Data analysis
Systematic examination, interpretation, and transformation of raw data into meaningful insights, patterns, and trends. Part of monitoring and other crucial business processes. It involves the application of various statistical, mathematical, or other expert methods to uncover relationships, draw inferences, and derive valuable information from datasets. Unlike data collection, which focuses on the gathering of information, data analysis centers on exploring, organizing, and interpreting data to reveal underlying patterns or relationships that can inform decision-making and support the achievement of specific objectives.
Close terminology
Examples of synonimes and close terminology:
Data Exploration The preliminary phase of data analysis involving the examination and summary of key characteristics of the dataset.
Inferential Statistics Statistical techniques that make predictions or inferences about a population based on a sample of data.
Hypothesis Testing The process of assessing the validity of a claim or hypothesis about a population parameter using statistical methods.
Data Visualization The representation of data through charts, graphs, or other visual elements to facilitate understanding and insights.
Outlier Detection Identifying data points that deviate significantly from the overall pattern in a dataset.
Predictive Modeling Building models to predict future outcomes or trends based on historical data.
Cross-Validation A technique used to assess the performance of a predictive model by partitioning the data into subsets for training and testing.
Requirement
Questions or objectives that guide the analysis and help determine the appropriate approach, methods and indicators. The questions ensure that the analysis is aligned with the goals of the analysis.
The data analysis needs requirements as the process input, e.g. from goal-setting , or ad hoc decision . It can also form requirements as an output for other processes, like: planning , decision or change management .
Problem Formulation
Crucial process of devising a data science solution to a business problem. Its purpose can be identification of crucial elements, opportunities and risks, prediction , optimization of processes etc.
Modelling
Creating a simplified representation of a complex system or dataset to understand its structure and handle its crucial elements.
Extrapolation
Extrapolation is a analytical technique, used broadly across various knowledge domains, incl. data science, to use information that is already known to estimate values, beyond the original observation range.
Hypothesis
Specific statement or assumption that is tested during the analysis. Hypotheses provide a framework for focused analysis, easier calibration and interpretation of results. At the same time it is connected with risk of research bias.
INs and OUTs (section under development)
coming in
going out
Controls to review
regulation, documentation, reports