Virtual Business Analytics with Python Data Insights Intern

Objective: In this task, you are required to identify a business challenge that can be addressed through data insights and analytics. Your goal is to define the problem clearly, develop hypotheses, and outline a strategic plan that leverages Python-based business analytics techniques.

Task Details: You will begin by researching common issues in the business analytics space. Then, choose a relevant business problem and define its context along with potential hypotheses to investigate. Your submission should include a thorough problem statement, a clear articulation of the impact of the problem, and proposed analytical approaches that could provide solutions.

Key Steps:

1. Conduct initial research on current industry problems related to business analytics.
2. Define a specific business problem and its context in a detailed narrative.
3. Create a set of hypotheses and suggest potential analytics methods using Python to address each hypothesis.
4. Outline a strategic plan that explains how data-driven insights will be employed to solve the problem.

Deliverables: A DOC file that contains: a detailed problem statement, analysis of the problem’s impact, a list of hypotheses along with their justifications, and an actionable strategic plan.

Evaluation Criteria: Your submission will be evaluated on clarity, depth of analysis, logical consistency of hypotheses, feasibility of the strategic plan, and overall presentation. Ensure your document follows a clear structure with headings and well-organized sections. ~ Approximate work time: 30-35 hours.

Objective: This task focuses on the fundamental data preprocessing process in business analytics. You are expected to choose a publicly available dataset, describe its relevance to your selected business problem from Week 1 or another business scenario, and apply data cleaning and transformation techniques using Python concepts.

Task Details: Begin by identifying a publicly accessible dataset that aligns with a relevant business challenge. Describe the dataset’s structure and potential limitations. You are to document the data collection process, identify and handle missing or inconsistent values, and explain the necessary cleaning steps. Although you are not required to use actual code execution, you must outline your approach and reference the Python libraries or techniques you would employ.

Key Steps:

1. Select a publicly available dataset and provide a detailed overview of its contents.
2. Discuss any data quality issues or potential biases present in the dataset.
3. Outline the step-by-step process to clean and preprocess the data, including handling of missing values and outliers.
4. Explain how data transformations can be applied to make the dataset suitable for further analysis.

Deliverables: A DOC file that includes an introduction to your dataset, detailed documentation of data cleaning methods, a summary of transformations, and a discussion on how these steps support robust business analytics.

Evaluation Criteria: The task will be assessed on the clarity and thoroughness of the data description, appropriateness of cleaning techniques, and the logical flow of the document. Your explanation must be comprehensive and reflect deep understanding of data preprocessing concepts. ~ Approximate work time: 30-35 hours.

Objective: In this task, you will perform an Exploratory Data Analysis (EDA) plan for your chosen dataset, focusing on uncovering data patterns, trends, and potential anomalies. The task emphasizes designing a visualization strategy using Python data visualization libraries, explaining how these insights support business decisions.

Task Details: You are to create a comprehensive EDA report. This report should include an explanation of the techniques you would use to analyze the dataset, a conceptual layout for creating charts and graphs, and interpretations of potential visual insights. Though you are not required to generate the actual visualizations, you must specify what type of charts (e.g., bar charts, scatter plots, histograms) could be used at each step, and provide detailed rationale for each choice.

Key Steps:

1. Explain the importance of EDA in understanding data before detailed analysis.
2. Outline the different statistical and visualization techniques suitable for your dataset.
3. Propose a series of steps detailing which attributes of the data will be explored using specific plots and charts.
4. Discuss how each selected visualization contributes to extracting actionable business insights.

Deliverables: A DOC file that outlines your EDA approach in detail. It should include a description of the dataset, a structured plan for generating visualizations, and interpretations on how the results could inform strategic business decisions.

Evaluation Criteria: Your submission will be evaluated on the depth and clarity of the EDA strategy, the justification of chosen visualization methods, structure, and overall coherence of the document. Ensure a logical flow and use HTML-like structure (headings, bullet points) within your DOC narrative. ~ Approximate work time: 30-35 hours.

Objective: This task is designed to simulate building a predictive model to address a business problem using Python. You will outline and justify the approach, technique, and evaluation metrics you would use when implementing a predictive model, including both regression or classification scenarios as may be applicable to your dataset.

Task Details: Develop a detailed plan that covers the key aspects of building a predictive model. Start with selecting the appropriate modeling technique based on your business problem. Discuss data partitioning into training and testing sets, detail methods to handle potential overfitting, and specify which Python libraries (e.g., scikit-learn) and techniques would be applied during each step. You need to include algorithm selection, parameter tuning, and performance evaluation metrics in your document.

Key Steps:

1. Define the business objective that necessitates predictive analytics, linking it to the problem you identified.
2. Select a potential predictive modeling technique and justify its suitability.
3. Outline the steps for data partitioning and model training, discussing how you would address issues such as bias and overfitting.
4. Describe the evaluation metrics and validation strategy that will determine model performance.
5. Provide a discussion on post-model deployment considerations, including how to interpret model insights for business decisions.

Deliverables: A DOC file that presents a comprehensive predictive modeling plan. The document should include rationalizations for chosen methods, detailed process documentation of model building, and a strategy for interpreting model outcomes to deliver valuable business insights.

Evaluation Criteria: The plan will be reviewed based on the logical flow of the methodology, the depth of explanation for each modeling step, clarity of performance evaluation, and overall completeness of the proposed model building process. ~ Approximate work time: 30-35 hours.

Objective: This final task simulates preparing a comprehensive final report that integrates your analysis, insights, and predictive modeling results to drive strategic business decision-making. Your primary output will be a detailed decision-support document that synthesizes your findings into actionable recommendations.

Task Details: You will create a DOC file report that summarizes all the tasks completed in the previous weeks. The report should be structured into distinct sections including an executive summary, methodology overview, key findings from the EDA and predictive modeling phases, and final recommendations. Furthermore, you need to discuss potential challenges, risks, and suggested mitigative strategies based on your analysis. The report should be well-organized, with clear headings, subheadings, and bullet points where necessary, to promote readability and ease of understanding.

Key Steps:

1. Begin with an executive summary that highlights the key insights and overall recommendations derived from your work.
2. Document the methodology used in each phase of the internship, referencing your initial problem definition, data cleaning, EDA, and predictive modeling approaches.
3. Analyze the outcomes of your analytical process, discuss how the insights align with business objectives, and elaborate on the strategic implications.
4. Conclude with actionable recommendations, including potential next steps for further business analytics initiatives.

Deliverables: A DOC file that serves as a final comprehensive report. This document should present all your analyses, integrate visualized insights (conceptually if not actual images), and provide clear, data-driven recommendations to support strategic business decisions.

Evaluation Criteria: Submissions will be evaluated on the clarity and organization of the report, the depth of strategic insights, the logical connection between data analysis and recommendations, and the overall quality of the document presentation. Emphasis will be placed on the ability to synthesize technical findings into meaningful business insights. ~ Approximate work time: 30-35 hours.