Data science has moved way beyond just being the buzzword and its impact is spreading across the industries. There is no slowing down for it in the future too. Given that the industry predicts an acute shortage of skilled data science professionals – many professionals are taking up courses to up-skill themselves in data science. This write up sheds light on the top programming languages that you must master to grow as a better data scientist.
Python is one of the most popular coding languages, and its popularity is due to its versatility. Python includes high-level data structures, dynamic typing, dynamic binding, and other features, making it suitable for complex application development. The versions of Python are copyrighted under a GPL-compatible license, which is certified by the Open Source Initiative. Python is considered to be ideal for general purpose tasks like data mining and big data facilitation.
The usability of Python in data science is varied, and that includes –
- Back end or server-side web and mobile app development
- Desktop app/software development
- Big data processing
- Mathematical computations
- System script writing
Top Python libraries for data science are –
Python is an ideal choice for projects that involve analytical and quantitative calculations and implementation of algorithms. One good example is YouTube, which uses Python and artificial intelligence for improving its internal infrastructure.
Learn Python through these online courses –
- Using Databases with Python
- Complete Data Science Training with Python for Data Analysis
- Programming for Everybody (Getting Started with Python)
- Computer Science & Programming Using Python
2. R Programming
R is an open-source tool and has been extensively used in developing statistical applications, statistical analysis, data analysis, as well as machine learning. R is an imperative programming language to churn the raw data and help users analyze, process, transform, and visualize information. You also have this option of developing prediction models, machine-learning algorithms, along with several packages for image processing. Prominent features of R that make it useful for data science applications include –
- A complete language with several elements of an Object-Oriented Programming language too
- Analytical support through a range of support libraries to clean, organize, analyze, and visualize your data
- Supports extensions and enables developers to write their libraries and packages
- Facilitates interaction with databases through add-on packages like RODBC package, Open DataBase Connectivity Protocol (ODBC) and the ROracle package that connect R with databases
Some of the useful R packages are –
- For data loading – DBI, odbc, RMySQL, RPostgresSQL, RSQLite, XLConnect, xlsx, haven, etc.
- For data manipulating – dplyr, tidyr, stringr, lubridate. etc.
- For data visualization – ggplot2, ggvis, rgl, htmlwidgets, googleVis, etc.
- For data modelling – car, mgcv, lme4/nlme, randomForest, multcomp, vcd, glmnet, caret, etc.
- For reporting results – shiny, R Markdown, xtable
R programming is widely used by statisticians, data analysts, researchers, and marketers, hence it has wide applicability in statistical computing, data analytics, and scientific research projects. A nice example is the creation of a credit card fraud detection system.
Courses you can consider to learn more about R programming are –
- Introduction to R for Data Science
- Data Science: R Basics
- Data Analysis with R
- Essential Math for Machine Learning: R Edition by Microsoft
- R for Data Analysis
Scala is an open-source modern multi-paradigm programming language that stands for “Scalable Language”. This language is designed to express common programming standards adequately. Scala also offers a lightweight syntax for defining anonymous functions, supports higher-order functions, and allows functions to be nested. Scala also has built-in support for pattern matching that provides algebraic types of functionality, used in many functional languages.
The type system of Scala supports generic classes, variance annotations, upper and lower type bounds, inner classes and abstract type members, compound types, explicitly typed self-references, implicit parameters and conversions, and polymorphic methods.
The most helpful features of Scala for data scientists are –
- Type inference
- Singleton object
- Lazy computation
- Case classes and Pattern matching
- Concurrency control
- String interpolation
- Higher-order function
Popular Scala libraries
- Data Analysis & Math – Breeze, Saddle, ScalaLab
- NLP – Epic, Puck
- Visualization – Breeze-viz, Vegas
- Machine Learning – Smile, Apache Spark MLlib & ML, DeepLearning.scala, Summingbird, PredictionIO
- Additional Libraries – Akka, Spray, Slick
- Suitable for – Useful for projects dealing in humongous amounts of data. Some of the popular Scala projects are PredictionlO, textteaser, nak (an ML library), BIDMach, bayes-scala, among others.
Learn Scala through these popular courses –
- Apache Spark and Scala (Online Classroom-Flexi Pass)
- Apache Spark 2 with Scala – Hands On with Big Data!
- Spark, Scala and Storm combo
- Scala Programming Course
- Apache Spark and Scala Certification Training
Java is a class-based, object-oriented, and general-purpose programming language. It is designed to have a lesser number of implementation dependencies. It is a perfect platform for cross-platform applications, including web applications and server-side codes, and is not limited to any processor or computer.
It was earlier designed to offer simpler alternatives, mainly in terms of memory management and class libraries. Still, its importance has never faded and has a significant role to play in Big Data. Most of the popular frameworks and tools used for Big Data are typically written in Java, including Fink, Hadoop, Hive, and Spark. From data mining and data analysis to the building of Machine Learning applications, Java is imperative in the field of data science.
Java is –
Popular Java Libraries
- DL4J – Deep Learning
- Advanced Data Mining and Machine Learning System (ADAMS)
- Java Machine Learning Library or Java ML
- Apache Mahout
- Waikato Environment for Knowledge Analysis (Weka)
- Java Statistical Analysis Tool Library or JSTAT
- Stanford CoreNLP
If you want to build an application from scratch, then Java can be the most useful platform. Moreover, Java is the best choice for building large and sophisticated machine learning applications.
Learn Java with these online courses –
- Java Programming for Complete Beginners – Learn in 250 Steps
- The Complete Java Certification Course
- Java Programming: Principles of Software Design
- Kotlin for Java Developers
- Java Programming: Solving Problems with Software
5. SQL (Structured Query Language)
SQL is a domain-specific programming language that helps in managing data in a relational database management system, or for stream processing in a relational data stream management system. It is a non-procedural language that cannot write a complete application. However, SQL helps to perform common data science tasks such as finding, exploration, and extraction data within relational databases. Though Python, R, and dashboards stand apart from SQL in terms of ease of use while performing sophisticated tasks, SQL still holds its place when it comes to speed.
The prime functions of SQL are –
- Data selection from tables
- Grouping and sorting functions
- Text mining
- Date functions
- Statistical functions
- Regular expressions
- Loading and copying data into database
- Data bucketing
SQL is widely used for data management in online and offline apps.
Learn SQL with these online programs –
- The Complete SQL Bootcamp
- SQL – MySQL for Data Analytics and Business Intelligence
- The Ultimate MySQL Bootcamp: Go from SQL Beginner to Expert
- Foundations for Big Data Analysis with SQL
The choice of which programming language to master depends upon your inclination and professional requirements. However, it is always a good idea to learn and practice on real-life examples to master it. Pick up simple projects and then move towards the challenging ones to progress on your journey to learn data science.
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