Descriptive analytics is a type of data analysis that summarizes and describes the features of a dataset. The following are the types of data used in descriptive analytics:
- Structured data: This data is organized in a tabular format and includes columns and rows. Examples include spreadsheets, databases, and customer information.
- Time-series data: This data includes information that is collected over time, such as sales data, stock prices, and website traffic.
- Demographic data: This data includes information about customer age, gender, income, education level, and location.
- Behavioral data: This data includes information about customer interactions with a brand, such as website visits, social media engagement, and product purchases.
- Sales data: This data includes information about sales performance, such as revenue, units sold, and customer acquisition cost.
Descriptive analytics involves calculating summary statistics such as mean, median, mode, standard deviation, and quartiles to describe the distribution of data. It also involves creating visualizations, such as histograms, bar charts, and line charts, to help understand the patterns and relationships within the data.
Descriptive analytics provides a summary of the characteristics of a dataset and is often used to understand the data before conducting more advanced data analysis techniques such as predictive modeling and hypothesis testing.
Approaches and theories of Data for Descriptive analysis
Descriptive analytics is a type of data analysis that summarizes and describes the features of a dataset. The following are the common approaches and theories used in descriptive analytics:
- Summary statistics: This involves calculating measures such as mean, median, mode, standard deviation, and quartiles to describe the distribution of data.
- Data visualization: This involves creating graphs and charts to visually represent the data and identify patterns, such as histograms, bar charts, and line charts.
- Inferential statistics: This involves using a sample of data to make inferences about the population from which the sample was drawn. Common techniques include hypothesis testing and confidence intervals.
- Probability theory: This is the branch of mathematics that deals with the analysis of random events and their outcomes. It provides the foundation for inferential statistics and is used to calculate the likelihood of different outcomes.
- Bayesian statistics: This is a branch of statistics that incorporates prior beliefs and prior probabilities into statistical analysis. It allows for updating beliefs and probabilities as new data becomes available.