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5 min read
Data Analytics involves examining raw data sets to derive conclusions. It's the process of cleaning, transforming, and modeling data to discover valuable information, draw conclusions, and support decision-making. This technique has a wide range of applications, including business, healthcare, finance, and social sciences.
Data: This is the raw information that forms the basis of data analytics. It can come from a variety of sources and in different formats, such as numbers, text, images, or even audio files.
Big Data: A term that refers to extremely large data sets that are difficult to manage and analyze using traditional data management tools. Examples include data from social media feeds, online transaction records, and telematics data.
Data Mining: This is the process of exploring large data sets to uncover hidden patterns, correlations, and other insights. For example, a retailer might use data mining to understand customer buying habits and create targeted marketing campaigns.
Predictive Analytics: This involves using data, statistical algorithms, and machine learning techniques to predict future outcomes. For instance, businesses can predict future sales based on historical data and current market trends. Learn more.
Descriptive Analytics It's a type of analytics that deals with analyzing historical data to understand what has happened in the past. Descriptive analytics can help a business understand how it performed over a certain period.
Prescriptive Analytics: It's a complex type of analytics that not only predicts future outcomes but also suggests actions to benefit from the prediction. It's like a GPS, not only forecasting what will happen but also providing directions to reach your destination.
Data Visualization: It's the practice of translating data into a visual context, like a chart or a graph. Data visualization allows users to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. For instance, data storytelling with AutoPi.
Machine Learning: A subset of artificial intelligence (AI) that gives computers the ability to learn from data without being explicitly programmed. Machine learning algorithms are often used in data analytics to make predictions or decisions without human intervention.
Business Intelligence (BI): BI is a technology-driven process for analyzing data and presenting actionable information to help corporate executives, business managers, and other end users make informed business decisions.
Data Warehouse: A large store of data collected from a wide range of sources used to guide management decisions. It is designed to support data analysis and reporting.
In today's data-driven world, understanding data analytics is crucial. With the ability to analyze big data comes the potential to improve strategic decision-making, boost operational efficiency, predict customer behavior, and increase profitability. Whether it's for personal use or for businesses, grasping what is data analytics? is a step towards unlocking valuable insights hidden in the ever-growing sea of data.
Remember, the field of data analytics is continually evolving, and as you advance in your understanding, you'll encounter even more terms and concepts. This glossary is a starting point to get you familiar with the basics.
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