Quick Mind Map for Statistics - Part 2

 

Statistics is the one of the pillar or a prerequisite of a Data Science and is used in Data analysis and Visualization in the lifecycle of a Data Science Project. In this series, presenting you a quick mind map of Statistics for Data Science. Statistics needs to be studied in the following order.

Statistics is subdivided into

1.) Descriptive Statistics

2.) Inferential Statistics.

This diagram represents the mind-map of Inferential Statistics. Inferential statistics is used to make the inferences about the population parameters, using sample statistics. For example we will use Sample Mean to calculate the Population mean. Like descriptive statistics, inferential statistics does not have any subsequent divisions, but concepts that help us to make inferences or predictions about Population parameters.

 So the first thing you need to know when you are learning the inferential statistics is the difference between

1.) Population

2.) Sample

You can find the difference  between them in detail here.

Next step is to know and understand the concept of Distribution and the types of Distribution? A thorough understanding of Normal Distribution and Standard normal distribution is required to proceed further.

Now, when you have clarity on Normal Distributions and Standard Normal Distributions, the next Concept to study is Central Limit theorem. It is very important and widely used.

After u knows what Central Limit Theorem is, you will use it to understand the concepts of Estimates such as Point Estimates and Confidence Intervals Estimates. You can learn about Estimates here.

You will also learn to calculate the confidence Intervals range and generalize the results to the Population parameters using z-test and Student – t test used for Mean. 

Next topic is Hypothesis testing and p-value about which you can read here.