Data Analysis has been around for a long time. Up until a few years ago, it was often practiced using expensive, closed-source tools like Tableau. However, with the rise of Python, SQL, and other open-source libraries, data analysis has become more accessible and powerful.
In this certification, I have gained fundamental skills in data analysis using Python. By the end of this certification, I learned how to read data from various sources like CSV files and SQL databases, and how to process and visualize data using libraries like Numpy, Pandas, Matplotlib, and Seaborn.
This repository contains the following five projects, each demonstrating specific data analysis techniques:
A Python program that calculates the mean, variance, and standard deviation of a 3x3 matrix. This project introduces fundamental statistical calculations using Python and Numpy.
- Tools/Libraries Used: Numpy
This project analyzes demographic data to discover insights about the population, such as the percentage of individuals who hold advanced degrees and the income distribution by race and gender.
- Tools/Libraries Used: Pandas
A project focused on visualizing medical data to highlight trends in BMI, cholesterol levels, and other health metrics. The visualizations help identify patterns that could be useful in healthcare analytics.
- Tools/Libraries Used: Pandas, Matplotlib, Seaborn
A time series analysis and visualization of website page views over a given period. The project demonstrates how to clean data, calculate moving averages, and visualize trends.
- Tools/Libraries Used: Pandas, Matplotlib, Seaborn
An analysis of sea level rise data over time, with a linear regression model to predict future sea levels. This project highlights the power of predictive analysis using historical data.
- Tools/Libraries Used: Pandas, Matplotlib, Seaborn
The following tools and libraries were used throughout the projects:
- Python
- Numpy
- Pandas
- Matplotlib
- Seaborn
Through these projects, I have built a strong foundation in data analysis using Python. I have learned how to clean, process, and visualize data to derive meaningful insights. This certification has provided me with the skills necessary to tackle real-world data analysis challenges using open-source tools.