Data Science is a rapidly growing field that requires a diverse set of skills and knowledge to be successful. As you navigate the world of Data Science, it's important to understand the key terminology used in this field.
In this glossary, we'll define and explain some of the most commonly used terms in Data Science.
Machine Learning: A type of artificial intelligence that uses algorithms to learn from data and make predictions or decisions without being explicitly programmed.
Data Mining: The process of discovering patterns in large datasets.
Data Wrangling: The process of cleaning and preparing data for analysis.
Regression: A statistical method used to predict a numerical value based on other variables.
Clustering: A technique used to group similar data points together.
Big Data: Extremely large datasets that require specialized tools and techniques to process and analyze.
Deep Learning: A subset of Machine Learning that uses artificial neural networks to simulate the learning process of the human brain.
Data Visualization: The use of graphical representations to communicate information and insights from data.
Artificial Intelligence (AI): A field of computer science that focuses on creating intelligent machines that can perform tasks that typically require human intelligence, such as natural language processing, visual perception, and decision-making.
Natural Language Processing (NLP): The field of AI that focuses on the interaction between computers and human language, including tasks such as text-to-speech and sentiment analysis.
Feature Engineering: The process of selecting and transforming raw data to create new features that can improve the performance of Machine Learning models.
Ensemble Learning: A Machine Learning technique that combines multiple models to improve their predictive power and reduce the risk of overfitting.
Neural Network: A type of Machine Learning model that is designed to simulate the behavior of the human brain, consisting of layers of interconnected nodes that process and transform data.
By expanding your knowledge of key terms in Data Science, you can improve your understanding of the field and stay up-to-date with the latest trends and techniques. If you're interested in learning more about Data Science, the Southampton Data Science Academy offers a variety of short courses that can help you use data science in your work:
