Core Concepts in Data Analysis and Business Intelligence (BI)
Data analysis involves inspecting, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. Business intelligence (BI) refers to the strategies and technologies used to analyze business data and present actionable insights to improve business performance.
Categorical variables are variables that represent qualitative data, consisting of distinct categories or groups. For example, in a dataset of car owners, the car's make (Toyota, Ford, Honda, etc.) would be a categorical variable.
Comparison is a method in data analysis that involves examining the differences and similarities between two or more datasets, variables, or groups. This can help identify patterns, trends, and relationships among the data.
Continuous variables are variables that can take on an infinite number of values within a given range. For example, the temperature in a city throughout the day is a continuous variable, as it can take on any value between the lowest and highest temperatures.
A field is a column in a dataset that represents a specific attribute or characteristic of the data. In business intelligence, fields are assigned to data columns after importing data into BI software.
Data types define the kind of values a variable can hold, such as integers, strings, dates, etc. In BI, roles are assigned to data types, which can be either dimensions or measures.
Data filtering is the process of extracting a subset of data based on specified criteria. This helps analysts focus on specific information within a larger dataset.
A dataset is a collection of data that serves as the source for data analysis and visualization. It usually consists of rows (records) and columns (fields).
Data visualization is the graphical representation of data, designed to present complex information quickly and clearly. Common forms include bar charts, line charts, pie charts, and scatter plots.
Distribution in data analysis refers to the way data is spread or distributed across various values or categories. Analyzing distribution helps reveal patterns, trends, and relationships among variables.
Exploratory Data Analysis (EDA) is the initial stage of data analysis where analysts use statistical and visualization tools to identify patterns, anomalies, and relationships in the data.
A feature in the context of BI tools refers to the functionality provided to end-users. Features are often accessible through tabs or menus in the software's interface.
In business intelligence, a measure is a numeric value of a data field that can be quantified, such as sales revenue. A dimension, on the other hand, is a qualitative value of a data field, such as product names or dates.
A relationship in data analysis refers to the connection or correlation between two or more variables. For example, the relationship between advertising expenditure and sales revenue in a company's data.
Sorting is a method of organizing data in a specific order, such as alphabetical, ascending, or descending. This can help identify patterns or make data easier to understand.
Summarizing data involves creating a statistical summary of the dataset, including metrics like count, sum, mean, maximum, and minimum. This provides a high-level overview of the data's characteristics.
A variable is a measure or attribute of a field in a dataset. Variables can be continuous, categorical, or a combination of both.