Data Analytics involves examining, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making.
Why do we choose Data Analytics in Future?
Data Analytics involves examining, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making. It combines techniques from statistics, computer science, and domain knowledge to analyze structured or unstructured data and extract meaningful insights.
Key components of data analytics include:
- Data Collection: Gathering raw data from various sources like databases, surveys, logs, or real-time sensors.
- Data Cleaning: Removing or correcting inaccuracies, inconsistencies, and missing values to prepare the data for analysis.
- Data Transformation: Structuring the data into a usable format, often through processes like normalization, aggregation, or feature engineering.
- Data Analysis: Using statistical methods, machine learning algorithms, and visualization tools to uncover patterns, trends, or correlations in the data.
- Data Interpretation: Converting the results into actionable insights that can inform business strategies or solve specific problems.
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