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What Programming Languages are Used in Data Science? - For Beginners
Data science is a booming topic that has spread across all industries. Data science is a board that consists of math, statistics, and programming skills. As such, aspiring data scientists are expected to have a basic knowledge of programming languages. Some of these languages may look familiar to you due to their powerful libraries, but not all are created in the same way! Each language is essential for data science, and you should ideally be fluent in at least one of them.
Let's take a closer look at each language:
1. Python
Python is the most popular data science programming language. Python is not used in almost every area of data science except scientific research, contrary to popular belief.
Python has a robust library ecosystem for machine learning, data analysis, and data visualization. Python is also widely used in other aspects of software development.
2 . R
R is a statistical coding language that data scientists widely use in the scientific field. Although Python is less popular, it still has a very good selection of data tools stored in packages. My personal favorite is the ggplot 2 visualization package!
3. Structured Query Language (SQL)
Query Structure Language is the preferred query language for relational databases. The majority of data scientists and analysts use SQL to select the data they require for analysis.
Examples of SQL usage in data science are:
An SQL query from a MySQL database is written.
Using SQL to feed data into an ETL pipeline
4. Javascript
In data science, Javascript is the least used scripting language.. However, I included it because of its widespread use in data visualization and software engineering.
Javascript is well-known for its D3.js library, allowing data to be displayed in beautiful charts. This is significantly more dynamic than the data visualization libraries available in Python and R.
5. Git
As previously stated, Git is an essential language to have in your data science toolbox for version control. All scripts written in the languages mentioned above can be tracked and traced quickly with proper version control if bugs occur.
Conclusion:
To conclude, programming languages are essential skills for every aspiring data scientist. Not only data scientists, but data analysts, business intelligence, data engineers, and other positions require coding skills.
Interested in learning these programming languages for your data science career?
Enroll in a data science course in Chennai and become an IBM-certified data scientist or data analyst.
Let's take a closer look at each language:
1. Python
Python is the most popular data science programming language. Python is not used in almost every area of data science except scientific research, contrary to popular belief.
Python has a robust library ecosystem for machine learning, data analysis, and data visualization. Python is also widely used in other aspects of software development.
2 . R
R is a statistical coding language that data scientists widely use in the scientific field. Although Python is less popular, it still has a very good selection of data tools stored in packages. My personal favorite is the ggplot 2 visualization package!
3. Structured Query Language (SQL)
Query Structure Language is the preferred query language for relational databases. The majority of data scientists and analysts use SQL to select the data they require for analysis.
Examples of SQL usage in data science are:
An SQL query from a MySQL database is written.
Using SQL to feed data into an ETL pipeline
4. Javascript
In data science, Javascript is the least used scripting language.. However, I included it because of its widespread use in data visualization and software engineering.
Javascript is well-known for its D3.js library, allowing data to be displayed in beautiful charts. This is significantly more dynamic than the data visualization libraries available in Python and R.
5. Git
As previously stated, Git is an essential language to have in your data science toolbox for version control. All scripts written in the languages mentioned above can be tracked and traced quickly with proper version control if bugs occur.
Conclusion:
To conclude, programming languages are essential skills for every aspiring data scientist. Not only data scientists, but data analysts, business intelligence, data engineers, and other positions require coding skills.
Interested in learning these programming languages for your data science career?
Enroll in a data science course in Chennai and become an IBM-certified data scientist or data analyst.
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