"Data analysis" refers to the process of examining, cleaning, transforming, and interpreting data to identify useful information, patterns, and trends. The goal of data analysis is to extract valuable insights from raw data that can be used to support decision-making processes, problem-solving, and strategic planning. Data analysis involves various methods and techniques, including statistical analysis, machine learning, data mining, visualizations, and predictive analytics.
Data import and integration: Ability to import and integrate data from various sources, regardless of format and structure.
Data cleaning and preprocessing: Cleaning data by removing duplicates, outliers, and erroneous values, as well as preprocessing through normalization and transformation.
Exploratory Data Analysis (EDA): Conducting exploratory analyses to identify patterns, trends, and relationships in the data, often through visualizations such as histograms, scatter plots, and heatmaps.
Statistical analyses: Application of statistical procedures such as means, standard deviations, correlation analyses, and hypothesis tests to examine data.
Machine learning: Deployment of machine learning algorithms and techniques to predict future events, classify data, and cluster datasets.
Data mining: Extraction of hidden information and patterns from large datasets by applying data mining techniques such as association rules, decision trees, and clustering algorithms.
Visualization: Creation of interactive visualizations such as charts, graphs, and dashboards to present complex data in an understandable and insightful manner.
Predictive analytics: Application of statistical and machine learning methods to predict future events and trends based on historical data.
Reporting and presentation: Generation of reports, summaries, and presentations to communicate and present the results of data analysis.
Integration with other systems: Integration of data analysis software with other enterprise systems for seamless data transfer and processing.