https://github.com/vitaliikalyta96/data-analysis
These projects are about data analysis using multiple tools and techniques to derive insights from various datasets.
https://github.com/vitaliikalyta96/data-analysis
a-b-testing amplitude bigquery google-sheets looker-studio postgresql python sql tableau-public tracking-plan
Last synced: 3 months ago
JSON representation
These projects are about data analysis using multiple tools and techniques to derive insights from various datasets.
- Host: GitHub
- URL: https://github.com/vitaliikalyta96/data-analysis
- Owner: VitaliiKalyta96
- Created: 2025-02-25T17:40:57.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-23T11:42:56.000Z (over 1 year ago)
- Last Synced: 2025-03-23T12:26:41.265Z (over 1 year ago)
- Topics: a-b-testing, amplitude, bigquery, google-sheets, looker-studio, postgresql, python, sql, tableau-public, tracking-plan
- Language: Jupyter Notebook
- Homepage:
- Size: 8.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Data Analysis Projects
### Overview
These projects focuses on data analysis using multiple tools and techniques to derive insights from various datasets.
The goal 🎯 is to explore 📈 , visualize 📊 and interpret data to support decision-making processes.
Tools Used:
✅ Google Sheets – For quick data analysis and reporting.
✅ SQL – For querying and managing structured databases.
✅ BiqQuery - A fully managed, serverless data warehouse by Google Cloud that enables fast SQL queries and large-scale
data analysis.
✅ Looker Studio – For web-based data visualization.
✅ Tableau – For interactive data visualization and dashboards.
✅ Amplitude - A product analytics platform that helps teams track user behavior, analyze engagement, and make data-driven
decisions.
✅ Python (Pandas, NumPy, Matplotlib, Seaborn) – For data manipulation, analysis, and visualization.
✅ A/B testing to compare different versions of a feature or treatment and analyze the impact on user behavior or
performance.
### Project Components
1. Data Collection - Collect data from multiple sources such as CSV files, databases, and APIs.
2. Data Cleaning and Preprocessing - Handle missing values, remove duplicates, and structure data for analysis.
3. Exploratory Data Analysis (EDA) - Use statistical summaries and visualizations to understand data patterns.
4. Data Visualization - Create charts, graphs, and dashboards to present key insights.
5. Insights & Decision Making - Interpret results and provide data-driven recommendations.
### How to run:
1. Clone the repository:
`git clone https://github.com/VitaliiKalyta96/Data-analysis.git`
2. Open any project in folder.