Tasks and Duties
Task Objective
Your objective for Week 1 is to conduct an in-depth exploratory data analysis (EDA) using Python. This task requires you to select any publicly available dataset that you find interesting and relevant to a business scenario. You will perform data cleaning, generate descriptive statistics, and create visualizations that reveal key patterns and insights. The final deliverable will be a comprehensive DOC file report presenting your approach, findings, and conclusions.
Expected Deliverables
- A DOC file containing a detailed report.
- Clear explanations of the data cleaning process, handling missing values, and outlier treatment.
- Descriptive statistics including means, medians, variances, etc.
- Multiple visualizations (histograms, scatter plots, box plots) that support your analysis.
- Interpretation of trends, anomalies, and significant findings relevant to business metrics.
Key Steps
- Select a publicly available dataset from platforms such as Kaggle or UCI Machine Learning Repository.
- Clean and preprocess the data to ensure reliability.
- Generate descriptive statistics and perform initial investigations into the data.
- Create various plots and visualizations using Python libraries like matplotlib and seaborn.
- Document your methodology, analyses, and the insights discovered.
- Conclude with recommendations based on your findings.
Evaluation Criteria
The report will be evaluated based on clarity, technical accuracy, depth of analysis, creativity in visualizations, and the ability to communicate business insights effectively. Ensure your DOC file is well-organized and contains all required sections.
Task Objective
In Week 2, you are tasked with building and evaluating a predictive model that forecasts a business-related metric. You will utilize Python for developing your model. Choose any publicly available dataset that contains time-series or sequential data pertinent to business forecast problems. Your final submission should be a DOC file containing a full report on your model's development, performance evaluation, and strategic recommendations for decision-makers.
Expected Deliverables
- A detailed DOC file report that covers all aspects of predictive modeling work.
- Description of the dataset and justification for its choice.
- Step-by-step process including data preprocessing, feature engineering, and model selection.
- Explanation of the modeling technique(s) used (e.g., linear regression, ARIMA, or machine learning algorithms).
- Visualizations comparing predictions vs. actual values.
- Analysis of model performance with metrics such as RMSE, MAE, etc., along with recommendations for future improvements.
Key Steps
- Select and obtain a suitable publicly available dataset.
- Clean the data and perform feature engineering to extract meaningful variables.
- Choose an appropriate predictive modeling technique and build the model using Python libraries such as scikit-learn or statsmodels.
- Evaluate the model performance with relevant evaluation metrics.
- Create visualizations to clearly present your model's accuracy and forecasting ability.
- Document your entire process and provide business recommendations based on your findings.
Evaluation Criteria
You will be evaluated on the clarity of your modeling process, thoroughness of data exploration, justification for technique selection, the accuracy of predictions, and the quality of business insights provided in your DOC file report.
Task Objective
Your goal for Week 3 is to integrate business strategy with data analytics insights. In this task, you will investigate how data analytics can inform and improve an organization’s business strategy. Select a theme relevant to business decision-making such as market segmentation, customer churn, or financial performance; again, you may use publicly available data for this exercise. Your DOC file submission should contain a robust analysis linking business strategy with data insights, highlighting actionable recommendations that can drive business improvement.
Expected Deliverables
- A well-organized DOC file report.
- A clear explanation of your chosen business strategy topic and its relevance.
- A detailed analysis using Python to process data and generate insights.
- Visual aids such as charts, graphs, and tables that clarify your findings.
- A section on actionable recommendations for business strategy based on your analysis.
- Justification of the chosen approaches and discussion on limitations, if any.
Key Steps
- Define your business strategy topic and research its importance in a real-world context.
- Obtain relevant data from public repositories.
- Perform data cleaning and statistical analysis using Python.
- Create visualizations that effectively support your analytical narrative.
- Draft the report in a clear, structured DOC file that begins with an introduction, moves through analysis, and ends with recommendations.
- Conclude with a discussion on potential future data analytics applications in strategic planning.
Evaluation Criteria
The evaluation will focus on the depth of your business understanding, the rigor of your data analytic methods, the quality and clarity of your visualizations, and your ability to translate insights into practical strategic recommendations.
Task Objective
For Week 4, you are required to conduct an evaluation of a hypothetical business initiative using a comprehensive data-driven approach. This task involves designing a framework in Python for the assessment of business initiatives, including risk analysis, performance metrics, and potential ROI calculations. You will simulate a scenario where your analytical findings will support strategic decisions related to investments or business process improvements. Your final DOC file submission should document the evaluation process, detailed findings, and recommendations.
Expected Deliverables
- A detailed DOC file report documenting your evaluation process.
- Definition of the business initiative and the contextual background of the analysis.
- An explanation of the data cleaning, risk assessment, and metric calculations performed using Python.
- Visualizations such as risk matrices, ROI graphs, and performance dashboards made using Python visualization tools.
- A discussion section that includes insights and strategic recommendations to boost business performance.
- Clear steps, analytical techniques employed, and a conclusion highlighting limitations and potential future improvements.
Key Steps
- Define a hypothetical business initiative and set up the context of its evaluation.
- Select relevant metrics and risk factors to analyze using publicly available data or simulated data that you create.
- Utilize Python to perform data cleaning and run analyses on risk and performance metrics.
- Create visualizations to support your analytical insights.
- Compile a comprehensive DOC file that details every step including methodology, analysis, interpretations, recommendations, and potential improvements.
Evaluation Criteria
Your submission will be assessed on the clarity and comprehensiveness of your evaluation framework, the correct implementation of data analysis techniques in Python, the effectiveness of your visualizations, and the depth of strategic insights and recommendations provided in the DOC file report.