Essential Programming Languages to Know for a Career in Data Science

The ability to code has become an integral part of any Data Science role. While anyone working in the data domain doesn’t have to be a pro at programming, the use of languages such as Python and R for data analysis has made them key skills to have.

Even if your primary responsibility is that of a data analyst, you might be required to pre-process data and transform it. More importantly, if you wish to be a data engineer or a data architect, you definitely have to know how to code in relevant programming languages.

Foundational programming skills are definitely important not only for a data scientist but several other professionals who work with data or data-driven technologies. There are many instances where financial analysts must use R and work on RStudio, the integrated development environment (IDE) for R.

Python is definitely one of the most popular choices for Data Science as it has a huge number of libraries available that promote its use in Data Science and provide a host of necessary functions. For instance, you can simply use SciPy for scientific or mathematical tasks. If you wish to build Machine Learning models and applications of AI such as Natural Language Processing, Python is again probably the perfect fit.

Languages such as R or Python for Data Science and AI are preferred by most, but many data scientists and researchers also use MATLAB or Scala. Meanwhile, core developers who need to build data-driven technologies or software find C++ or Java to be a great fit. It is mainly because these languages have more powerful processing capabilities; these languages are also older but not as simple to learn such as Python which has an incredibly easy syntax.

However, R is the best in cases where programmers are required to use statistical techniques for analytics or forecasting. It is a programming language that has been created for statisticians and statistical tasks. You can also choose to build AI models with the help of data with statistical learning techniques using R.

If you wish to start from scratch you can begin with SQL. SQL or the Standard Query Language is not exactly a programming language but a query language; it is an absolute must for working on relational databases.

SQL also helps you understand the various CRUD (Create, Read, Undo, Delete) functions inside a Database Management System. Even though there are NoSQL databases available in the market, knowing how to use SQL is still one of the in-demand skills in Data Science.

Types of Programming in Data Science

There are 5 types of computing languages available to Data Science professionals:

With these various programming languages at their disposal, data science professionals can program data-driven software, data infrastructure, AI models, databases, and various data-driven systems. Scripting languages can help them execute scripts that work on the available data. Meanwhile, a query language can help them carry out data transactions.

8 Essential Programming Languages for Data Science

1. Python: Learning Data Science using Python is easy and fun as this language is extremely close to the English language we communicate in. It is a high-level programming language. Python seems like it has been almost built for Data Science, being able to import various libraries such as Matplotlib, NumPy, pandas, and many more.

You can use Python for data cleaning, data pre-processing, data analysis, and even data visualization. For instance, you can use the Bokeh library for creating highly interactive visualizations. Similarly, you can do data analysis with the help of pandas. One of the most common IDEs for Python is Jupyter Notebook.

If you wish to learn python for Data Science, getting the hang of Anaconda will also would make sense. Anaconda is one of the best Data Science platforms for using Python, and learning how to use Python is one of the essential Data Science skills.

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2. R Studio is the IDE for R, and the programming language can be seen being used to plot high-quality graphs and visualizations. R also has extensive support for data wrangling, thus being a favourite for data transformation tasks. Knowing R is also a very effective Data Science skill.

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3. Scala can be used in many processes involved with handling large datasets. The language is as powerful as Python and R when it comes to building machine learning models and modeling data. The language is statically typed and is also used whenever there is a need to use Java code.

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4. MATLAB allows developers to create deep-learning models with very little code. This is due  MathWorks, the creators of MATLAB, offering a Deep Learning Toolbox for connecting and building the layers of deep neural networks. You can also easily import priorly trained AI models and adjust the training parameters according to your requirements.

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5. Many developers use VBA for modifying and customizing office suite applications as well. However, VBA in Data Science is used for data processing, word processing, and visualizations. You can use VBA for generating reports, graphs, and various kinds of forms that can be used in Data Science pipelines or for reporting.

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6. SQL: SQL is a query language and is extensively used to manage database systems such as MySQL, MariaDB, and SQL Server. Many developers choose to use SQL for data operations or for facilitating data for other processes. However, with distributed file systems and NoSQL databases gaining popularity, SQL is slowly losing users.

There are many other query languages such as SchemeSQL, ScalQL, ActiveRecord, and HaskellDB.

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7. C++: C++ is one of the most powerful languages that dominate the programming world. However, it is not necessarily one of the best for Data Science. Even then, the low-level to medium-level language is used for developing data infrastructures and applications that are data-driven.

Even though you will not see developers using C++ for analytics, you will definitely see data architects using it for integrating databases with powerful applications. Operating systems, browsers and games can all be built with it. When it comes to memory management, C++ is probably one of the best choices (except C# with its great garbage collection features).

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8. SAS Language: The SAS language has been specifically built for being used with SAS, the statistical tool for analytics. Even though SAS is developed for statistical analysis, it can be used for data processing and then migrating the output to other platforms through HTML or PDF documents. You can use SAS for reading data from excel files, databases, and other spreadsheet document files in order to conduct analysis.

Once the analysis is complete, you can generate the output in graphs or tables. This particular language can be compiled in various operating systems but is limited in terms of functions depending on if it is a Windows or a UNIX-based system. In the real world, SAS is used for complex analytics but for minimalistic graphical representations.

The use of SAS is always more focused on generating table-based data that can be easily read.

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It is not just programming languages that you need to know, though, you must also have a foundational understanding of data structures and algorithms. It also helps to know system design as the job role of a data architect requires one to program secure frameworks for databases and other environments. You can learn whichever language suits you the best with a well-structured data scientist course.

When it comes to programming languages, Python is the easiest to learn, but the programming language you should truly go for is highly dependent on your personal requirements. Thus, it is always great to check out all the other options you have. Especially in a sector such as Data Science, you have many alternatives.

The data science demand in India is growing with every passing day and with programming languages being an essential part of Data Science, it will definitely serve you well to pick up the essential Data Science skills associated with these languages. You can check out Hero Vired’s Integrated Program in Data Science, Machine Learning, and Artificial Intelligence in order to learn programming languages such as Python and R.

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