Top Programming Languages For Data Science | Programming Languages Data Scientist Must Learn

2. Ruby

3. 10 Ruby • Created in the mid 1990’s by Yukihiro Matsumoto • Acts as the fundamentals for working with frameworks like Ruby on Rails

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5. 10 Ruby • Emphasizes on ensuring the language is expressive and easy to understand • Provides more than 60,000 libraries and multiple frameworks

6. 10 Ruby • Free format language (start writing code from any line or column) • Case-sensitive (gets! = GETS)

7. 10 Ruby • Has a set of reserved keywords that are only used for specific tasks • Is a dynamic programming language

8. 10 Ruby Core software supporting Data Science Apache Arrow Numo/Cumo Red Chainer

9. Julia

10. 9 Julia • Julia was developed by a group of people at the Massachussets Institute of Technology • It’s a multi purpose programming language built specifically for Data Science

11. 9 Julia • Julia has an excellent dispatch making it very fast • Julia also offers a pretty great REPL • Julia is pretty easy and very usable for DS/ML in terms of syntax. • Offers high scalability 

12. Scala

13. 8 Scala • Created in early 2000 by Martin Odersky • Supports object-oriented, functional programming • Users have access to the features of JVM and Java libraries

14. 8 Scala • Requires fewer lines of code, helping speed up development, testing and deployment • Helps improve runtime stability, performance, developer productivity, etc.

15. 8 Scala • Apache Spark is the widely used tool in the industry which is written using Scala programming language • Scala provides features like val, Higher Order Functions, Partial Functions, Pattern Matching & Case Classes, Collections, Currying and Implicit

16. MATLAB

17. 7 MATLAB • Matlab was developed by MathWorks • Used for mathematical computation • Can be integrated with languages like C,C++, and Java

18. 7 MATLAB • Has powerful debugging tools • Uses graphics commands that make visualization of results easy

19. 7 MATLAB • MATLAB has rich ML libraries • Best for matrix calculations • Fewer lines of code • MATLAB offers specialized toolboxes for machine learning, neural networks and computer vision

20. TensorFlow

21. 6 TensorFlow • TensorFlow is a computational framework for building machine learning models • TensorFlow offer wide variety of tool kits that allow you to write at your preferred level of abstraction • TF specific abstract methods which are highly optimised for components

22. 6 TensorFlow • tf.layersmethod abstract can be used to play with the layers of a neural net • You can build a model and evaluate the model performance using the tf.metricsmethod • The most widely used level is the tf.estimatorAPI, which allows you to build production ready models

23. JavaScript

24. 5 JavaScript • Developed by Brendon Eich • Server-side and client-side programming language • Offers a wide range of frameworks and libraries

25. 5 JavaScript • Data visualization - Libraries such as D3.js, Chart.js, Plotly.js and many others make powerful data visualization • Product integration – Companies use web technologies with a Node-based stack for the core product • TensorFlow.js – Machine learning library developed by Google

26. 5 JavaScript • Functionality – No data science packages and functions compared to other languages • Productivity – JavaScript doesn’t make it easy for the user with manuals or guides. • Multithreading – Node.js is not suited for computationally intensive tasks. • Opportunity cost – It is easier to learn other languages like R or Python

27. Java

28. 4 Java • Java was developed by James Gosling and team • Offers incredible stability and is easily manageable • Offers good security and memory management • Java products are storming the market

29. 4 Java • Java has a great toolset with libraries like Weka, Java-ML, MLlib and Deeplearning4j used for ML and data science problems • The JVM is one of the best platforms, enabling you to write code that is identical on multiple platforms

30. 4 Java • Most of the popular Big Data frameworks/tools on the likes of Spark, Flink, Hive, Spark and Hadoop are written in Java • JVM has Scala which is excellent for Data Science • Java is fast

31. SQL

32. 3 SQL SQL • Developed by Raymond F Boyce • Structured Query Language(SQL) is a standard database used to create, maintain and retrieve relational databases

33. 3 SQL SQL • Easy to learn - It uses simple language structure with English words that are easy to understand • From a Data Science point of view, SQL can be used both for pre-processing and Machine Learning purposes

34. 3 SQL SQL • Through slicing, filtering, aggregations and sorting, SQL will allow you to play around with your dataset • SQL integrates well with other scripting languages like R and Python • The best solution for dealing with huge datasets is SQL

35. R

36. 2 R • Created in 1992 by Robert Ihaka and Robert Gentleman • Provides an environment for implementing statistical techniques • Provides data handling and storage facilities

37. 2 R • Can be compiled on a wide range of UNIX platforms, Windows and macOS • It provides a large collection of tools for data analysis

38. 2 R • R has an extensive library of tools for database manipulation and wrangling. Some of them are dplyr package, data.table package and readr package • R packages like ggplot2 and ggedit are used for data visualization and representation

39. 2 R •  R offers ML packages like MICE, CARET and randomFOREST • R programming language is open source. This makes it highly cost effective for a project of any size.

40. Python

41. 1 Python • Developed by Guido-Van-Rossum • Has easier syntax than other languages like Java • Used in creating web frameworks and web applications

42. 1 Python • Python is powerful and easy to use • Python provides a massive database of libraries like Scikit Learn, TensorFlow, Seaborn, Pytorch, Matplotlib and many more

43. 1 Python • The library Matplotlib provides a strong foundation around which other libraries like ggplot, pandas plotting, pytorch, and others are built.