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 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:
- High-level programming languages
- Medium-level programming languages
- Low-level programming languages
- Scripting languages
- Query Languages
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
- 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.
- A huge number of libraries
- Extremely flexible
- Simple syntax and easy to learn
- Has a host of libraries for Machine Learning
- Takes more compilation time than low-level languages
- Not as powerful as low-level languages for software and infrastructural development
2.R: R is a programming language specifically designed for statistical purposes. It is an excellent language for statistical modeling, learning, and analysis. R is one of the most preferred choices for research-based projects as well as for financial analytics.
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.
- Best programming language for statistics
- Great for data cleaning and transformation
- Great visualizations and plotting
- Platform independent
- Not as flexible as Python
- Harder to learn compared to Python
- Weaker origin and lack of security
3. Scala: Scala has been created with the intent to address the shortcoming of Java. Even though Java can also be used in Data Science, it is not as concise as Scala. It is also a fantastic programming language for processing data and for working on distributed systems. Many data scientists use Apache Spark for large-scale data processing, and this is where Scala can be seen being used.
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.
- Supports both functional programming and object-oriented programming
- Scala can be used for executing Java code
- Scala offers an expressive typing system ensuring consistent and secure statistical abstraction
- Type information is harder to understand
- Needs to run on the JVM or Java Virtual Machine
- Does not have a large developer community behind it
- There is no proper tail-call optimization
4. MATLAB: MATLAB is an excellent programming language for numeric or scientific computing. It is a great language for plotting data or functions, implementing algorithms, and manipulating matrices. You can also use this multi-paradigm language to integrate programs written in other languages.
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.
- Great for Deep learning
- Requires minimal code with MathWorks integrations and tools
- Offers immediate debugging
- Takes more time for execution compared to languages such as C++
- Requires powerful processing hardware
- Is not open-source like R and Python
5. Visual Basic for Applications: Visual Basic for Applications or VBA functions with the help of MS Office applications and is a programming language developed by Microsoft. Unlike TypeScript (Also by Microsoft), which can be used for various purposes, VBA’s primary focus is on office suite environments.
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.
- Allows one to automate processing functions
- Offers incredible accessibility features
- More secure compared to some environments
- Removes the need for burdening other suite software such as Excel
- Requires separate knowledge in VBA for writing programs
- Excel is required to use VBA
- Hard to debug
- Limited in terms of functions
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.
- Simple to use
- Great for CRUD functions
- Extremely valuable for integrating data from relational databases
- Security issues
- SQL does not allow full control of databases
- Hard to scale
- The interface is not that great
- Not great for big data
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).
- Polymorphism at runtime
- Great for data abstraction and powerful processing tasks
- Portability across multiple platforms and Operating Systems
- Great for infrastructural development
- Best memory management
- Not the best option for Data Science
- Hard to learn with low-level syntax
- Does not have as many libraries for Machine Learning and Data Science as Python and R
- Inefficient garbage collection
- Security concerns
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.
- Easier to debug
- Supports large databases
- One of the best languages for statistical analysis
- Offers tried-and-tested algorithms
- You can also get customer support for the SAS tool
- Expensive compared to open-source alternatives and free IDEs
- Difficult to mine text and other graphical data
- The language makes it harder to visualize compared to R (also statistically-inclined) while the SAS software itself is not as great at graphical representations as Tableau
- It is even more difficult to learn than R
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.