Introduction
In this article, we will learn about.
- What is Data Science
- Top Programming Languages advised for practicing Data Science
What is Data Science?
Data Science is a field that enables users to get the best insights of data that are being delivered to them which may be used to make any decision to analyze performance or to predict some risk or any outcome based on certain criteria. The data has to be structured in a way that it may be utilized in a proper manner so that the required algorithms can be applied.
Data Science is an interdisciplinary unified of statistics, data analysis, and machine learning with algorithms to make sure the data that is being delivered is utilized at its full potential.
![Data Science]()
Top Programming languages advised for practicing Data Science
Selecting a programming language is one of the most important tasks that need to be addressed. Here, we will be discussing the programming languages that you should check once before you consider practicing Data Science. The programming languages are as follows.
Python
Python is one of the finest programming languages and one of the very first to choose from the list when it comes to practicing data science. Python provides huge support when it comes to libraries or packages to make sure that the operations done on data science projects are utilized well. Not only that, but Python has a huge community when it comes to developers data analysts, and data scientists.
R
R programming language is very unique and it's one of its kind, it gives you the feature of being a vector language too. R is a statistical language mainly used in the biology sector or genetics. It provides support for Robust Visual Library, and with R you can even develop Web Apps. One of the most important things is that it involves R packages, which is a general collection of R functions, or compiled code.
Julia
To be frank, Python and R are absolutely one of the prior choices for data science, but Julia on the other is becoming a popular choice for data science enthusiasts. It has a speed like C and allows the usage of tools like of Python and R.
![Python]()
Now that we have an introduction to data science and the advised programming language for data science, it's really important for us to understand the depth. Data science isn't about getting the data and making decisions straight away, it includes practicing cleaning, structuring, and extracting the data and applying a suitable algorithm so that the proper importance of the process is served. Cleaning data is very much important as it may include repeated data or even missing data, which may bring problems in deriving the optimum data results or even reduce the accuracy level, which at the end of the process Is not desirable as it will introduce disturbance for the decision-making process. Structuring is also a very important task as data when it's available it is possible it may be in the form of mixed because the data could be more unnecessary data fields even unstructured way, doing operations on it will dilute the performance of algorithms and also time to derive into outcome will increase gradually. Extracting the required data from the data provided and applying the needed operation is one of the important tasks and this mainly includes practicing the domain as it involves extracting the required data in the initial stage.
Conclusion
Every day, data is increasing, and so are the requirements of data scientists, because today's data is a treasure for tomorrow. Understanding data helps the company to serve customers better, and thus the customer experience, be it an enabled chatbot or even a recommendation system, and the example list goes nonstop. Even Harvard Business Review states that data science is the sexiest job of the 21st century.