Tasks and Duties
Task Objective
Your objective for this week is to design a comprehensive business analytics strategy using Python-based approaches. You are required to develop an analytical plan that identifies target business areas, outlines key performance indicators (KPIs), and establishes a timeline for executing data analysis on publicly available datasets. This task emphasizes planning and strategy formulation, which are crucial for any business analytics role.
Expected Deliverables
- A detailed DOC file report outlining your strategic plan.
- Sections covering rationale behind KPI selection, data sources, timeline, and resource allocation.
- Documentation of your plan’s implementation logic that can later be used for execution.
Key Steps to Complete the Task
- Research publicly available information and data sources relevant to business analytics.
- Define the business problem or opportunity and propose a strategic analytical approach using Python.
- Identify and justify the selection of KPIs and metrics aligned with business goals.
- Draft a step-by-step timeline including milestones, resource needs, and risk assessments.
- Compile your strategy into a well-structured DOC file with clearly labeled sections.
- Include pseudocode or flow diagrams to illustrate your strategy formulation process.
Evaluation Criteria
- Clarity and coherence of the strategic plan.
- Depth of analysis regarding KPI selection and data source justification.
- Logical timeline and clear delineation of steps to be taken.
- Quality and presentation of the DOC file submission (structure, formatting, and detail).
- Correct incorporation of Python-based planning methodologies.
This task is designed to take approximately 30-35 hours to complete. Be sure to include a thorough explanation of your approach so that it stands as a self-contained document without further external references.
Task Objective
This week’s assignment focuses on the critical aspect of data extraction and cleaning, coupled with exploratory data analysis (EDA) using Python. You are to simulate a complete data preparation pipeline which involves sourcing public data, performing cleaning operations, and carrying out preliminary exploratory analysis. The goal here is to ensure data quality and extract insights that inform further analytics processes.
Expected Deliverables
- A DOC file report that details your data cleansing process, including code snippets written in Python.
- An explanation of data extraction techniques, cleaning methods, and methods used to handle missing or inconsistent data.
- Graphical representations (e.g., charts or plots) with interpretations of EDA findings.
Key Steps to Complete the Task
- Identify a publicly available dataset suitable for business analysis and explain your selection.
- Outline the steps you would take to extract and import the data using Python libraries such as Pandas.
- Describe the data cleaning process including treatment of missing data, outlier detection, and normalization procedures.
- Conduct an exploratory analysis using visual tools such as histograms, scatter plots, and correlation matrices, and interpret key findings.
- Document all steps, including snippets of pseudocode or actual code, in a structured DOC file report.
Evaluation Criteria
- Completeness and clarity of the data extraction and cleaning workflow.
- Insightfulness of exploratory analysis and visual interpretation.
- Proper explanation of the methodology and tools used.
- Presentation quality of the DOC file (structure, detail, and formatting).
- Reproducibility of the process based on the provided documentation.
This task should take approximately 30-35 hours to complete, hence ensure each step is elaborated and all procedures are documented comprehensively for clear understanding and reproducibility.
Task Objective
In this task, you are required to delve into advanced data visualization techniques and statistical analysis using Python. Your goal is to transform numerical insights derived from data into compelling visual narratives that support business decision-making. This assignment emphasizes the ability to not only generate graphical representations but also to interpret statistical relationships and trends within the data.
Expected Deliverables
- A comprehensive DOC file report that includes detailed visualizations (e.g., line charts, bar graphs, heatmaps) and their corresponding statistical commentary.
- An explanation of the statistical methods applied (such as regression analysis, hypothesis testing, or correlation analysis) and justification for their use.
- Annotated code excerpts or pseudocode that outline the visualization and analysis process using Python libraries like Matplotlib, Seaborn, or Plotly.
Key Steps to Complete the Task
- Select a publicly available dataset relevant to business analytics and justify your choice.
- Perform statistical analysis to determine key trends and correlations in the dataset.
- Create a series of advanced visualizations using Python, ensuring that each graphic is clearly labeled and data-preprocessing steps are noted.
- Discuss the insights derived from each visualization, linking the findings to potential business impacts.
- Compile your visualizations, code explanations, and analysis into a detailed DOC file report.
Evaluation Criteria
- Depth and clarity of the statistical analysis and its relation to business insights.
- Creativity and effectiveness in presenting data through visualizations.
- Quality of explanations and logical connection between the visualizations and business implications.
- Documentation quality in the DOC file including structure, annotations, and formatting.
- Accuracy and reproducibility of the Python-based visualizations and analyses.
This task is estimated to require 30-35 hours. Ensure that the submission is self-contained and all steps are described thoroughly to illustrate a full-cycle process in advanced data visualization and statistical analysis.
Task Objective
The final week’s assignment is aimed at integrating predictive modeling techniques with performance evaluation strategies using Python. You are tasked to select a public dataset, develop a predictive model, and evaluate its performance. This exercise encapsulates the move from data exploration and visualization to predictive analytics, which is crucial for anticipating business trends and making informed decisions.
Expected Deliverables
- A detailed DOC file report which includes the formulation of the predictive model, the training process, and the evaluation metrics used.
- A clear explanation of data preprocessing steps, feature engineering techniques, and model selection rationale.
- Inclusion of code snippets or pseudocode that document the Python-based implementation of the model using libraries such as Scikit-learn.
- A performance summary including metrics such as accuracy, precision, recall, or RMSE depending on the model type chosen.
Key Steps to Complete the Task
- Select an appropriate publicly available dataset and describe your choice including its relevance to predictive analytics.
- Conduct necessary data preprocessing and feature engineering to prepare the dataset for modeling.
- Build at least one predictive model (e.g., linear regression, decision tree, or clustering model) using Python.
- Evaluate the model performance through relevant metrics and provide a comparative discussion of potential model improvements.
- Document the entire process in a DOC file report with structured sections, including methodology, code documentation, and a results discussion.
Evaluation Criteria
- Clarity and thoroughness in explaining the model development process.
- Appropriateness of data preprocessing and feature engineering methods.
- Correct application of predictive modeling techniques and clear presentation of evaluation metrics.
- Quality of the DOC file report including presentation, structure, and comprehensiveness.
- Ability to critically assess model performance and discuss areas for potential improvement.
This comprehensive task is anticipated to take 30-35 hours. Your final submission should be a self-contained document that provides a complete overview from data selection to model evaluation, demonstrating your proficiency in predictive analytics with Python.