Statistics can be a very powerful tool which can help you perform better especially when you are aiming for data science. Statistics is the basic use of maths which can help you perform the analysis of the data. It is a basic visualisation of a bar chart which can help give high level information. These statistics are generally use to make sure that they involve some help which can help concentrate on the conclusion which can help work on the data rather than just an estimation. Statistics is one of the those subjects for students where they ask someone else to do my statistics assignment for me. But statistics is a subject that can give you a deeper insight which is much more finer. This involves creating or generating data which is structured on the basis of data sciences which can work on techniques where they can get more information.
Statistical features is one of those subjects which can probably used to understand the statistical concepts in data science. It is the first stat which one applies to explore the dataset which generally includes things like bias, variance, mean, median, mode, percentile and others. These features are generally used to describe a few simple statistical features which are much easier to calculate. These statistical features are much more quick yet informative for you whole data.
This is a concept which has a probability or chance that the event might occur. It commonly quantified within the range of 0 to 1, this is a possibility that the 0 represents that the event would not occur but 1 mans that there is a certain possibility that the even might occur. This probability distribution is a function which can help represent the probabilities of all the possible values. There are three probable distribution.
- A uniform distribution is where the single value occurs in a certain range while anything out the range of Zero can be off.
- A Normal Distribution is a very commonly used Gaussian distribution which can be defined by its mean and standard deviation.
- A poisson Distribution helps understand and add the factor of skewness.
Dimensionality Reduction is quite an intuitive process to understand. In this, we see that the dataset can be reduced with the number of dimension. There are also data science which can help understand the right feature variable. This is one of the most common statistic techniques which allows to decrease the dimensionality. PCA is creates a vector which helps represent the features which shows how important they are. They also can be used to understand the dimensionality reduction and style .