Which Programming Languages to Learn for Data Science?

With the growth of Data Science, it is important to know Programming Languages to Learn for Data Science.

Firstly, what is Data Science? Data Science is a blend of algorithms, tools, and machine learning principles. It helps humans to make better decisions.

Companies invest a lot of money in Data Science, so they can get right information which ultimately helps them in making right decisions. Data Science is the future of Artificial Intelligence.

Pillars of Data Science

Top 5 Programming Languages for to Learn for Data Scientists 2020

Data Scientist is a person who knows how to extract meaning from data, how to interpret data. It requires tools and methods from machine learning and statistics.

Data Scientists are able to code. There are many programming languages for data science. These languages are essential to realize many data science functions.

Following is a list of top 5 Programming Languages to Learn for Data Science, which every data scientist should learn:

·         Python

·         R

·         SQL

·         Java

·         Matlab

1.      Python Programming Language

Python is suitable for projects that involve quantitative and analytical calculations especially in Finance field.

Forecast Watch analytics use python to work with weather data.

YouTube and Google incorporations use this language to improve internal infrastructure.

Python language is used for Artificial Intelligence development. 84% of data scientists are using python as the main language.

2.      R Programming Language

R was the highest-paid technology in 2014. 70% of data miners use R language. It has 2 million users across the world.

The strength of this technology is statistics. Built-in functions are used with R language that allows visualizing any data (i.e. structured and un-structured data).

R is suitable to create a credit card fraud detection system. It is also possible to create sentiments analysis model that gives insights on what users think about a service or a product.

3.      SQL Programming Language

SQL is used to manage data in online and offline applications. It is the key tool for working with big data. SQL has analytical and transactional capabilities. For a data science specialist, SQL skills are key requirements.

4.      Java Programming Language

Java is a high-performance language and is used to write machine learning algorithms.

For Big Data and IoT, Java is a good language.

Java language provides great security features and can be used to manipulate sensitive data.

It is suitable for projects that involve integrating the created product with existing solutions.

5.      Matlab Programming Language

It is the best programming language for data science. Matlab is used for image processing, data analysis and mathematical modeling. It is highly specialized to work with Big Data.

It is suitable for projects that require strong arithmetic support such as signal processing. This technology can also be used in educational and industrial sectors for solutions.

Conclusion

Selecting a language for your project depends on:

  1. Your prior experience with programming and data science (math and stats).
  2. Domain of project and the extent of scientific and statistical processing required.
  3. Future scope of your project
  4. The language that is most widely supported by your team and organization.

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