Data

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It combines techniques from statistics, computer science, and domain expertise to analyze data and make informed decisions. Key elements of data science include:

1. Data Collection and Cleaning – Gathering and preparing raw data for analysis.
2. Data Analysis and Modeling – Using statistical methods, machine learning, and algorithms to uncover patterns and trends.
3. Visualization – Presenting data insights through charts, graphs, and dashboards.

Machine learning is a subset of artificial intelligence (AI) that enables computers to learn and make decisions without being explicitly programmed. It involves using algorithms and statistical models to analyze data, identify patterns, and make predictions or decisions based on that data. Key aspects of machine learning include:

1. Supervised Learning – Training models on labeled data to predict outcomes (e.g., email spam detection).
2. Unsupervised Learning – Finding hidden patterns or structures in unlabeled data (e.g., customer segmentation).
3. Reinforcement Learning – Teaching models to make decisions by rewarding desirable outcomes (e.g., in game strategies).
4. Deep Learning – A specialized form of machine learning using neural networks to process complex data (e.g., image or speech recognition).

Machine learning is widely used in applications like recommendation systems, fraud detection, and natural language processing.

Data scraping, also known as web scraping, is the process of automatically extracting data from websites or other online sources. It involves using specialized tools or scripts to gather information such as text, images, or structured data like tables, which is not readily available for download.