1. Business Problem Solving

Identify clear hypotheses or research questions:

• Multiple relevant business problems and opportunities for improvement should be identified through data analysis.

• Formulate well-defined hypotheses or research questions.

Apply appropriate data analysis techniques:

• Use techniques that match the data and business problem, with clear rationale for their selection.

Provide clear and impactful insights:

• Deliver clear, impactful insights that facilitate decision-making.

• Translate data analysis results into actionable recommendations.

2. Data Preparation

Identify data quality issues:

• Identify most relevant data quality issues with clear explanations of their impact on analysis.

Implement data cleaning/wrangling techniques:

• Consistently apply appropriate data cleaning techniques to all identified data quality issues.

• Handle null values, duplicates, drop unnecessary columns, manipulate strings, and format data.

• Address missing data and fully justify the imputation strategy.

3. Data Analysis

Apply EDA techniques:

• Employ sophisticated EDA techniques to analyze data, validate hypotheses, draw conclusions, and provide unique insights.

• Demonstrate comprehensive understanding of the data’s characteristics, patterns, and relationships.

• Utilize a range of numerical measures and graphical methods according to data type.

Use inferential statistics:

• Utilize inferential statistics such as hypothesis testing with p-values to check for significant correlations and to check for normality.

• Apply appropriate data transformations to ensure normality when necessary.

4. Data Visualization and Communication

Use appropriate data visualization techniques:

• Create interactive and informative visualizations using Python libraries or visualization tools such as Tableau or Power BI.

• Communicate insights effectively with well-designed visualizations.

Develop a clear and effective dashboard:

• Ensure the dashboard is clear, well-organized, visually appealing, and allows for decision-making.

• Define, measure, and plot KPIs and metrics with great attention to detail.

5. Coding and Data Analysis

Develop proficiency in data analysis:

• Apply rigorous methods for predictive analysis using machine learning or descriptive analysis using statistics and SQL.

• Perform data preprocessing, address all issues, and justify the preprocessing steps.

• Apply appropriate machine learning models, ensure model assumptions are checked, and perform hyperparameter tuning.

• Evaluate model performance using appropriate metrics and make necessary adjustments.

Use SQL and Python:

• Use SQL for data manipulation, advanced queries, and combining with Python for comprehensive data analysis.

6. Clean and Modular Code

Write clean, modular, and efficient code:

• Ensure no unused code is present.

• Write functions that are modular and reusable, saved in .py files.

• Apply naming conventions consistently.

• Avoid hard-coded values and global variables, using config files instead.

• Organize files and folders appropriately.

7. Version Control with Git and GitHub

Track changes in the source code:

• Make at least one commit per project day with clear and precise descriptions.

• Use atomic commits and separate branches for development.