Data science is a combination of statistics, mathematics, and computer science. It has blended the methodologies of statistics, theorems of mathematics, and computational techniques of computer science to incorporate them within machine learning and artificial intelligence, which is helping almost every field in the world. Whether it is aviation, medical science, information technology, or business, data science is serving as the key element by replacing conventional methods and problem-solving procedures. (Helleputte, Gruson, Gruson, and Rousseau, 2016)

In this article, we will discuss how the assimilation of data science is contributing to the various fields, especially computer science, mathematics, and statistics.

##### Statistics

The statistic is the branch of science that deals with the study and organization of data to draw concrete conclusions based on analyses. It can safely be termed as the huge leap from guesstimating that is used to be the most attainable outcome from all the technical analysis of data. Data science is comprised of statistical methods and rules which assist the process of data analysis, collection, prediction, and conclusion. The contribution of data science to the field of statistics can easily be gauged through the newly developed systems that store and structure the raw data for not only data scientists but also the independent statisticians. (Olhede and Wolfe, 2018)

After the development of compact systems of their own involving artificial intelligence and machine learning, data science is capable of assisting statisticians in the initial stages of data extraction, sorting, refining, and storage. Statisticians can today claim a share of data for their independent research work. They can populate the data for sampling and hypothesis testing. By utilizing the visualization techniques, which are also provided by the data science tools, statisticians can plot these data variables to be able to analyze the relationship and draw relative conclusions.

There are many common software among data science computations, the statisticians, and actuarial scientists for varying research and analysis purposes, e.g., Excel, SQL, and Python (Ali and Bhaskar, 2016)

##### Statistics with Medical Science

Data science has empowered statistics with the help of big data and predictive analysis in such a way that statistical theorems are being utilized in predicting disease occurrence with a high success rate. For instance, Naïve Bayes Theorem.

Naïve Bayes Theorem works best on extensive data sets, approaches, and compares the calculations with two conditions and draws a conclusion concerning either of the conditions. It transpires with the help of close assumptions, which was made possible by the application of big data and predictive analysis mechanism. It helps in knowing the outcomes of a particular disease through measurable factors. The big data, predictive analysis, and Naïve Bayes design have an accuracy rate of up to 97.12% in predicting disease. (Venkatesh, Balasubramanian, and Kaliappan, 2019)

The latest software for statistical analysis is SPSS, which is designed by IBM. This tool is capable of fraud detection, crime analysis, and can also visualize the growth of revenue and profitability based on data. It uses the methodology based on predictive analytics, which can foresee events and make dependable predictions. SPSS is also used by insurance companies to detect organized fraud early in the process and also identifies suspicious transactions and fraudulent claims. The system is so powerfully integrated with the fraud detecting techniques that it can even differentiate and explain the frauds. Thanks to data science.

Various other tools used for statistical operations like R, MATLAB, and Microsoft Excel, are being used today for data processing to study and research human behavior.

A good business runs on healthy data, which should be relative, compatible, and profitable for the business and serves it right in terms of the business environment, practices, techniques, and relevant market. Many marketing campaigns fail due to the lack of structured information to target the correct area. Data science is the new solution provider for businesses in a nutshell. It has provided a one-door solution to specific types of businesses.

Since the basic input is the data, it has to be reliable, correct, and relevant because the further processes are dependent on this data as well as the solutions and desirable business outcomes. These outcomes, in turn, are dependent on the business decisions which are reliant on the analysis being made through the data taken as input. It is a chain process, every part linked with each other. (James, 2018)

The data extraction process is a very important step for initializing the solution providing startup for a business. It is performed with the help of powerful tools. The data is collected from consumers and stakeholders in different ways. Surveys and experiments also come handy in knowing what is needed, where, and for whom it is needed. Data science is fully equipped with all the techniques to figure it out.

Feedback is very important when it comes to knowing how the business is performing. To know how the services are affecting or helping people or whether a certain product is fulfilling its intended purpose of production. Feedback is the perspective of consumers, and all businesses around the world are always keen to know how their customers perceive them.

Data science is full of those tools and expertise which answer all those concerns of a business and even propose policy alternatives that are interchangeable and helpful in terms of increasing productivity and revenue. The data relating to business reviews is mined with the influential apparatus of data science through innovative technology that is social media and web compatible.

People are asked to give feedback and review about a service or product through the applications and often through online platforms. The technology is smart enough that whenever you write something about a product or service, that data is turned into information through the system and stored for further processes. There are web crawlers that run through websites and extract data from text, images, voice notes, and even through language that is being used for a product or service. The advanced programming language, which is called Neuro-Linguistic Programming (NLP), is very popular in this aspect. It is used to detect responses and reactions through text and draws patterns of human behavior in different cases.

The data is stored as feedback, which is very valuable in knowing the reaction and views of users. This data is then analyzed and visualized to see how it performed and which key factors pulled it back in providing satisfaction to the customers. The facts and figures are studied, and favorable outcomes are predicted by applying hypothetical tests on them. This creates a conducive environment for solution orientation and how the policies can be improved or enhanced.

##### Technology Blogs

Technology blogs are a goldmine of information which are providing a massive amount of helpful content on almost every topic, field of study, research area, health care, meteorology, etc. The data sciences emerged as a bright star in the realm of technology. More and more institutions and organizations are adopting it to benefit from the big data and data science technologies.

The technology blogs help through the immense information that is being shared for the consumption of the skilled individual as well as a layman alike. These blogs are mostly dedicated to the upcoming skills and discoveries in the field of technology and science. A lot is being written to make learners familiar with the concepts and techniques of data science. Technology blogs have interactive platforms where people attached to the field of data science, such as data scientists, data engineers, and data analysts, are educating learners by explaining the concepts of this attractive field of science and innovations, news, and information. Terms, techniques, tools, and methods are being shared and discussed to keep people updated with the latest innovations, happenings, and events all around the world. (Grauer, 2001)

The top technology blogs are Reddit, Data Science by Google News, Data Science Central. There are also various websites available on the subject where you can find various active debates and discussions shared by the data scientists, engineers, and those who are using this technology in their respective fields. These technology blogs are providing an e-learning platform and career guidance for young data scientists.

A large number of conferences, tutorials, demos, informative sessions, open communication platforms, and workshops are being conducted today- to enlighten the masses about this field of science and study. They are usually hosted by universities, educational institutes, scientific organizations, and civil societies to spread awareness as well as initiate brainstorming sessions to find solutions to the problems prevailing in the fraternity.

The targeted audiences are the data scientists, engineers, analysts, and students who are interested in learning and intend to pursue it as a career. The conferences are designed for the learners and mid-career personals with the focus to present the conceptualization of data extraction, big data, database management, data integrity & security, information systems, and artificial intelligence.

##### Mathematics

Same as statistics, data science has empowered the computer systems to calculate complex mathematical problems easily. Mathematics is the field of science that is involved in our daily lives and impacts our decisions and lifestyle. Data science advanced the algorithms of mathematics and digitalized them to be compatible with computational purposes for commercial sectors like banking and finance.

It is even used in sports in the form of trigonometry and geometry, which helps the sportsmen around the world to improve their skills and techniques. The analytical tools are used to forecast a games’ scenario, the speed of the ball, and even the force and course of acceleration in a specific direction in different conditions. Data science has made learning of mathematics, both interesting and interactive. GeoGebra is a software and learning tool which is designed to teach and learn mathematics from a primary-class to higher education students on a single platform. It is very interactive in which one can visualize geometrical shapes with their equations, draw graphs, and experiment with 3D models.

##### Conclusion

The applications of data science are everywhere. It is:

• Helping in risk assessment and fraud detection in the insurance industries
• Assisting researches in healthcare and drug productions
• Business analytics in management and revenues
• Security firms in voice and face recognition, and much more.

The applications and benefits of data science are widespread and are being harvested by every industry and sector.

Although data science has been successful in being a breakthrough solution provider, there are many components which are nevertheless need to be discovered. With the advancements in machine learning and artificial intelligence, data science is all set to become the most sought-after field of study.

##### References
1. Helleputte, T., Gruson, D., Gruson, D., & Rousseau P. (2016, July). Data science, Artificial Intelligence, and Machine Learning: Opportunities for Laboratory Medicine and the Value of Positive Regulation. Clin Biochem, 69, 1-7. http://doi.org/10.1016/j.clinbiochem.2019.04.013
2. Olhede, S.C., & Wolfe, P.J. (2018, May). The Future of Statistics and Data Science. Statistics & Probability Letters, 136, 46-50. https://doi.org/10.1016/j.spl.2018.02.042
3. Balasubramanian, C., Kaliappan, M., & Venkatesh, R. (2019, July 5). Development of Big Data Predictive Analytics Model for Disease Prediction Using Machine Learning Techniques. J Med Syst, 43(8), http://doi.org//10.1007/s10916-019-1398-y
4. James, G.M. (2018, May). Statistics within Business in the Era of Big Data. Statistics & Probability Letters, 136, 155-159. https://doi.org/10.1016/j.spl.2018.02.034
5. Grauer, M. (2001). Information Technology. International Encyclopedia of the Social & Behavioral Sciences, 7473-7476. https://doi.org/10.1016/B0-08-043076-7/04297-2