With the dawn of the era of big data, the needs of an extensive storage system also arise to focus on finding solutions to store data and building a framework for them. Although the solution to storage has been found with the help of Hadoop and another framework, the focus is now on the processing of data where the role of data Science comes into prominence.

About Data Science

Data science is a simple yet wonderful concept of the intermingling of various algorithms, tools, and principles related to machine learning with the main purpose to reveal useful information from the raw data. The primary use of data science is to make predictions and decisions in statistics using:

Predictive Causal Analytics

It is a model which predicts the possibilities of occurrence of a particular event in the near future. One notable example is building a model on payment history for people by predicting the future payments will be in the given period or not.

Prescriptive Analytics

This model is utilized for not only predicting but also suggesting the range of actions and outcomes associated with it by taking its own decisions. Its example is the self-driving car of Google which collects data, brings intelligence to it and uses this model to run algorithms to allow the vehicle to take their own decisions such as when to take turns, speed up/down, etc.

Prediction Making Machine Learning

It is the best model for determining the future trend for transactional data for a company such as fraud detection model works on this using a history of fraudulent purchases.

Pattern Discovery Based Machine Learning

It is an unsupervised model which uses an algorithm called clustering, which makes predictions to discover hidden pattern within the given data set. Data science utilizes more of machine learning and predictive causal analytics with a focus on both present and future results.

Languages To Excel In Data Science

In order to become a master in data science and jump to and from within the given problem to find its solution, learning “building block” languages would be beneficial. It includes general purpose Language such as Python and statistical computing languages such as R. For becoming a competent data scientist, getting familiar with the given language instead of becoming expert is enough to research your questions, debugging the code and tacking concerns related to it in a simple manner.

What is Python?

Python is a high-level and opens sourced Programming Language which consists of syntax and code readability to help the programmers to convey any concepts within a few lines made of codes. It consists of a vast library that provides a helping hand to the data scientists by providing them tools that are ready to use and enhance their work at a faster pace.

Why Python Is Important In Data Science?

In a very short duration, Python has become a popular programming language among various data scientists. It has become a popular option among them to handle big data in a significant manner. There are multiple reasons which have made this language as the best choice among the programmers and developers:
• Easy to use
• Easily Integrate with other Programming Languages
• Scalable
• Future-oriented
• Huge Community Base
• Important for Deep Learning
• Provide better analytical tools
All these features have made this programming language popular among data scientists. Besides, they are now opting for the most excellent Python development services so that the scientists would be able to achieve more in a minimum amount of time.

Conclusion

Data science is a broad concept for better storage and processing of the data to extract valuable information from it and make accurate predictions. It runs on various programming language notable of being them is Python. It has become a top choice for multiple data scientists due to its various features. Therefore, it has been suggested to hire Python developer who is expertise in their field and would help the data scientists to accomplish their projects in a minimum period.

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