In the recent years the field of technology has seen exponential growth is data. The complexity of data processing has increased due to the mixture of structured, semi structured and unstructured data. Data Science and Data Engineering has gained momentum and has become a foundation for Artificial Intelligence (AI) and Machine Learning. Data has become most essential commodity across all discipline. In fact, it is said that “Data is the new oil of the modern age”. The importance of data will continue to grow and will become a key driver for Artificial Intelligence (AI) revolution.
How Is Programming Used in Data Science?
The field of data science and data engineering relies on programming across all job functions, from automating data cleansing, processing, transformation and organizing raw data sets.
What Programming Language Is Best for Data Science?
Learning skills required for Data Science and Data Engineering will become very essential to survive and succeed in the field of technology. One of the key skills to master to become an expert in the field of Data Science and Data Engineering is programming. Here are the top 10 programming languages for Data Science & Data Engineering in 2022.
- Python
- R
- SQL
- Scala
- Julia
- Java
- JavaScript
- C/C++
- Matlab
- Perl
1. Python
Python is a general purpose popular programming language. Learning Python opens up doors not only in data science, but also in web and software development.
Python is an open source object-oriented programming language, grouping data and functions together for flexibility and composability. In data science, it’s commonly for data processing, implementing data analytics algorithms, and training machine learning and deep learning algorithms. Python supports multiple data structures and uses a plain English syntax, making it a great language for beginner programmers.
“There is no comparison in terms of online documentation, user community, ease-of-learning, and general capabilities of Python.”
“There is no comparison in terms of online documentation, user community, ease-of-learning, and general capabilities of Python,” said Dr. Clayton Miller, Assistant Professor, Department of Building, School of Design and Environment, National University of Singapore (NUS), and instructor for the NUS course Data Science for Construction, Architecture, and Engineering on edX. “I also suggest data science-focused learners pick up the R language in parallel as it provides encapsulated libraries that aren’t always available in Python.
When to use Python in data science? Python is a great place to start if you’re learning to code for the first time, want something scalable, and/or are looking to keep your career options open.
2. R
While Python is general purpose, R is more specialized, suitable for statistical analysis and intuitive visualizations.
R is built to handle massive data sets and complex processing through RStudio. Its statistics-specific syntax is intuitive for researchers with statistics backgrounds, and powerful visualizations offer more intuitive communication of results.
When to use R in data science: Data scientists with some programming experience or beginning data scientists looking to make a mark in the research field should consider learning R. If you have experience as a statistician, you’ll also recognize the structure of R.
3. SQL
Learning SQL, or structured query language, is vital for manipulating structured data. Large-scale datasets can contain millions of rows, making it difficult to find precisely what data you need. SQL is a querying language, allowing you to adjust, locate, and check massive data sets. As a domain-specific language, it’s convenient to manage relational databases.
“Scripting with Python, fundamental statistics, and SQL are critically important regardless of which direction you go in data,” said Gwen Britton, Associate Vice President of Southern New Hampshire University (SNHU) Global Campus STEM & Business Programs and instructor for edX MicroBachelors programs in data management and business analytics.
When to use SQL in data science: If you’re using relational databases, you must learn SQL.
“Scripting with Python, fundamental statistics, and SQL are critically important regardless of which direction you go in data.”
4. Scala
Scala is an extension of Java, a language associated strongly with data engineering, with interoperability thanks to Java bytecode compiling and running on Java Virtual Machine. Built as a response to perceived problems in Java, it’s a newer, more elegant language.
Scala enables high-performance frameworks for handling siloed data, perfect for enterprise-level data science.
With vast libraries and support on common integrated development environments (IDEs), it’s functional and scalable. Scala also supports concurrent and synchronized processing.
When to use Scala in data science: Data systems developers faced with high volume datasets regularly can use Scala to analyze without overloading.
5. Julia
Another specialized language, Julia is specifically designed for computations and numerical analysis.
Although purpose-built, it provides versatility and supports both parallel and distributed computing and is incredibly fast. It’s fast enough for interactive computing and can switch to a low-level programming language if necessary.
When to use Julia in data science: If you’re focusing on data visualization or deep learning, numerical analysis, or interactive computing, the niche focus of Julia offers fast performance.
6. Java
Though it’s in the name for “JavaScript,” Java is a completely different programming language used for different purposes. It tends to be used for Android apps, credit card programming, desktop applications, and web enterprise applications.
There are many key differences in how Java and JavaScript are written, assembled, and executed. Java code must be compiled and is used to build applications to run in a virtual machine or browser, whereas JavaScript is all-text and runs on a browser only. Java is an object-oriented programming language, while JavaScript is (as the name suggests) an object-oriented scripting language.
“Building skills in Java can be a positive step towards gainful employment in some of today’s most popular and cutting-edge companies. Thousands of technology companies like Uber, Airbnb, Netflix, and Slack reportedly use the language in their software infrastructure,” said Fisayo Omojokun, Senior Lecturer at Georgia Institute of Technology and instructor for introductory Java courses on edX.
When to use Java in data science: Java typically dominates if development is taking place entirely on the server side, whereas JavaScript is usually more client-facing, with a focus on making web pages more interactive. Learning Java offers frameworks for data science areas such as deep learning or data handling and big data tools like Apache Spark and Hadoop are written in Java.
“Building skills in Java can be a positive step towards gainful employment in some of today’s most popular and cutting-edge companies.”
7. JavaScript
JavaScript is closely associated with web development and applications, bringing the capability to build vibrant web pages into the world of data visualizations. It’s another general-purpose choice for data scientists with a good selection of packages and great web integration.
JavaScript helps convey insights from truly big data. It offers data scientists a considerable set of libraries for building dashboards, visualization, and just about any task a data scientist would need. It’s scalable, but it functions best as a secondary language rather than a primary data science language.
When to use JavaScript in data science: Data scientists with development needs or who want to pick up a concurrent language for visualizations would be well served by learning JavaScript.
8. C/C++
Learning C/C++ offers excellent capabilities for building statistical and data tools. These will translate well to Python and scale well for performance-based applications.
C/C++ is also surprisingly useful because it compiles data quickly. It builds highly functional tools and allows for serious fine-tuning. It can be complicated to pick up if you’ve never studied programming languages before.
When to use C/C++ in data science: Developers with experience in low-level languages could use C/C++ for scalable projects.
9. MATLAB
MATLAB is a programming language and environment specific to mathematical and statistical computing. It offers built-in tools for dynamic visualizations and offers users a deep learning toolbox that transitions well. It allows you to ease challenging mathematical processes.
It scales well and provides built-in graphics for custom plot points and visualizations. You frequently see MATLAB in teaching contexts to train things like linear algebra or numerical analysis. If you’re carrying out complex mathematical processes, MATLAB can be very useful. However, it’s not free, and Python now has multiple options that mimic MATLAB.
When to use MATLAB in data science: If you’re in academia or your workplace is already using the environment, you have good reason to invest time into learning MATLAB.
10. PERL
Perl is known as a ‘Swiss-army knife of programming languages’, due to its versatility as a general-purpose scripting language. It shares a lot in common with Python, being a dynamically typed scripting language. But it has not seen anything like the popularity Python has in the field of data science.
This is a little surprising, given its use in quantitative fields such as bioinformatics. Perl has several key disadvantages when it comes to data science. It isn’t stand-out fast, and its syntax is famously unfriendly. There hasn’t been the same drive towards developing data science specific libraries. And in any field, momentum is key.
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