What's included

• Data Collection and Cleaning:

  Gather relevant data from various sources. Clean and preprocess the data to ensure accuracy and consistency.

• Exploratory Data Analysis (EDA):

  Perform exploratory data analysis to understand the dataset. Identify patterns, trends, and outliers in the data.

• Descriptive Statistics:

  Provide summary statistics and key metrics for the dataset. Present data distributions and central tendencies.

• Data Visualization:

  Create visualizations (charts, graphs, etc.) to effectively communicate insights. Illustrate trends, correlations, and patterns for non-technical stakeholders.

• Hypothesis Testing:

  Formulate hypotheses related to the project objectives. Conduct statistical tests to validate or reject the hypotheses.

• Model Development (if applicable):

  Build predictive models or machine learning algorithms if part of the project. Train and evaluate models based on relevant metrics

• Insights and Recommendations:

  Summarize key findings from the analysis. Provide actionable recommendations based on the insights

• Documentation:

  Document the entire analysis process, including methodologies and assumptions. Prepare a clear and concise report for stakeholders.

• Presentation:

  Deliver a presentation to stakeholders, explaining the analysis, findings, and recommendations. Address any questions or concerns raised by the audience.

• Feedback and Iteration:

  Incorporate feedback from stakeholders into the analysis. Iterate on the analysis if needed based on additional insights or requirements.

• Code Repository (if applicable):

  Share well-documented code in a version-controlled repository for future reference.