15 de janeiro de 2024 09:16:08 ART
1. Data Volume and Variety:
- Data Analytics: Typically deals with structured data and may not require handling large volumes. It is well-suited for relational databases and traditional business data.
- Data Science: Often deals with unstructured and large-scale data, requiring skills in big data technologies. Data scientists may work with diverse data types, including text, images, and streaming data.
2. Time Horizon:
- Data Analytics: Focuses on past and present data to provide insights for current decision-making. It is concerned with short to medium-term analysis.
- Data Science: Involves a more future-oriented approach, using historical data to make predictions and model future scenarios. It is often geared towards longer-term strategic planning.
In summary, while data analytics services and data science share common elements, such as working with data to derive insights, they differ in terms of scope, objectives, techniques, and tools. Data analytics is more focused on interpreting historical data for immediate decision-making, whereas data science encompasses a broader set of activities, including predictive modeling and algorithm development for solving complex problems.
