Difference between Data Analysis and Data Analytics
==2024-12-23=!=
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Theory
| Aspect | Data Analysis | Data Analytics |
|---|---|---|
| Definition | Hands-on data exploration and evaluation. | A broader term encompassing the methodology and science behind the analysis, with data analysis as a subset. |
| Scope | Focused on examining datasets to uncover patterns, trends, or insights. | Involves the entire lifecycle of working with data, including collection, processing, analysis, and reporting. |
| Objective | To interpret raw data and answer specific questions or hypotheses. | To use systematic and scientific approaches to support decision-making and strategic planning. |
| Methods Used | Primarily includes statistical methods, visualization, and descriptive techniques. | Incorporates advanced techniques such as machine learning, predictive modeling, and prescriptive analytics. |
| Tools and Techniques | Tools like Excel, Tableau, or Python libraries such as Pandas and Matplotlib. | Broader set of tools, including databases (SQL), programming (R, Python), and advanced frameworks (TensorFlow). |
| Output | Generates insights, trends, and summaries from data. | Provides actionable insights, predictions, and recommendations for business or research. |
| Example | Analyzing sales data to identify peak seasons. | Building a predictive model to forecast future sales trends. |
PTR
- While data analysis focuses on interpreting data for immediate insights, data analytics is the broader process that supports strategic, data-driven decisions.