Skip to content

AbdulAmirK/Vanguard

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

15 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Vanguard

Prerequisites

Before this starting this project, you should have learnt about:

  • Data types, operators and structures

  • Flow control (if-else statements and loops)

  • Functions

  • Filters

  • Pandas and Numpy

  • Basic Statistics

  • SQL



Introduction

Vanguard, a prominent U.S.-based investment management company, embarked on a journey to evolve digital experience with the goal of enhancing client engagement. Recognizing the rapidly changing digital landscape, we aimed to refine our online process with a sleeker, more intuitive User Interface (UI). This strategic move was driven by our commitment to meeting the growing needs of our clients and ensuring a more seamless navigation experience across our digital platform.


Project Overview

The project centers on analyzing the impact of a new User Interface (UI) design on the online process completion rates at Vanguard, an investment management company. By conducting an A/B test comparing traditional ("Control") and revised ("Test") UIs, the study aimed to determine if the new design enhances user experience and engagement. Initial analyses focused on demographic insights and engagement patterns, followed by detailed examinations of completion rates, regressions at specific process steps, and overall user behavior. The observed improvement in completion rates for the Test group exceeded Vanguard’s minimum effectiveness threshold, indicating the new UI's potential benefit. However, distinct regression points identified for each group—step 3 for Control and step 1 for Test—highlighted specific areas requiring optimization. The project underscores the importance of continuous UI refinement to improve user navigation and process completion, aligning with broader objectives of increased engagement and potential revenue growth.

Acknowledgements

Dataset source:Client Profiles, Digital Footprints, Experiment Roster

Cleaning & Merging Corrected Data Types Removed Duplicate Rows Handled Missing Values Filtered Outliers Split Data such as date & time Created New Features like age groups

About the dataset

Dataset Description

-Client Profiles, Digital Footprints, Experiment Roster

Tools and Technologies Used

  • Python: The primary programming language for data cleaning and analysis.
  • Pandas & NumPy: For data manipulation and numerical analysis.
  • Matplotlib & Seaborn: Utilized for generating visualizations.
  • Jupyter Notebook and Google Colab: Served as the interactive environment for code execution and data exploration.
  • SQL Workbench
  • Tableau

Findings and Conclusions

Deliverables

  • Complete and clean notebook(s) containing the code, analysis, and visualizations
  • Database: The exported .sql file with SQL Queries
  • Python files
  • Jupyter notebook containing the report in full with visualizations
  • A README file with a thorough project documentation.
  • A slide deck for the project presentation

Contributors

  • Amir Abdul
  • Marco Mo

logo_ironhack_blue 7

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors