https://github.com/edaaydinea/quantiumdataanalytics
https://github.com/edaaydinea/quantiumdataanalytics
Last synced: 7 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/edaaydinea/quantiumdataanalytics
- Owner: edaaydinea
- Created: 2025-02-05T14:57:23.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T15:04:29.000Z (8 months ago)
- Last Synced: 2025-02-05T16:23:01.633Z (8 months ago)
- Size: 14.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Quantium - Data Analytics Virtual Internship
This repository contains the Quantium Data Analytics Virtual Internship program completed by me. The program was offered by Forage and Quantium. The program was divided into three tasks. The tasks were as follows:
1. **Task 1: Data preparation and customer analytics**
2. **Task 2: Experimentation and uplift testing**
3. **Task 3: Analytics and commercial application**
## Table of Contents
- [Quantium - Data Analytics Virtual Internship](#quantium---data-analytics-virtual-internship)
- [Table of Contents](#table-of-contents)
- [Task 1: Data Preparation and Customer Analytics](#task-1-data-preparation-and-customer-analytics)
- [Task 2: Experimentation and Uplift Testing](#task-2-experimentation-and-uplift-testing)
- [Task 3: Analytics and Commercial Application](#task-3-analytics-and-commercial-application)
## Task 1: Data Preparation and Customer Analytics
In this task, I performed data preparation and customer analytics. The steps included:
- Cleaning and preprocessing the data to ensure accuracy and consistency, including handling missing values and outliers.
- Conducting exploratory data analysis (EDA) to understand customer behavior and identify trends, such as purchase patterns and customer segmentation.
- Creating visualizations to present the insights gained from the data, using tools like Matplotlib and Seaborn.
- Summarizing key findings and providing actionable recommendations based on the analysis.
## Task 2: Experimentation and Uplift Testing
In this task, I focused on experimentation and uplift testing. The steps included:
- Designing and implementing experiments to test hypotheses about customer behavior, such as A/B testing to compare different marketing strategies.
- Analyzing the results of the experiments to measure the effectiveness of different strategies, using statistical methods to ensure the validity of the results.
- Performing uplift modeling to identify the impact of interventions on customer segments, using techniques like decision trees and random forests.
- Interpreting the results to provide insights into which strategies are most effective for different customer segments and making data-driven recommendations.
## Task 3: Analytics and Commercial Application
In this task, I focused on applying analytics to solve commercial problems. The steps included:
- Identifying key business questions and objectives that can be addressed using data analytics.
- Developing analytical models to provide insights and support decision-making, using techniques such as regression analysis and clustering.
- Creating dashboards and reports to communicate findings to stakeholders, using tools like Tableau and Power BI.
- Providing actionable recommendations to improve business performance based on the analysis.
- Evaluating the impact of the recommendations and refining the models as needed.