Most Popular Programming Languages ​​for Data Science

Data science has gone far beyond being a buzzword and its influence is spreading across all industries. It should not be stopped in the future. With the industry anticipating an acute shortage of qualified data science professionals, many professionals are taking courses to improve their data science skills. This article throws light upon the best programming languages ​​for data science that every aspiring data scientist must master to advance in a data science career.

Python

Python is one of the most popular programming languages ​​for data science. Python includes high-level data structures, dynamic typing, dynamic linking, & many other features, to making it suitable for developing complex applications. Versions of Python are copyright protected under a GPL-compliant license, which is supported by the Open Source Initiative. Python is ideal for general purpose tasks such as data mining and big data facilitation. The usability of Python in data science is varied and this includes:

• Server-side/back-end web & mobile app development
• Desktop software/application development
• Big data processing
• Calculations
• Writing system scripts
  

The main Python libraries for data science are:

• TensorFlow
• Scikit-Learn
• Numpy
• Keras
• PyTorch
• LightGBM
• Eli 5
• SciPy
• Theano
• Panda

Suitable For

Python is an ideal choice for projects involving analytical and quantitative computations and algorithm implementation. A good example is YouTube, which uses Python and artificial intelligence to improve its internal infrastructure.
Learn Python with these online courses:

• Using databases with Python
• Comprehensive data science training using Python for data analysis
• Programming for Everyone (Introduction to Python)
• Computer Science and Programming with Python

R Programming

R is an open source tool and has been widely used in developing statistical applications, statistical analysis, data analysis, and machine learning. R is a programming language necessary to produce raw data and help users analyze, manipulate, transform, and visualize information. You also have the option to develop prediction models, machine learning algorithms, along with various image processing packages. Notable features of R that make it useful for data science applications include:

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• A complete language that contains various elements of an object-oriented programming language as well
• Analytical support through a variety of support libraries to clean, organize, analyze and visualize your data
• Supports extensions and allows developers to write their own libraries and packages
• Facilitates interaction with databases with additional packages such as the RODBC package, the Open Database
• Connectivity (ODBC) package, and the ROracle package that connects R to databases

Some useful R packages are:

• Load data: DBI, odbc, RMySQL, RPostgresSQL, RSQLite, XLConnect, xlsx, sanctuary, etc.
• Data processing: dplyr, tidyr, stringr, lubridate. etc.
• Data visualization: ggplot2, ggvis, rgl, htmlwidgets, googleVis, etc.
• Data modeling: car, mgcv, lme4/nlme, random Forest, multcomp, vcd, glmnet, caret and so on.
• Results Report: Glossy, R Markdown, xtable

Suitable For

R programming is widely used by statisticians, data analysts, researchers and marketers, so it has wide application in statistical computing, data analysis and scientific research projects. A good example of this is the creation of a credit card fraud detection system. Courses that you can be consider to learn more about R programming are:

• Introduction to R for Data Science
• Data Science: R . Fundamentals
• Data analysis with R.
• Basic Mathematics for Machine Learning: Microsoft R Edition
• R for data analysis

Scala

Scala is a modern, open-source, multi-paradigm programming language that stands for “Scalable Language”. This language is designed to adequately express common programming standards. Scala also provides a lightweight architecture for defining anonymous functions, supports higher order functions, and allows you to nest functions. Scala support for pattern matching that provides algebraic types of functions, which are used in many functional languages.
Scala’s type system supports generic classes, variance annotations, upper and lowercase type limits, inner classes and abstract type members, compound types, explicitly typed self-references, implicit parameters and transformations, and polymorphic methods. The most useful features of Scala for data scientists are:

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• Write the inference
• Singleton object
• stability
• lazy computing
• Status categories and pattern matching
• Concurrency control
• String interpolation
• Ranking function
  

Popular Scala Libraries

• Data Analysis and Math: Breeze, Saddle, ScalaLab
• NLP – Epic, Puck
• Visualization – Breeze Fizz, Vegas
• Machine Learning: Apache Spark MLlib & ML, DeepLearning.scala, Summingbird, PredictionIO
• Additional Libraries: Akka, Spray, Slick
• Good for: Useful for projects that deal with huge amounts of data. Some of the popular Scala projects are PredictionlO, textteaser, nak (ML library), BIDMach, bayes-scala and for some others also.

Learn Scala with these popular courses:

• Apache Spark and Scala (Classroom-Flexi Pass online)
• Apache Spark 2 with Scala: Get Started with Big Data!
• Spark and Scala Storm Combo
• Scala programming course
• Training Apache Spark and Scala

Java

Java is a object-oriented, and class-based programming language. Java has fewer implementation dependencies. It is ideal for cross-platform applications, including web applications and server-side code.

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It was previously designed to offer simpler alternatives, primarily in terms of memory management and class libraries. However, its importance has never faded away and it has an important role to play in big data. Most of the popular frameworks and tools used for big data are usually written in Java, including Fink, Hadoop, Hive, and Spark. From data mining and data analysis to building machine learning applications, Java is a must in the field of data science. java is –

• Basic
• Portable
• Object oriented
• Secured
• Dynamic
• Distributed
• Robust

Popular java libraries

• DL4J – Deep Learning
• Neural
• Advanced Data Mining and Machine Learning System (ADAMS)
• Machine Learning Library Java or Java ML
• RapidMiner
• Apache Mahout
• The Waikato Knowledge Analysis Environment (Weka)
• Statistical Analysis Tools Library Java or JSTAT
• Stanford Core NLP

Suitable For

If you want to build an app from scratch, Java may be the most useful platform. It is also the best choice for building large and complex machine learning applications. Learn Java with these online courses:

• Java Programming for Beginners – Learn in 250 Steps
• Complete Java Certification Course
• Java Programming: Principles of Software Design
• Kotlin for Java developers
• Java Programming: Software Troubleshooting

SQL (Structured Query Language)

SQL is one of the most popular field-specific programming languages ​​for data science that helps in managing data in a relational database management system or for stream processing in a relational data flow management system. It is a non-procedural language that you cannot write a complete application. However, SQL helps perform common data science tasks, such as searching, exploration, and data mining within relational databases. Although Python, R, and Control Panels differ from SQL in terms of ease of use when performing complex tasks, SQL still takes its place when it comes to speed.

The main functions of SQL are:

• Selecting data from tables
• Grouping and sorting functions
• Text extraction
• History Jobs
• Statistical functions
• Regular expressions
• Joins
• Loading and copying the data into the database
• Data storage

Suitable For

SQL is widely used to manage data in both online and offline applications. Learn SQL with these online programs:

• Complete SQL Bootcamp
• SQL: MySQL for Data Analysis and Business Intelligence
• Ultimate MySQL Bootcamp: Go from novice to SQL expert
• Basics of Big Data Analysis with SQL

The choice of programming languages to master data science depends on your professional inclinations and requirements. However, it is always a good idea to learn and practice real-life examples to master them. Choose programming languages for data science and simple projects, then move on to the more difficult languages to advance your journey of learning data science.