Types of data analysis

Hello World! My name is Mike I am hoping to be a self-taught data scientist, and this is my first post. I live in Australia’s NSW south coast after emigrating from England, and from time to time will share stories personal to me. This blog is planned to be a summary of my learning through Coursera, Udemy, manuals etc. It is a journey I am only just starting.

This story is a summary of types of data analysis. In the rough order of difficulty.

Descriptive analysis — Descriptive analysis is a way to summarize and present data in an easy-to-understand way. It includes techniques like calculating averages, measuring how spread out the data is, and making graphs to show patterns. The goal is to make the data understandable to anyone, regardless of technical expertise, and to use it to make informed decisions.

Exploratory analysis — Exploratory analysis is a way to look at data to find patterns, relationships, and interesting things that you might not have expected to see. It involves looking at graphs and charts, cleaning up messy data, and transforming the data so it is easier to analyse. The goal is to generate ideas and questions that can be evaluated later, and to gain a better understanding of the data before doing more formal analyses. It is a creative and flexible process that requires diligence and an open mind.

Inferential analysis — Inferential analysis is a way to use a small sample of data to make conclusions or predictions about a larger group of people or things. It involves using statistical methods to analyse the sample data and determine the likelihood that the results are true for the entire population. The goal is to use the sample data to make accurate predictions and generalizations about the larger population. This type of analysis is used in many areas, such as scientific research, market research, and public opinion polling.

Predictive analysis — Predictive analysis is like trying to predict the future using math. You look at past data and try to find patterns that can help you guess what might happen next. This can be useful in many areas, like business, healthcare, or sports. It helps organizations make better decisions and plan for the future by giving them insights into what might happen next.

Causal analysis — Causal analysis is about finding out why something happens and how different things are connected to each other. We look at data to see what causes what. This helps us understand how things work and make better decisions. It’s used in many different fields to study complex systems and make predictions about what might happen next.

Mechanistic analysis — Mechanistic analysis helps us understand how things work by studying their individual parts and how those parts work together. It is like taking apart a machine to see how it works. This helps us understand complex systems and make more accurate predictions about their behaviour. It is used in many different fields to develop better models and gain a deeper understanding of the world around us.