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Basic Statistics For Data Science- Types, Concepts & Much More

StarAgilecalenderJanuary 30, 2022book18 minseyes2276

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If you are wondering about an exciting and amazing career as a data scientist then first you need to familiarize yourself with basic statistics for data science. Data science is an art that can be truly understood by the concept of statistics. There is no data scientist who can perform well in their field without a strong grip over the basic concepts of statistics. So you do not need to worry about this as we are going to explain the role of statistics in data science and what are the basic concepts that you should be aware of so that when you are all ready to study those concepts, you can have a good career path for yourself and become ready to enter this world. With the right Data Science Training, you will be able to hone these skills and make sure that you can apply all your knowledge to real-time problems.

What is the role of Statistics in Data Science?

Before jumping to the concepts used in statistics for data science, we are going to know the role it plays so that we have a better understanding. When you are using various ways to collect the data, analyze it and use it for various purposes, those methods come under statistics. It is regarded as the fundamental tool which is used by data scientists all over the world to have various kinds of findings which they discover by gathering and then analyzing a large set of data from the specific field.

Data is a very powerful tool for any organization and when the organization is well equipped for collecting the data and finding the various trends and information from that data, it helps the company to become successful. This is the role of a data scientist. They use various concepts and formulas in statistics to find those trends and information and they provide helpful insights to the organization to work upon. This way, understanding the statistics in the data science field will help the company. Therefore, a data scientist should have a fundamental knowledge of these tools so that he can perform well as per his role. So, we are going to give you information about the basic concepts in Statistics used in data science below so that you can expand your knowledge and Learn Data Science.

Fundamental concepts of Statistics in Data Science

Descriptive Statistics

The ways that are used to represent and describe the data and analysis done, come under descriptive statistics. Using these, the graphical or numerical representation is done and various identifications and analyses are done. It provides the basic features of the data-driven and it is very helpful in representing the data in a very meaningful way to understand. It shows what the data is actually. There are various concepts that are included in descriptive statistics. It includes- Normal distribution which can be understood by a bell curve, a central tendency that contains various concepts like mean, mode, and median, variability in which the data set is divided into quartiles, variance, standard deviation, and modality. These are used to find the information from the given set and are widely used in data science.

Probability and Probability Distributions

Probability is the chances of something happening or not. Generally, it is between 0 and 1 where 0 defines the event won’t be happening at all, and 1 defines that the event will definitely occur. There are various things that we take care of when we are calculating probability. There is a conditional probability that is calculated when the events are related to each other and their probability of happening is related to each other. Independent probability happens when the two events are independent of each other. Also, two events are mutually exclusive when they cannot happen at the same time. If we talk about a probability distribution, it is the function that is used to represent all the possible values of any event happening in the experiment. It helps in specifying all the possible events that may occur. There are three types of functions that are defined in probability distribution:

  1. Probability Mass Function
  2. Probability Density Function
  3. Cumulative Density Function

These are some of the very important concepts that you will be using in your data science career. If you wish to learn more about them, then you should start looking for the best Data Science Course which will help you get an in-depth knowledge of these concepts.

Regression

If the work demands to find the relationship between one or two independent variables and one dependent variable, the regression is used.  There are two types of regression which are defined below:

Linear Regression: Using this approach the relationship is being found between one independent variable and one dependent variable. In this kind of setup, the independent variable is being controlled in the scientific experiment and then the results on the dependent variable are being calculated.

Logistic Regression: In this approach, the experiment remains the same but instead of one independent variable there are two independent variables and their effect is being calculated on the dependent one.

There are various ways that can be used for calculating linear regression, and in the course, you will learn all about the step-by-step calculation of linear regression.

Over and undersampling

The best way to solve the classification problem is by using the concept of over and under-sampling. It is the best tool when there is a lot of data and information on the one-sided and it is tipped heavily as compared to the other one. The over and under sampling approach fits perfectly in this. Using under-sampling, the relevant amount of data is being taken from the majority case while there are many examples from the minority case to balance it out. In this, the probability distribution is maintained. And if we talk about oversampling, in this the copies of minority data is being created so there are the same number of examples from both the cases and accurate analysis can be carried out with the right information. In this, the number of copies that are created is made keeping the overall distribution of class the same. This is very helpful and useful results can be drawn using this.

Using techniques like Synthetic Minority Over-Sampling Technique (SMOTE), this technique can be used. 

Bayesian Statistics

Frequency statistics are frequently used for calculating probability but in the places where the frequency distribution fails; there is the rise of the Bayesian theorem. It is based on Baye’s theorem and it is widely used. It might be confusing for the people who are recently coming to learn this, but with time and information available for this, this becomes an important tool. There is information related to the distribution of an object or an event, but when the new information arrives, it is perfectly combined to create a new probability distribution system which is known as posterior distribution. This is more used when the prior information or data you have cannot be properly used for your future representation and you need to add more information to get accurate results.

Along with the various concepts that are mentioned above, having a good knowledge of the type of analytics will also help a lot and data scientists can have a better understanding then. There are three types of analytics:

Descriptive analytics

It often contains the visual representation of data in the form of line, line, bar, or chart form. It is not used to make any predictions in the future as it is the representation of the events and things that happened in the past. It is the core of the reports which can be used to have information in hand. This can give an insight into what information is there in the organization.

Predictive analytics

Referring to the more advanced method for analytics of data, this is used to make predictions from the data already driven. It uses various concepts from the statistics like the probability to calculate the possibility of an event happening. It includes machine learning and various algorithms that are very widely used nowadays and more and more companies are looking for analytics that could help them in predictive analytics.

Prescriptive Analytics

Using these kinds of analytics, the companies can have the results of the actions that they want to take for their future. This involves the use of various statistical methods from mathematics and also from computer science to drive results. The data is collected from descriptive and predictive analytics and then it is used for decision making for the company.

Final word

Well, the world of statistics is huge, and when you want to learn you are going to have so many interesting things always in front of you to learn. A statistics basics for data science is always helpful when you are working as a data scientist. So in this article, you have seen how the statistic is helping data scientists to resolve various issues and come to a better decision with the use of data available. These are just some of the basic concepts of statistics. These are very helpful and you should learn in-depth about them as they are very useful for your career.

So after reading this article, you have developed a curiosity to learn these and want to have an amazing career in data science then we have just the right platform for you. With StarAgile, you are going to find the best data science courses that have the best teachers for you. You will not only gain the theoretical knowledge of these concepts but have plenty of examples to hone your practical skills. So do not waste any more time and head straight to the best course available for you and give your knowledge a boost with Data Science Certification and shine further.

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