Introduction
Data Analysis :
is a process of interpreting data that leads to meaningful decisions
Data Analysis is a small part of Data Analytics and is prepared on a single prepared data set.
Data Analytics :
uses raw data captured from many sources to process, analyze and interpret what may happen and how an organization can use this knowledge for its benefits.
Benefits of Data Analysis :
- Finding Patterns
- Discovering Opportunities
- Predicting Events and actions
- Make well-informed decisions
Types of Analytics :
- Descriptive Analytics – “what happened ?” Descriptive analytics helps to answer “what happened ?”. It uses data visualization techniques, such as the following:
- Pie charts: A diagram that shows data as slices in a circular-shaped graph
- Bar charts: A diagram that shows data in rectangular bars horizontally or vertically
- Line graphs: A diagram that uses lines to connect single data points generally plotted over a period of time
- Tables: A diagram that shows data in rows and columns
- Generated narratives: You can ask questions about your data and receive answers with visualizations
- Diagnostic Analytics – “why it happened ?” Diagnostic analytics helps to answer “why it happened ?”. It uses techniques such as the following:
- Drill-down: Seeing an overview of the data, to a detailed view within the same dataset
- Data discovery: A process for gathering, cataloging, and classifying data from different databases for analytics
- Data mining: Using analytics against a large dataset to discover meaningful insights
- Correlations: A measure between two variables that shows how closely related they are without stating a cause-and-effect relationship
- Predictive Analytics – “what might happen ?” Predictive analytics helps to answer “what might happen ?” in the future. It uses techniques such as the following:
- Machine learning (ML): A technique that teaches software how to learn from data, find patterns, and make decisions with minimal human intervention
- Forecasting: Predicting future value by looking at unique trends
- Pattern matching: Finding pre-determined patterns in raw data
- Predictive modeling: Predicting future events by analyzing patterns with input data
- Prescriptive Analytics- recommends actions to the predicted outcome Prescriptive analytics recommends actions to the predicted outcome. It uses techniques, such as the following:
- Graph analysis: Analyzing relationships between objects in a network or graph
- Simulation: Modeling the behavior of a system by using the characteristics and relationships of system components
- Complex event processing: Deriving a conclusion through tracking and analyzing streaming data
- Neural networks: An ML model that copies the function of neurons in the human brain to learn and solve complex problems
- Recommendation engines: A system that uses ML recommends the most relevant items to a user, based on behavior patterns