What is Data Analysis?

Data analysis involves collecting, cleansing, transforming, and modeling data to discover useful information, suggesting conclusions, and enable decision-making. Data analysis encompasses diverse techniques under various names in different business, science, and social science domains.

Different techniques have been developed to analyze different types of data. For example:

Structured data is usually analyzed using statistical methods or machine learning algorithms.
Unstructured data can be analyzed using text mining or natural language processing techniques.
Spatial data can be analyzed using geographic information systems or Geographical Information Science techniques.
Temporal data can be analyzed using time series analysis or event-based modeling.

The term "data analysis" can refer to different things, and the choice of analysis technique often relates directly to the type of data you are analyzing. For example:

Exploratory data analysis helps you understand your data better and identify patterns and relationships.
Descriptive data analysis summarizes your data and describes its main features.
Predictive data analysis uses statistical techniques to predict future events or trends.
Causal data analysis tries to identify cause-and-effect relationships in your data.

Data analysis contains a set of independent processes that work iteratively to give the desired results. We’ll explore these processes at length later on in this article, but this is what the steps look like:

Collecting data from various sources
Cleaning and preparing the data for analysis
Exploring the data to identify patterns and relationships
Modeling the data to make predictions or test hypotheses
Communicating the results of the analysis

Data analysis is an iterative process, and you may find yourself going back and forth between different steps as you work. For example, you may need to go back and collect more data if your initial analysis reveals that you don't have enough information to answer your question. Or you may need to explore your data differently if your initial modeling efforts don't produce satisfactory results.

Categories: : Data Analysis Course