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Python for Cybersecurity: Scripts and tools for automating security tasks, log analysis, vulnerability scanning, and network monitoring.
Example: A Python script that scans open ports on a target machine and logs the results to a file.
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Data Analytics Projects: Data cleaning, visualization, and analysis using libraries like NumPy, Pandas, Matplotlib, and Seaborn.
Example: Using Pandas to analyze Texas Rangers player stats and Matplotlib to visualize batting averages over the season.
Example: Analyzing a CSV of sales data to find trends and visualizing monthly revenue with Matplotlib.
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Web Scraping: Collecting and processing data from websites using BeautifulSoup and Requests.
Example: Scraping product prices from an e-commerce site and saving them to a spreadsheet.
Example: Scanning multiple cybersecurity news sites for recent articles and compiling the headlines and links into a daily report.
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APIs & Automation: Building scripts to interact with APIs, automate repetitive tasks, and integrate with other tools.
Example: Automatically checking the Texas Rangers' game schedule and results via a sports API, then sending a daily report if they played that day.
Example: A script that fetches weather data from an API and sends a daily summary email.
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Machine Learning Experiments: Projects using scikit-learn and TensorFlow for classification, regression, and clustering.
Example: Using player statistics to train a model that predicts the outcome of a Texas Rangers game (win or loss).
Example: Training a model to predict house prices based on features like size and location.
