✅ From Raw Data to Business Insights - My Journey Using Python and SQL

In the realm of data analytics, transforming raw data into actionable insights is both an art and a science. This blog post delves into a personal project that highlights my journey from data collection to deriving business insights using Python and SQL, two powerful tools in the data analyst’s arsenal.

Project Overview: Enhancing Customer Experience Through Data

The focal point of this project was to analyze customer feedback across various touchpoints to identify determinants of customer satisfaction. The dataset presented a complex blend of ratings, textual feedback, and transaction details, posing a unique set of challenges.

Data Preparation with Python

Python’s robust libraries, Pandas and NumPy, were instrumental in preprocessing steps. A tailored approach was adopted to cleanse the data, involving handling missing values, outliers, and duplicate records. Below is a Python snippet that encapsulates the essence of data cleaning:

import pandas as pd
import numpy as np

# Data loading
df = pd.read_csv('customer_feedback.csv')

# Deduplication
df = df.drop_duplicates().reset_index(drop=True)

# Handling missing values with strategy
df['rating'] = df['rating'].fillna(value=df['rating'].mean())

Delving Deep with SQL Queries

Post-cleaning, the dataset was migrated to a SQL database for deep analysis. SQL’s aggregation and window functions facilitated complex queries to draw correlations between customer feedback and satisfaction scores, shedding light on improvement areas. Here’s a sophisticated SQL query example:

WITH ranked_feedback AS (
  SELECT
    feedback_category,
    RANK() OVER (ORDER BY AVG(rating) DESC) as rank,
    AVG(rating) as avg_rating
  FROM customer_feedback
  GROUP BY feedback_category
)
SELECT * FROM ranked_feedback
WHERE rank <= 3;

Visual Insights and Storytelling

Insights were brought to life using Python’s Matplotlib and Seaborn for visualization, crafting compelling stories around customer preferences and pain points. The visualizations emphasized key findings, such as the paramount importance of product quality over speed of delivery in customer satisfaction.

Conclusion

This project underscored the invaluable role of Python and SQL in the data analysis process, from raw data handling to insightful visual storytelling. It illustrated not just the technical proficiency required but also the analytical mindset necessary to translate data into strategic business recommendations.