Introduction to Data Science | Keldin's ILP Portfolio

Description

In data science, I applied data analysis and machine learning techniques to visualize data, understand trends, and make predictions. Throughout the course, I learned how to obtain data, preprocess it, apply machine learning methods, and visualize the results. By the end of the course, I had enough theoretical knowledge and skill with modern statistical programming languages and their libraries to define and perform complete data science projects.

California’s Population Growth & Decline

For the final project in my data science class, I developed a model to correlate various factors contributing to population growth and decline in California. Some of the technologies I used include:

Python
CSV files
Pandas
Numpy
Matplotlib

All project files and source code are available on my GitHub here. There is also a video presentation of the findings which can be viewed here

Below is a snippet from the report produced for this project. The full report can be viewed or downloaded here:

Data science snippet