Data Science vs. Machine Learning: What’s the Difference?

Data Science is an interdisciplinary field that involves the use of statistical and computational methods to extract insights and knowledge from data. It involves various techniques such as data mining, machine learning, and predictive analytics to identify patterns, trends, and correlations in large datasets.

Machine Learning is a subfield of Data Science that focuses on building algorithms that can learn from data and make predictions or decisions based on that learning. It involves the development of models and algorithms that can automatically improve their performance based on the information they receive.

Machine Learning algorithms can be classified into three types: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves the use of labeled data to train a model to make predictions or classifications on new data. Unsupervised learning involves finding patterns and structures in unlabeled data. Reinforcement learning involves training agents to take actions in an environment to maximize a reward.

The applications of Data Science and Machine Learning are diverse and include areas such as finance, healthcare, marketing, and transportation. Some examples of applications include fraud detection, image and speech recognition, recommendation systems, and autonomous vehicles.