"Data analysis" refers to the process of analyzing and interpreting data to gain insights, patterns, trends, or insights. This process typically involves processing raw data, applying statistical methods and models, and presenting the results in an understandable form. Data analysis is used to extract information from the data, support decision-making, identify problems, and improve business processes.
Typical functions of software in the "data analysis" area are:
Data import and processing: Importing and processing data from various sources, formats, and file types.
Data cleaning and preprocessing: Cleaning data, removing duplicates, outliers, or inconsistencies, and preprocessing data for analysis.
Statistical analysis: Applying statistical methods such as mean, median, standard deviation, regression, correlation, and hypothesis testing to analyze data.
Visualization: Representing data in the form of charts, graphs, tables, or dashboards for easier interpretation and communication of results.
Reporting: Generating reports, summaries, or presentations based on the analysis results for decision-makers or stakeholders.
Exploratory data analysis: Conducting exploratory data analysis to discover patterns, trends, or unexpected insights in the data.
Predictive analytics: Applying predictive analytics techniques such as machine learning or data modeling to predict future events or trends.
Interactive analysis tools: Providing interactive tools or dashboards that allow users to analyze and explore data independently.
Time series analysis: Analyzing data over time to identify seasonal patterns, cyclical trends, or long-term changes.