https://github.com/ysayaovong/datacamp-data-analyst-in-python

Develop your data analytics skills in Python. Gain the data analyst skills to manipulate, analyze, and visualize data. No coding experience required!
https://github.com/ysayaovong/datacamp-data-analyst-in-python

Last synced: 7 days ago
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Develop your data analytics skills in Python. Gain the data analyst skills to manipulate, analyze, and visualize data. No coding experience required!

• Host: GitHub
• URL: https://github.com/ysayaovong/datacamp-data-analyst-in-python
• Owner: YSayaovong
• Created: 2024-11-19T23:11:17.000Z (about 1 month ago)
• Default Branch: main
• Last Pushed: 2024-12-13T14:14:35.000Z (21 days ago)
• Last Synced: 2024-12-13T15:58:59.311Z (21 days ago)
• Language: Jupyter Notebook
• Size: 1.6 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# DataCamp: Data Analyst in Python Career Track

This repository documents my journey through DataCamp's **Data Analyst in Python** career track. This program provided a comprehensive learning experience in data analysis, focusing on Python programming, data manipulation, statistical analysis, and data visualization. 

## Why I Chose This Career Track

As a professional transitioning into data analytics, I wanted to enhance my analytical skills and deepen my knowledge of Python for data-driven problem-solving. This career track was a structured pathway to master the tools and techniques required for extracting insights from data and creating impactful visualizations. 

The program aligned with my goal to integrate data analysis skills into my professional toolkit, complementing my prior experiences in mechanical design and engineering. 

## What I Accomplished

Over the course of this program, I gained proficiency in key data analysis skills and tools, including:  

- **Data Manipulation:** Leveraged Python libraries like `pandas` and `numpy` to clean, transform, and analyze datasets.  

- **Data Visualization:** Created effective visualizations using `matplotlib` and `seaborn` to communicate findings clearly.  

- **Statistical Analysis:** Performed exploratory data analysis (EDA) and statistical tests to derive insights and support decision-making.  

- **SQL for Data Analysis:** Queried databases to extract, manipulate, and analyze data efficiently.  

- **Project-Based Learning:** Completed capstone projects that simulated real-world scenarios, applying all the skills learned throughout the program.

## Skills and Tools

- **Programming Languages:** Python  

- **Libraries:** pandas, numpy, matplotlib, seaborn  

- **Database Skills:** SQL for querying and analysis  

- **Data Visualization:** Creating plots, charts, and dashboards to summarize data  

- **Statistical Methods:** Hypothesis testing, regression, and data modeling  

## Key Takeaways

1. Strengthened my ability to work with large datasets and extract meaningful insights.  

2. Developed technical proficiency in Python for data analysis and visualization.  

3. Gained experience in end-to-end data workflows, from raw data cleaning to presenting actionable findings.