# Must-Know Statistical Concepts for Data Scientists

Want to get started as a Data Scientist? This article talks about some of the top basic statistics concepts for all those who are interested in the journey towards Data Science.

Who are Data Scientists?
Top Basic Statistics Concepts for Data Scientists
Concluding Lines: How to Learn Data Science

Who are Data Scientists?

A Data Scientist is one who is responsible for analyzing, processing, modeling, and then interpreting the results to create desirable plans. In simple words, we can say that they work closely with data and know how to extract and interpret data. They hold a strong knowledge of computer science, statistics, and mathematics and use their extensive knowledge to open solutions to business demands.

Data Science developers and scientists are in high demand these days. If you want to become one, you are just a click away!

Top Basic Statistics Concepts for Data Scientists

If you have already decided to take your career in this domain and become a Data Scientist, you need to study and learn statistics and its concepts. So let’s have a look at a few basic statistics concepts that every data scientist should know.

Probability Distribution

It is a function that depicts the probabilities of the outcomes or possible value in an experiment. In the data science context, the value of probability ranges from 0 to 1, where 0 indicates that event will not occur, whereas 1 indicates that event will certainly occur. In order to understand probability distribution, one needs to understand these three basic terms.

Uniform distribution

A uniform distribution is a type of distribution of probabilities where all outcomes have the same probability that it will be the outcome. We can consider it as a representation of definite variables that can be either 0 or 1.

Normal Distribution/ Gaussian Distribution

Also known as Gaussian Distribution, this distribution is defined by its mean and standard deviation and looks like a bell-shaped where the curve’s peak designates the most likely value the variable can take, and as we move away from the curve’s peak, the probability decreases. In other words, we can say that data that is near the Mean is more frequent in occurrence compared to the data which is far from the Mean.

Poisson Distribution

Just like Normal distribution, this has a comparatively uniform spread in all directions at the time of low-value skewness. Such distributions are used to find the probability that an event might happen or not knowing how often it ordinarily occurs. Also, such distributions can be used to predict how many times an event might occur in a given period of time.

Dimensionality Reduction

Dimensionality reduction is the process of reducing the dimensions of your data set, or in other words, this reduction process reduces the number of input variables in a dataset. This mechanism helps in fixing problems that occur with data sets in high dimensions that don’t exist in lower dimensions. This mechanism offers various potential benefits, such as faster computing, fewer redundancies, and more accurate models. Therefore in order to become a skilled professional in the data science domain, understanding this mechanism is crucial.

Measures of Central Tendency

Central tendency is the central value of a probability distribution, and Mean Median and Mode are the common measures of central tendency where Mean is the average of the values in the series, the Median is the value in the middle, and Mode is the value that appears most frequently.

Variance and Standard Deviation

Variance is a measure of the variation among values where a larger value means that data is more spread out from the mean, and the smaller value indicates that data is more concentrated around the mean.
It can be calculated by the formula:

Standard deviation is the square root of variance.

Oversampling and Undersampling

Oversampling and Undersampling is a technique for imbalanced classification. There are two main ways to perform random resampling. One is Oversampling, in which samples are duplicated from the minority class, whereas the other sampling is Undersampling, in which samples are deleted from the majority class.
In Oversampling, we will create copies of our minority class to have the exact number of examples, similar to the majority class. Whereas in Undersampling, we will select some of the data from the majority class, using as many examples as the minority class.

Concluding Lines: How to Learn Data Science?

If you are working or plan to work in the field of data science, it is crucial to understand the above-mentioned fundamental statistical concepts along with other advanced level topics such as neural networks, machine learning, and R programming.

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