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  • Writer's pictureKANATA GC

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.

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