Data analysis

Let's break down the use of each tool in my data workflow, from cleaning to visualization.

1. Data Cleaning

• Excel:

  • Use Cases: Best for smaller datasets, quick manual cleaning tasks, and exploring data structure.  Good for one-off cleaning tasks or when you're less comfortable with coding.

• SQL:

  • Use Cases: Ideal for cleaning larger datasets stored in databases. Offers powerful querying and manipulation capabilities.  Good for repeatable cleaning processes.
  • Python:
    • Use Cases: Highly flexible and powerful for complex cleaning tasks, especially with unstructured or semi-structured data.  Excellent for automation and building reusable cleaning scripts.

2. Data Processing

1. SQL:

• Use Cases:  Efficiently transforming and aggregating data within a database.  Ideal for preparing data for analysis or visualization.

2. Python:

• Use Cases:  Flexible and versatile for complex data transformations, especially when dealing with unstructured or semi-structured data.  Great for custom calculations and algorithms.

3. Data Visualization

• Tableau:

  • Use Cases:  Creating interactive dashboards and visualizations for exploring and communicating insights.  Excellent for ad-hoc analysis and presenting findings to a wider audience.

• Power BI:

  • Use Cases: Similar to Tableau, Power BI is used for creating interactive dashboards and reports.  It integrates well with other Microsoft products.