Task Objective

The objective of this task is to introduce you to the essential phases of business analytics by having you acquire a publicly available dataset, perform data cleaning, and conduct an initial exploratory analysis using Python. This task is designed to help you understand data inconsistencies, missing values, and outlier detection, which are critical for any business analysis project.

Expected Deliverables

You are expected to submit a DOC file containing a comprehensive report on your chosen dataset. The report should include the data acquisition source, rationale for selection, data cleaning process, descriptive statistics, and initial graphical visualizations (screenshots or embedded images of Python plots). Include sections detailing the approach, challenges faced, and insights gained from the initial analysis.

Key Steps to Complete the Task

1. Dataset Selection: Identify and select a dataset from a reliable public source that interests you from the business analytics domain (e.g., sales data, financial metrics, consumer behavior).
2. Data Cleaning: Write Python scripts to clean the dataset by handling missing values, filtering outliers, and standardizing the data formats. Document the methods used.
3. Exploratory Analysis: Generate summary statistics and visual representations (such as histograms, box plots, and scatter plots) to discern patterns and trends.
4. Compilation and Documentation: Organize your findings into a DOC file report that clearly explains each step, includes code snippets, and presents screenshots of your output.

Evaluation Criteria

Your submission will be evaluated based on the clarity and thoroughness of your report, the correctness and efficiency of your Python code, the quality of the data cleaning process, and the insights drawn from your exploratory analysis. The originality of your dataset choice and the depth of your analytical commentary will also be considered.

This task is expected to take about 30 to 35 hours of work. Make sure your documentation is detailed and self-contained so that any reader can understand your process and conclusions without additional resources.

Task Objective

This task is designed to deepen your understanding of statistical modeling in business analytics. You will develop and implement models such as linear regression or correlation analysis using Python. The focus of this task is to learn how to derive actionable insights from data patterns and visualize these insights effectively to support business decision making.

Expected Deliverables

Submit a DOC file that documents the entire process of building your statistical model. The document should include an introduction to your chosen modeling technique, the rationale behind your approach, detailed steps of model development, and results analysis. Additionally, include visualizations (such as regression plots, scatter plots, residual plots) and sections discussing potential improvements.

Key Steps to Complete the Task

1. Conceptual Framework: Start with a summary of the statistical modeling technique you will use, including theoretical background and relevance to business analytics.
2. Data Preparation: Use the dataset from Week 1 or a different public dataset to prepare the input for modeling. Explain any additional data transformations if needed.
3. Model Implementation: Write Python code to implement the chosen model, ensuring code annotations and comments explain the methodology.
4. Visualization and Interpretation: Create visualizations that clearly depict your model's findings. Interpret the results in clear text and include these analyses in your report.
5. Documentation: Consolidate your steps, code snippets, outputs, and insights into a well-organized DOC file report.

Evaluation Criteria

Submissions will be assessed on the accuracy and appropriateness of the statistical methods applied, the clarity of your code and documentation, and the effectiveness of your visualizations in conveying insights. The depth of analysis and critical thinking demonstrated in interpretation and conclusion will be key indicators of success. This task should be approached as a total commitment of approximately 30 to 35 hours.

Task Objective

In Week 3, you will transition from descriptive analytics to predictive modeling. This task requires you to apply machine learning algorithms using Python to predict key business parameters such as sales forecasts, customer churn, or other performance indicators. You will also explore feature selection and model evaluation techniques to ensure robust predictions.

Expected Deliverables

Your final DOC file submission should include a detailed report outlining your prediction model. The report must describe the problem statement, dataset overview, feature engineering, model selection, performance evaluation metrics (such as RMSE, accuracy, or R-squared), and a critical analysis of your results. Embed relevant code snippets and images of model performance plots.

Key Steps to Complete the Task

1. Problem Framing: Clearly define a predictive analytics problem using a public business dataset. Explain the business relevance of your prediction objective.
2. Data Preparation and Feature Engineering: Preprocess the data as required and perform feature selection. Document any transformation or engineering step thoroughly.
3. Model Selection and Implementation: Implement at least one predictive model (e.g., decision tree, linear regression, or random forest) in Python. Ensure that you compare at least two different models to determine the best performer.
4. Evaluation and Visualization: Evaluate models using appropriate metrics and generate visualizations that help compare model performance.
5. Documentation: Create a comprehensive DOC file that consolidates the entire process: problem definition, methodology, code detailing, results, and your concluding observations.

Evaluation Criteria

Your work will be evaluated based on the appropriateness of the machine learning methods chosen, thoroughness in documentation including code clarity, the robustness of model evaluation, and the quality of insights derived. The creativity in addressing potential shortcomings and the thoughtful interpretation of your predictive outcomes will form a significant part of the assessment. Dedicate 30 to 35 hours to ensure a well-polished and complete report.

Task Objective

This task challenges you to connect business analytics with decision-making under uncertainty by creating a simulation model. Your aim is to simulate a business process—such as inventory management, supply chain fluctuations, or market dynamics—using Python. Through simulation, you will analyze different scenarios to assess risks and opportunities, ultimately making recommendations based on the outcomes.

Expected Deliverables

The delivery for this task must be a DOC file report that details your simulation approach. The report should include a description of the business scenario, simulation model design, display of different simulated outcomes with graphical presentations, and a discussion section where you compare various scenarios and explain your decision-making process. Include code excerpts and screenshots of visual outputs.

Key Steps to Complete the Task

1. Define the Scenario: Identify a business situation which is feasible to simulate using Python. Clearly define the context, parameters, and the range of possible outcomes.
2. Model Design and Coding: Develop a simulation model using Python. Incorporate randomness (e.g., Monte Carlo simulation) to simulate real-world variabilities.
3. Scenario Analysis: Run multiple simulation iterations to obtain diverse outcome scenarios. Use visualization techniques to plot the distribution and frequency of outcomes.
4. Decision Analysis: Analyze the simulated data to assess risk factors and identify optimal decision strategies based on the outcomes.
5. Report Documentation: Consolidate all the steps into a DOC file that includes the simulation rationale, methodology, code samples, results, and a final analysis section detailing your business recommendations.

Evaluation Criteria

You will be evaluated on the creativity and clarity of your simulation model, the technical correctness and efficiency of your Python implementation, the effort put into analyzing outcomes, and the logical soundness of your business recommendations. Critical elements include the thorough explanation of your decision analysis process and the clarity in presenting your simulation results. This task is designed to require between 30 to 35 hours of focused work.

Task Objective

In the final week of your internship, you are required to synthesize your analytical skills into an integrated business analytics report. This task involves the creation of a comprehensive DOC file report that not only reviews the findings of your previous tasks but also introduces a dynamic Python-based dashboard that facilitates interactive data visualization. The emphasis is on delivering a coherent narrative that links data acquisition, statistical modeling, prediction, and simulation into actionable business insights.

Expected Deliverables

Your deliverable is a detailed DOC file that should include an executive summary, detailed sections for each previous analysis step, and a section dedicated to the dashboard. Document how you built the dashboard using Python libraries such as Matplotlib, Seaborn, or Plotly. Include screenshots of the live dashboard and instructions on how a user might interact with it, ensuring that the document is self-contained and explainable.

Key Steps to Complete the Task

1. Synthesize Previous Tasks: Begin with an executive summary that ties together your work on data cleaning, statistical analysis, predictive modeling, and simulation.
2. Dashboard Development: Create an interactive dashboard using Python. Explain the selection of visualizations, the interactivity features, and how these elements combine to facilitate decision-making.
3. Integration and Documentation: Write a comprehensive analysis that includes methodology, code excerpts, outputs, graphical visualizations, and insights for each phase of the project.
4. Instruction for Usage: Provide clear instructions and contextual background for a prospective user to navigate and understand the dashboard functionalities.
5. Final Presentation: Ensure that the DOC file is well-organized, with a table of contents, proper section headings, and detailed narrative descriptions of all analyses performed.

Evaluation Criteria

Your final submission will be assessed based on the ability to integrate complex analysis phases into a coherent narrative, the quality and interactivity of the dashboard, and the overall professionalism of your documentation. The clarity, organization, and depth of your final report, as well as the practical utility of the dashboard in a business context, will be critical evaluation points. Prepare to invest between 30 to 35 hours in polishing your final document.