KANATA GC (Types of Data Analysis)
There are several types of data analysis, including:
Descriptive analysis: summarizes the main characteristics of a dataset.
A business analyst might summarize the average salary and age of employees in a company to get a general understanding of their demographic.
A retail company might summarize the sales data of their different product categories to understand which are the best-selling items.
Predictive analysis: uses statistical models to make predictions about future outcomes based on historical data
An economist might use a time series model to predict future interest rates based on historical data.
A retail company might use customer purchase history to predict which items a customer is likely to purchase in the future and make personalized recommendations.
Inferential analysis: draws conclusions from a sample to make inferences about a population
A researcher might sample a small group of people and make inferences about the population as a whole based on the results of a survey.
A market research firm might sample a small group of customers and make inferences about the preferences and behavior of the entire customer base.
Exploratory data analysis (EDA): involves summarizing and visualizing the main features of a dataset to understand its underlying structure
An economist might use a time series model to predict future interest rates based on historical data.
A data analyst might create a heat map of customer locations to identify regions with higher concentrations of sales and target those areas for marketing efforts.
Causal analysis: seeks to identify cause-and-effect relationships between variables
A healthcare researcher might run a randomized controlled trial to determine the cause-and-effect relationship between a new drug and its effectiveness in treating a particular disease.
A retailer might conduct an A/B test to determine the impact of different pricing strategies on sales.
Time series analysis: examines the change in a set of variables over time to identify trends and patterns
A financial analyst might analyze stock prices over a period of time to identify trends and make investment decisions.
A retail company might analyze sales data over time to identify seasonal trends and make informed decisions about inventory management.
Experimental design: involves creating a controlled experiment to observe the effects of different treatments on an outcome variable
A psychologist might conduct an experiment to determine the impact of different study techniques on exam scores.
A retailer might run a controlled experiment to determine the impact of different in-store displays on product sales.
These are just a few types, with few general examples and a second example of how each type of data analysis can be applied in a retail context.
The specific techniques can vary widely depending on the problem being analyzed and the tools available.