Data Collection
The first step in analytics is gathering relevant data from various sources, such as databases, sensors, websites, and more. Data can be structured (e.g., databases) or unstructured (e.g., social media posts).
Resource Pooling
Cloud providers maintain vast pools of computing resources that are shared among multiple users. Resources are allocated dynamically based on demand.
Data Cleaning and Preparation
Raw data often requires cleaning and preprocessing to remove errors, inconsistencies, and missing values. Data preparation also involves transforming data into a suitable format for analysis.
Exploratory Data Analysis (EDA
EDA is the process of visually and statistically exploring data to understand its characteristics, distribution, and potential patterns.
Statistical Analysis
Statistical techniques, such as regression analysis, hypothesis testing, and correlation analysis, are used to uncover relationships within data and make predictions.
Machine Learning
Machine learning algorithms, including supervised and unsupervised learning, are applied to analyze data and make predictions or classifications.
Data Visualization
Data is often visualized using charts, graphs, and dashboards to communicate insights effectively.
Data Interpretation
Analysts interpret the results of their analysis to derive actionable insights and make data-driven decisions.