A programming language is a formal language that consists of a collection of instructions that produce different types of output. These languages are used to implement algorithms in computer programs and have a wide range of applications. There are also various programming languages available for data science. Data scientists should study and understand at least one language because it is necessary for a data scientist to perform numerous data science duties.
Although data science is constantly evolving, and to be with the trend you need to be proficient in several technologies in the industry. Almost Every organization has rich data that, with the assistance of a qualified data scientist, may improve the way they do business. Also, a data scientist will be able to identify different techniques that aren't performing well.
So, If you want to become a data scientist, you should first need to learn these top programming languages :
Things to Consider While Choosing The Best Programming Language In Data Science
Several factors must be considered when selecting a programming language:
- What types of data science tasks will you be required to complete?
- What are your company's goals?
- What programming languages are you already familiar with?
- What level of difficulty are you willing to take on?
- What are your educational goals?
Top Programming Languages in Data Science
R Programming Language
R is an open-source programming language used for statistical computation, data analysis, and machine learning, it is a high-level programming language specially built by statisticians. Also, R is extensively used by data scientists and researchers, also it is being adopted by popular companies such as Google, Facebook, and Twitter for data analysis and statistics. In short, R has quickly surpassed several other programming languages to become one of the most prominent languages in data research.
Python is the world's most popular data science programming language. This dynamic and general-purpose language is naturally object-oriented. It also supports a variety of paradigms, including functional, structured, and procedural programming. That's why python is one of the most used programming languages among data scientists. Python can be easily used for data manipulations. Moreover, it has support for lots of libraries like SciPy, NumPy, Matplotlib, and Pandas. Also, most programmers prefer python because it makes it easy to read the data from a spreadsheet by directly creating a CSV output.
Also, Python's versatility is one of the characteristics that make it different from the competition. You can build solutions for a wide range of use cases if you have Python in your toolbox.
SQL, or Structured Query Language, has grown rapidly and become popular as a data management programming language. Although SQL tables and queries are not exclusively utilized in data science activities, they can assist data scientists when they are interacting with database management systems. This domain-specific language makes it exceedingly easy to store, manipulate, and retrieve data from relational databases.
Scala is one of the popular functional programming languages. It is powered by the JVM. It is an excellent choice if you frequently work with large amounts of data. Because of its JVM heritage, it works well with Java in data science. Remember that Scala was used to create Apache Spark, a popular cluster computing platform. Scala is a smart choice if your data science projects will revolve around Spark.
Julia combines the advantages of Python, Ruby, and R with the speed of C and incorporates familiar mathematical notation similar to Matlab.
We can say that Julia is a language that is totally enough for general programming while excelling in certain areas of computer science such as machine learning, data mining, and distributed parallel computing.
One of Julia's key advantages is its speed, which is similar to languages such as C, Rust, Lua, and Go.
Julia excels at data science because :
- Mathematicians will find the language easier to master.
- It employs a syntax similar to that of non-programmers' arithmetic formulas.
- Automatic memory management with manual garbage collector control.
- Out of the box, it is optimized for machine learning and statistics.
- It has dynamic typing, almost like a scripting language.
- Several Julia libraries for interacting with your data
C++ is used to create several apps and OS, that's why most data scientists prefer it.
Data scientists favor easy-to-use and debugging languages like Python or R because they don't want to waste time solving some errors in C++.
C++, on the other hand, plays an important role in data science because many libraries used in other languages are created in it. It takes computational work to create a machine learning model, thus utilizing an efficient language like C++ makes sense.
It can be the best choice if you wish to work in the data science sector by creating libraries for other languages.
Ruby is the programming language we can frequently use for processing text. Developers have also used it to test prototypes, develop servers, and perform other standard tasks.
You must always analyze your employment requirements before choosing a specific data science language. R, for example, is used in the banking industry to create stock market models and anticipate share values. Python is used by programmers in the retail industry to create recommendation engines that provide appropriate choices to customers.
Python is obviously one of the most popular programming languages available right now, with over 70,000 libraries and around 8.2 million users globally. Python supports TensorFlow, SQL, and other data science and machine learning libraries. Basic Python expertise also aids in learning computer frameworks such as Apache Spark, which is well-known for its data engineering and large data analysis jobs.