is focused on pulling knowledge from data, which means acquiring in-depth information implementing scientific concepts to vast sets of data used to inform high-level decision-making. Taking the Covid-19 scenario, the government officials evaluate data sets accessed from various sources such as mortality rates, contact tracing, infection, and location-based data to ascertain which areas are affected and how to provide support where it is most required to curb the infection rates.
A programming language is the strength of any developer. A new programming language pops up every once in a while to deliver faster and more improved results. Developers can find themselves intermingled with various programming languages, determining which one to pick for their next project.
All programming languages are not similar, and what works well for one project might not work the same way for another. These languages are used in PCs to apply algorithms and have several applications. There are numerous programming languages for data science as well. Data scientists must learn and be efficient in at least one language as it is a must-have to realize various data science functions.
High-Level & Low-Level Programming Languages
There are 2 types of programming languages: High-Level & Low-Level.
Low-Level programming languages are somewhat less advanced and most comprehensible languages used by computers to conduct different operations. These include machine and assembly language.
Machine language reads binaries and is performed by a computer, while assembly language directly deals with hardware manipulation and performance issues. An assembler software turns the assembly language into machine code. Low-level programming languages are quicker and can memorize more than their high-level counterparts.
High-level programming languages render more robust programming concepts & abstraction of details. Such high-level languages can build code that is independent of the computer type. Also, they are movable, closer to human language, and extremely useful for problem-solving instructions.
That is why several data scientists use high-level programming languages. If you are willing to enter the field, you should ponder learning data science language to begin your journey.
Let's learn in detail about the top programming language that you must learn as a data scientist.
Top Programming Languages for Data Science
In today's era, Python is the most commonly used data science programming language. It is an open-source and user-friendly language that has been in existence since 1991. It is a general-purpose and dynamic language and is naturally object-oriented. It even supports various paradigms, from functional to organized & procedural programming.
It is one of the most well-known languages for data science as well. It is quicker and a better choice for data manipulations with below 1000 iterations. Automatic data processing and data learning become effortless with the packages compiled in Python. Also, Python makes it easier for programmers to encrypt the data in a spreadsheet by making a CSV output.
This is another object-focused programming language utilized by data scientists. Today, several Java libraries are available covering each sort of problem that a programmer might face. There are some unusual languages for visualizing data and creating dashboards.
This versatile language can tackle various tasks at once. It is even handy in implanting everything from electronics to desktop & web applications. Well-known processing frameworks such as Hadoop runs on Java. It is also one of those data science languages that can be rapidly and easily scaled up for massive app development.
It supports many machine learning libraries such as ConvNetJs, Keras.js, and TensorFlow.js.
Scala, a high-level programming language created in 2003, runs on the Java Virtual Machine. It can make working with Java way easier. Its uses range from web programming to machine learning. It is even a scalable and effective language for tackling big data. In today's era, Scala supports not only object-oriented and functional programming but also concurrent & synchronized processing.
Below are Scala's key offerings:
It is versatile, steady, and can deliver comparatively faster results under certain situations.
Comes with more than 175,000 libraries extending Scala's functionality
It is supported by many IDEs like VS Code, Atom, Vim, IntelliJ, Sublime Text, and your browser.
Offers solid community support
R is an open-source and high-level programming language that mainly handles the graphical & statistical side of data science things. R offers several statistical computing and analysis options like - clustering, time series analysis, linear and non-linear modeling, and statistical tests. RStudio and Jupyter are the third-party interfaces that make it easier to work with R.
Moreover, R renders superb extendibility, allowing other programming languages to alter R's data objects without much hassle. But, it has over 1000 interaction and is more intricate to learn than Python.
Structured Query Language (SQL) has become a well-renowned programming language for handling data over the years. Even though not uniquely used for data science operations, knowledge of SQL tables and queries can help data scientists manage database management systems. SQL caters to a crucial purpose and gives you the facts and stats from a vast data pool.
Julia is a data science programming language and is developed for high-performance computational science and quick numerical analysis. It can instantly apply mathematical concepts like linear algebra. It is also an excellent language to deal with matrices.
Julia can be used for front-end and back-end programming, and it's API can be implanted in programs. Some high-profile businesses can also use this programming language for various tasks, including risk analysis, time-series analysis, and space mission planning.
Today, there are over 250 programming languages worldwide. This article has covered 6 top programming languages for data science. Of these 6, Python evidently emerges as a winner with more than 70k libraries and 8.2 million users across the globe. Python allows integration with SQL, TensorFlow, among other machine learning and data science libraries. Python's fundamental knowledge helps you pick computing frameworks like Apache Spark, famous for its big data and data engineering analysis tasks.
It is crucial to learn a programming language before becoming an expert in this field. Data scientists should keep in mind the advantages and drawbacks of different programming languages before making a final decision.