC file, ensuring that every visualization is accompanied by context and commentary. Organize the document with clear sections, headings, and a logical flow of ideas.
Evaluation Criteria
- Innovativeness and clarity of the data narrative.
- Effectiveness and clarity of each visualization.
- Detail and justifications provided in the DOC file.
- Overall document structure and readability.
- Compliance with the estimated 30-35 hour workload.
Task Objective
This week, your focus is to perform rigorous statistical analysis and hypothesis testing using Python. The goal is to use statistical methods to validate or refute a formulated hypothesis. You will choose a relevant hypothesis related to a business or scientific problem, work on a publicly available or self-generated dataset, and thoroughly document your analysis in a DOC file.
Expected Deliverables
- A comprehensive DOC file that documents your hypothesis, methodology, analysis, results, and conclusions.
- Detailed statistical tests performed (such as t-tests, chi-square tests, or ANOVA) alongside the Python code used for the analysis.
- Visualizations supporting your test results, such as charts or histograms.
- An interpretation of the statistical results, discussing the validity of your hypothesis.
Key Steps to Complete the Task
- Define a Hypothesis: Identify a clear, testable hypothesis related to a topic of interest, ensuring it has real-world relevance.
- Methodology Design: Outline the statistical methods that will be used to test your hypothesis. Include the design, data requirements, and expected analytical approach.
- Data Analysis: Execute the analysis using Python libraries (such as SciPy, Statsmodels, or Pandas). Interpret the p-values and confidence intervals correctly.
- Result Documentation: Compose a DOC file that includes an introduction to the hypothesis, a description of the data and statistical methods used, visualizations of the results, and a comprehensive conclusion discussing the implications of your findings.
Evaluation Criteria
- Accuracy and thoroughness of statistical analysis.
- Clarity in hypothesis formulation and testing methodology.
- Quality and appropriateness of visualizations.
- Coherence of the final DOC file, including structure and content depth.
- Adherence to the assigned workload of 30-35 hours.
Task Objective
Week 4 centers on the fundamentals of machine learning model development in Python. Your task is to build and validate a basic machine learning model using publicly available or self-generated data. You are expected to document the entire process—from data preprocessing to model selection, training, and evaluation—in a DOC file. This exercise will introduce you to the core principles of model development and assess your ability to critically evaluate model performance.
Expected Deliverables
- A DOC file detailing your approach to model development, including data preparation, algorithm selection, and evaluation metrics.
- Python code snippets or pseudocode demonstrating key steps in your model pipeline.
- At least 2 visualizations that compare model performance metrics (e.g., confusion matrix, ROC curve).
- A critical discussion on potential sources of error and suggestions for model improvement.
Key Steps to Complete the Task
- Data Preparation: Begin with a data cleaning and preprocessing step, ensuring that your dataset is properly formatted for model training.
- Model Selection and Training: Choose a simple machine learning algorithm (e.g., logistic regression, decision trees, or clustering methods) and implement the model using libraries like Scikit-learn. Document your choice of algorithm and justification.
- Evaluation: Assess the model’s performance using appropriate metrics and visualizations. Discuss error rates and the potential impact of overfitting or underfitting.
- Final Report: Compile a DOC file that narrates your process, presents your findings, and assesses the model’s strengths and limitations. Use clear visualizations to support your evaluation.
Evaluation Criteria
- Soundness of the model development process.
- Clarity and detail of the documentation in the DOC file.
- Effectiveness of visualizations in conveying results.
- Insightfulness of the evaluation and discussion on improvements.
- Timely completion within the 30-35 hour timeframe.
Task Objective
In the final week, your task is to integrate all the skills acquired over the previous weeks into a cohesive end-to-end project report. This report should not only detail your technical findings but also articulate clear strategic recommendations for stakeholders based on data insights. You are required to produce a DOC file that acts as a comprehensive project report, complete with executive summaries, visualizations, and actionable insights.
Expected Deliverables
- An exhaustive DOC file that includes an executive summary, methodology, detailed analysis sections, and strategic recommendations.
- Inclusion of various visualizations and code snippets that support your analysis.
- A section on potential business impacts or research outcomes informed by your findings.
- A discussion of limitations and future improvement areas.
Key Steps to Complete the Task
- Project Synthesis: Gather insights and analysis from previous weeks. Identify key findings and patterns that have been discovered during the internship.
- Strategic Analysis: Develop strategic recommendations based on your analysis. Explain how these recommendations can drive improvements or inform decision-making.
- Reporting: Draft a comprehensive report in a DOC file. Ensure the report includes clear sections such as an executive summary, introduction, methodology, results, discussion, and conclusion.
- Visual and Code Documentation: Integrate previous visualizations and code excerpts to substantiate your points. Ensure each element is annotated with descriptive explanations.
Evaluation Criteria
- Depth and clarity of the comprehensive project report.
- Quality of strategic recommendations based on data insights.
- Coherence and effectiveness of the document structure, including an executive summary and detailed sections.
- Integration of prior learning with clear visual and textual evidence.
- Adherence to the prescribed workload within the 30-35 hour period.