Data Analysis

1. Understanding Project Requirements

• Review your instructions, goals, and deliverables.

• Clarify data format, timeline, and any special rules.

2. Data Collection or Receipt

• Collect raw data from client files (PDF, Excel, web sources, databases, etc.)

• Identify source structure and organize input templates if needed.

3. Data Entry

• Enter data accurately into spreadsheets, databases, or systems.

• Ensure correct formatting, consistent labels, and standardized entries.

• Use data validation rules or drop-downs when required.

• Perform double-checking or cross-verification for accuracy.

4. Data Cleaning & Preparation

• Remove duplicates, fix errors, and handle missing values.

• Convert date/time formats, numbers, text fields to correct structure.

• Standardize column headers and content formatting.

5. Data Exploration (EDA)

• Summarize the data using descriptive stats (mean, max, count, etc.)

• Create basic charts to explore trends and patterns.

• Identify key variables or outliers.

6. Data Transformation

• Create calculated fields or grouped categories.

• Merge/split columns or filter based on specific conditions.

7. Data Analysis

• Apply pivot tables, queries (SQL), or formulas to extract insights.

• Analyze performance, trends, and correlations.

• Optionally apply simple forecasting or breakdowns by category.

8. Data Visualization & Dashboard Creation

• Build interactive dashboards (Power BI / Excel).

• Use slicers, filters, and KPIs to make data more readable.

9. Reporting & Delivery

• Prepare final reports (PDF, Excel, Power BI file).

• Include visuals, summaries, and recommendations based on the data.

10. Revisions and Support

• Collect your feedback and apply adjustments as needed.

• Offer ongoing support for future updates or additional analysis.