Data collection is the foundation of data analytics. It involves gathering raw data from various sources, including databases, APIs, IoT devices, and web scraping techniques. The quality of collected data directly affects the accuracy of analytics results.
Data cleaning ensures accuracy and consistency in datasets. This step involves handling missing values, removing duplicates, and correcting inconsistencies to improve data quality.
Data transformation converts raw data into an analyzable format. This step involves structuring, filtering, and aggregating data to prepare it for analysis.
Data analysis involves applying statistical and machine learning techniques to extract insights. These techniques help identify trends, correlations, and patterns in the data.
Machine learning algorithms play a key role in data-driven decision-making. They help automate pattern recognition, predictions, and classifications.
Data visualization tools help convert complex datasets into intuitive graphical representations, allowing users to identify insights quickly.
