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Is Python THE MOST PREFERRED LANGUAGE FOR Data Science?

Today, variety of programming languages are available, but choosing which language to learn is very difficult. Some works for software engineering, some for data science, and some for creating games.

There are 2 types of programming level:-

Low level programming language

High level programming language

Low-level programming language is the one understandable language mainly used by computers to perform there operation. This language is faster and and more efficient than high level languages. Like, Machine Language.

Machine Language is consists of binaries which can easily read and execute by the computers. For converting into machine code an assembler is required called assembler software.

 High-level programming language has a strong relation with the details of computer. This programming enables the programmer to create code which is independent of the type of computer. These are more familiar to most of us. Examples like, Python, Java, Ruby, and many more. Most of the programmer use High-level programming languages, including data scientists.

Programming Languages for Data Science

 Python

Today, most of the data professionals are using Python.  As it is a general purpose and dynamic programing language, so Data scientists and programmers like Python.  This seems tobe more preferred for data science over R as it ends up faster than R and also its data manipulation. This language is having good packages for natural language processing and data learning and also object-oriented.

R

This is not an easy language to learn, and most people find that Python is easier. Actually, R beats Python by using the lapply function. R was built by statisticians and reflects this in its operations. Data science applications feel more natural in Python. It is an open source language and software for statistical computing and graphics.

Java

Java is an another general-purpose, object-oriented language. This language is very versatile, being used in embedding electronics, in web applications, and desktop application. Data scientist need Java,and frameworks like Hadoop runs on the JVM. These frameworks constitute of the big data stack. Java have a number of libraries and tools for machine learning and data science, it is easily scalable for larger applications, and it is fast.

Hadoop

Hadoop is a processing framework that manages data processing and storage for big data applications running in clustered systems. This allows storage space for huge amount data with processing power with the ability to handle virtually limitless tasks at once.

SQL (Structured Query Language)

It is a domain specific language used for managing data in a relational database management system. SQL is like Hadoop for managing data, but of data is different. Tables and queries of SQL are tough for every data scientist.

Julia Programming

It is an another high level programming language and also designed for high performance computational science and analysis. For web programmng in both front and back end.  This language is faster for Python because as it was designed to implement mathematical concepts like metrics, algebra. While creating programs Julia provides the speedy development of Python, so, it run fast as C programs.

Scala

It’s a general programming language which provides support to functional programming, object oriented programming. Scala was designed to address many issues of Java. This language has different uses for web applicationhs to machine learning, whereas it covers front end development. The language is known for being measurable for handling big data as well.

In today’s life Python is the one most widely used programming language for data scientists. This language allows the integration of SQL,Tensorflow and many more functions for data science and machine learning. Python has also allows a programmer to create CSV files as output to easily understand data in spreadsheets.

From my view point, newly aspiring data scientists should go for first to learn and become master Python before going for any other programming language.

We NearLearn, provides the Best Machine Learning Training with Python in Bangalore, along with Blockchain Training, Reactjs training, Artificial Intelligence, Data science, Corporate Training, and many more.

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Artificial Intelligence

And what is AI?

 The theory of Artificial Intelligence is that computer systems can be used to operate tasks that would normally desire human.

These can be from communication understanding and conversion of those different languages, through to visual feeling and even decision making.

Anything can be treated as Artificial Intelligence if it includes a function of creating something that we would normally think would build on the intelligence of a human.

So, how this is achieved is not the matter – but the fact that it can be done, is an idea of artificial intelligence.

Advantages of AI-:

1)      Dealing with everyday tasks- Its potential to complete regular tasks through complex automation that increase production. This also increases people’s creativity.

2)    Faster Decisions- Intellectual Technologies can help to take instant decisions and quicker actions.

3)    Avoids Errors- Mistakes were naturally occur by humans. Computers, do not make these kinds of mistakes, so assuming they are programmed properly. Through Artificial Intelligence, data will process without errors.

Disadvantages of AI-:

1)    Loss of Jobs- Artificial Intelligence is having many low-skilled jobs available. As, robots have already taken many jobs, or maybe now it extent to some new levels.

2)    Lack of judgment calls- While making decisions, AI is not able to do creative calls in respect of decisions, whereas Humans have great initiative sense for making decisions.

 AI’S IMPACT IS EVERYWHERE- THE FUTURE

Technology is increasing speedly, and Now, we have more power in our than earlier. Artificial Intelligence (AI) is engaging the concept of science fiction for decades. In the last few years, scientists have made progress in “Machine Learning”. It’s a kind  of “Deep Learning” which allow machines to regulate data for themselves on a very refined level, granting them to behave complicated functions.

Here are 6 ways AI might affect us in the future:

v  Transportation: Although it could take a long period of time to perfect them, but autonomous cars will one day help us to move from place to place.

v  Manufacturing: AI powered robots work beside humans to perform a fixed matter of tasks like assembly.

v  Healthcare: With the AI, diseases are more correct and accurately diagnosed.

v  Education: Books are digitized to presence of AI.

v  Media: Journalism is utilizing AI, and also continues to benefit from it.

Customer Service:

Last but hardly least; Google is also using an AI assistant that made easier for the people to make appointment with someone through the help of Google.