In data science, raw data is the original data that has not been processed or analyzed in any way. It is the starting point for data scientists, who use it to extract insights and patterns that can be used to make predictions, solve problems, or inform decision-making.
Raw data can come in a variety of forms, including text, numbers, images, and audio. It can be structured, meaning that it is organized in a consistent way, or unstructured, meaning that it is not organized in any particular way.
Raw data can be messy and contain errors. It may be incomplete, duplicate, or inconsistent. Data scientists must clean and prepare raw data before they can use it for analysis. This process involves identifying and correcting errors, removing duplicates, and formatting the data in a consistent way.
Once raw data has been cleaned and prepared, data scientists can use it to perform a variety of tasks, such as:
- Exploratory data analysis (EDA): EDA is used to explore the data and identify patterns and trends. This can help data scientists to understand the data and to ask better questions about it.
- Data modeling: Data modeling is used to create mathematical models that can be used to predict future outcomes. Data scientists use a variety of modeling techniques, such as regression, classification, and clustering.
- Machine learning: Machine learning is a type of artificial intelligence that allows computers to learn without being explicitly programmed. Data scientists use machine learning algorithms to train models on raw data. Once a model is trained, it can be used to make predictions about new data.
Examples of raw data in data science
- Sales data: This data could include information about products sold, customers who made purchases, and the dates and times of purchases.
- Social media data: This data could include text, images, and videos that are posted on social media platforms.
- Sensor data: This data could include information about temperature, humidity, and pressure that is collected from sensors.
- Medical data: This data could include information about patients' health records, such as their diagnoses, medications, and test results.
Raw data is the foundation of data science. Without raw data, data scientists would not be able to extract insights and patterns that can be used to make predictions, solve problems, or inform decision-making.
Raw data can be a valuable resource for data scientists. However, it is important to remember that raw data is not always clean or error-free. Data scientists must carefully clean and prepare raw data before they can use it for analysis.
