Top 10 Programming Languages To Must Know For Data Science In 2021
Data science is still a field of new things. It’s in high demand and profit-generating in the market. AI and Machine Learning are also good sources of profit. If you are a data scientist, you should know various programming languages. It works with intelligent models that have algorithms to run. Now, to understand algorithms, you should know programming languages.
Before we start with the best programming languages for data science, let us know a little about data science.
What Is Data Science?
The name “Data science” was founded in 1962 by John Tukey. He called it data analysis. Data science uses multiple programming languages for scientific methods, processes, algorithms, and systems to get results and insights from the desired statistics. That input is used in a wide range of applications. It is used in data mining, machine learning, and big data.
Data science is a study to process extensive data with the latest tools and techniques. Mathematics, statistics, computer science, and information science are a technique of data science.
Now, let us know the best programming language for data science and why they are essential.
The 10 Best Programming Languages For Data Science
- Python
Python is the most popular among data scientists because of its broad range of uses. Python is generally the first choice among other programming languages in machine learning, artificial intelligence, and other technology.
Python has powerful libraries which are helpful for data science, such as TensorFlow, Keras, scikit-Learn, and matplotlib. Python is valuable in big data for input collection, modelling, visualization, and other related tasks. If you have a question, Python has an answer, and it has a large community for various support. So, it all makes Python an essential programming language for data science.
Python is best in automation, data science, and automating tasks; that’s why Python is essential in data science. Because of automation fluentness in Python, Python is the best programming language for data science.
2. JavaScript
JavaScript is a well-known programming language among developers. It is used for web development and mobile applications. We know that JavaScript is the best in data visualization. Data visualization is made by JavaScript libraries like D3.js, Chart.js, Plotly.js.
JavaScript is also best for product integration and ETL. Data scientists rely on product developers, and they closely work with them. JavaScript is best for filesystem operations with Node.js. Node’s file system module “fs” gives an excellent API for filesystem operation synchronously or asynchronously. MongoDB is a great database system to work with extensive input.
Along with the advantages of JavaScript in data science, JavaScript seems to be the best fit for it; hence it doesn’t have packages and functionality. The best advice is to go with Python that we discussed earlier, or Go with R that we are going to discuss in the next topic.
3. Java
Java programming language is used by top businesses also used for securing enterprises, machine learning, data science and data mining. Currently, the advantages of Java in automation and businesses are at the next level. We know that Python and R are favorite programming languages for data scientists, but some companies head to Java to acquire some functions.
Java is the oldest programming language known by every developer. So, the older the language, the more professional the developers are. Java’s ability to integrate and to compliable with any other components is at an appreciable level.
In big data, most of the tools are written in a java programming language. However, frameworks are also written in Java, so it is fundamentally compatible. So, Java is usable in some field of data science like deep learning, data visualization, data analysis, NLP (Natural Language Processing), and data import and export. Developers can also acquire Java virtual machines for data science. Its benefits are saving time by writing identical code for multiple platforms.
Also, Java makes it easy for extensive data processing in data science as it is a strongly typed programming language. So, if we compare Python with Java, Python is an easier to read, more arranged language. However, if developers are experienced with Java, then it can be the best programming language for data science with little more complexity.
4. R
R is free, and it has an open-source software environment. R is not that popular, but nowadays, it is steadily increasing its popularity. R programming language is mainly used for graphics and statically computing. Data science also uses statistical computing for import/export data, data processing, and data analysis.
R can manage large and complex data sets, but has a downside of security and can not be used for web application but can be used for machine learning operations.
5. C/C++
C programming language is the older programming language, and it’s best for data science. The newer languages use C/C++ for the codebase. C++ has the ability to compile inputs quickly because it is helpful in data science.
Compiling data is helpful in various applications, and C++ is a comparably low level of hardness, so data scientists can easily use the deeper object and aspects of a data scientist. For more significant projects in data science, C++ is suggested because its performance is good.
Because C++ needs a moderate level of effect in complicated data science workloads, C++ is suitable for big data uses. But we cannot say it’s the best programming language for data science, but it is a comparably functional language because of its low-level codebase.
6. SQL
SQL (Structured Query Language) is used for programming and management of data in RDBMS (Relational database management system) and RDSMS (Relational Data Stream Management System). SQL is also used for handling structured input. SQL is also an old language; that’s why it has a robust structure.
At some point, SQL makes it hard to use for some users because of its user interface. If you go for particular versions, they are not free and also, there is no surety that you will be given full access to the control database.
7. MATLAB
MATLAB is a programming language and also a numeric computing environment. MATLAB is used for matrix manipulations, plotting data, implementation of algorithms and making of the user interface. But MATLAB’s primary use is numeric computing. Because of numeric computing, MATLAB is helpful for data science.
MATLAB is specifically valuable for data science because of its deep learning toolbox. MATLAB is slower than others because of compiled language. We can not put it into its best programming language for data science because it is expensive to use. It can be used to perform other technical tasks.
8. Scala
Scala is a strongly typed programming language that is object-oriented. It first appeared in 2004 and gained popularity steadily. The main benefits of Scala to use for data science is that it runs on the java platform. So, it is compatible with existing java apps. We saw in Java that Java is an older language, and it is a robust language.
Scala is suitable for many datasets while compatible with Java, which opens opportunities for current and future supports. Scala has concurrency support that makes a great choice to use it on building data science frameworks.
Scala is easy to use in data science and will be easy for those familiar with Java. Java is a base in Scala, so it is scalable, and it works well in data science for data analytics.
9. Julia
Julia is a high performance and dynamic programming language released in 2012. It is a general-purpose language used to write any application. Julia has many features that can provide excellent benefits for data science, like Julia supports numerical analysis and computational science. It is effortless to use because of the low level of programming.
Julia can be used for data visualization, deep learning, statistics analysis and multi-dimensional datasets. It is usable in data science except for its community which is not significant. If you need an answer, then it’s harder to find quickly. It is recommended to Go for Python or R if you’re not very well versed with Julia.
10. SAS
SAS is the oldest statistical analysis tool which was released in 1976. SAS is a statistical software used for advanced analytics, multivariate analysis, business intelligence and predictive analysis. SAS is written in the C programming language, so if you are familiar with C, then the SAS will be easier to use.
Major companies are using SAS for analysis, but some disadvantages make this tool not usable. SAS is software, so it can not be used everywhere like a programming language. For any deeper level analysis task, SAS is incompatible with it. Another disadvantage is that SAS needs a license to work; the SAS tool is unusable without a proper license.
Conclusion
Data science is a high demand stream. Proficiency in programming languages is a must before entering into the data science stream. After observing the above article, you may get an idea about the top 10 languages for data science. But among those, the best programming language for data science in Python, without any doubts.
If you have any questions, you can get in touch with Latitude Technolabs. We have an experienced team that is ready to help.
