By Danielle David

Introduction

This Data Analysis project examines two key datasets: calls.csv and webleads.csv.

For calls.csv, the analysis will focus on telephonic interactions with potential and existing customers.

webleads.csv, on the other hand, focuses on leads generated through our digital platforms.

The analysis shall follow Google's Data Analysis Framework:

1. Ask: Define the objectives and key questions that the analysis aims to answer.
2. Prepare: Outline the data collection methods and verify the integrity and reliability of the datasets.
3. Process: Detail the steps taken to clean and preprocess the data for analysis.
4. Analyze: Employ statistical methods and visualization tools to extract meaningful insights.
5. Share: Present the findings in an accessible format, tailored to both technical and non-technical audiences.
6. Act: Propose actionable strategies based on the analysis to drive business growth and efficiency.

Python and Tableau shall be utilized in this analysis.

For the non-technical audience, the Summary of the Findings section below will summarize all the data into actionable insights.

For the technical audience who wants to see the logic of the code, please see Detailed Analysis section.

Summary of the Findings

Calls.csv

Missing Values