Data Science vs. Machine Learning: Unpacking the Key Differences

Data Science and Machine Learning (ML) are interconnected fields that use various techniques to analyze, interpret, and make predictions from large amounts of data. However, they are distinct in their methods and objectives.

Data Science is a multidisciplinary field that combines statistics, computer science, and domain expertise to extract insights and knowledge from data. Data scientists use various techniques, such as data mining, data visualization, and machine learning algorithms, to analyze and interpret data to solve complex problems and make data-driven decisions.

Machine Learning, on the other hand, is a subset of artificial intelligence (AI) that focuses on developing algorithms and models to enable computers to learn from and make predictions or decisions based on data. ML techniques involve training models on large datasets and using them to make predictions or classify new, unseen data. Machine learning is an essential tool in the data science toolkit, but it is not the only one.

In summary, data science is a broader field that encompasses various techniques and methods for extracting insights and knowledge from data, while machine learning is a specific approach within data science that focuses on building models to make predictions or decisions based on data.