Recent years have shown huge rise in the amount of data being generated each day. With technology reaching every part of the household through mobile phones and smart devices like Smart TVs, Smart Washing machines, Alexa etc., the data generated by user each day is huge. This data would make no sense unless it is analyzed and presented to the user in appropriate way useful to them. The data is collected in various forms and from various sources. It is structured and unstructured. Using this data as is could prove to be challenging. This is the reason due to which Big Data and Data Science go hand in hand. Data Scientists break the big data into usable information and create software and algorithms to analyze this data for end-users to make sense out of it. As seen in the DIKW Pyramid, it is important to generate Wisdom from this data through Information and Knowledge. Data Science is the domain which plays a major role in this process.
Data Science is a field which uses scientific methodologies, algorithmic techniques, systems and processes to extract knowledge and insights from the structured and unstructured data. The field is closely related to statistics and mainly consists of preparing the data for analysis, actual data analysis and presenting data in high level forms to the end user. This can be majorly categorized into the following steps,
- Data Acquisition
- Data Pre-processing
- Data Modeling
- Data Analysis
- Data Visualization
There are various techniques, languages, frameworks, visualization tools, and platforms to perform the listed operations. The programming languages used for data science prove to be an important tool for implementing several algorithms and realizing various data science functions. Every programming language differs in terms of syntax, ease of use, readability, capabilities, suitability toward particular operations etc.
Following are the top 6 Data Science programming languages for year 2024,
- Python: This open-source language has been around for 30+ years and bags the topmost position in the list. It is popular as the learning curve is not steep. The syntax is easy to learn and readable. It is a breeze even for non-programmers who want to switch to programming with ease. The language is capable of doing simpler to complex tasks without any interfacing issues. It has proved to be one of the most versatile high-level programming languages that contains multiple built-in libraries for different functions. The use of Python for data science sounds apt because of popular libraries and tools like NumPy, Pandas, Matplotlib, scikit-learn etc. available to the end-users for machine learning, data analysis, data visualization, etc. Additionally, advanced Python libraries like TensorFlow, Keras, Pytorch, etc. facilitate the end-users with Deep Learning Tools for Data Science problems. The user community being huge, there is ample support available in public forums too. There are major market players like Google, YouTube, Mozilla, Facebook, Netflix have been successfully using Python. Among the few cons of this programming language is its non-suitability for mobile devices. It cannot be easily adapted for mobile computing and additional efforts beyond the purview of beginners might be required to do so.
- R: This bags the second position for its persistence when it comes to Statistical capabilities. It has its roots for around two decades and was initially developed by members belonging to academia and statistics. The Comprehensive R Archive Network (CRAN) is the backbone of R, with over 15,000+ packages available for use by the data scientists. It is equipped with many libraries that contain an array of functional, tools and methods to effectively manage and analyze data. These libraries like dplyr, ggplot2 etc. are particularly focused on managing image and textual data, data manipulation, visualization, machine learning, etc. In addition to robust statistical techniques, R also has great charting and graphic abilities. However, R is an old language and did not keep much pace with the changing requirements. Additionally, it lacks inbuilt security and cannot be embedded into web browser for secured calculations. You can reach out to us for programming assistance in R Programming Assignment Help – Data Science Help
- SQL: The popularity of Structured Query Language (SQL) is not new and hence grabs third position in this list. It has been used since years to manage data. This is the database language used for retrieving data from relational databases. The data is well structured and organized. The CRUD (Create, Read, Update and Delete) operations on data are performed using SQL. For any data scientist, these operations are crucial. Hence, SQL can prove to be a strong hand for extracting and wrangling the data from the database. The language is very intuitive and readable. It helps in speedy searching and retrieving of data. The syntax is easy and declarative. It also has variety of implementations like MySQL, SQLite, PostGreSQL etc. Google BigQuery is a fully-managed data warehouse that enables scalable analysis of petabytes of data. It also has built-in machine learning capabilities. The SQL databases are tightly managed. A single change could affect the entire database. This limitation could be overcome by writing large-scale migration scripts to accommodate single change. Another limitation could be that the SQL databases are only vertically scalable.
- JAVA: The popularity of JAVA is not hidden. This high level programming language with wide applicability bags the fourth position in this list. The versatility of Java isn’t hidden when it comes to computer embedding, web applications and desktop applications. With advancement in data science, Java seemed to take a slower pace with this new emerging technology. However, it was not a long wait for it to get back in power due to its core competencies like distributed computing, platform independent feature and straightforward coding approach. Additionally popular Java based framework like Hadoop, which runs on Java Virtual Machine (JVM) has already proven its popularity for data processing and storage in distributed structures for large data applications. Other popular Java based frameworks relevant to data science domain are Hive and Spark. With higher processing power, Java allows large amounts of data to be processed and also helps multitasking. Popular data science libraries include MLLib, Deeplearning4J, Java-ML, Weka etc.
- JavaScript: Though JavaScript was earlier introduced primarily for frond end development, it grabs fifth position in the list. With limited statistical support in this language, developers were not much optimistic about its usability for data science. However, times changes and enough native libraries are now available in JS for working with big data and machine learning. Tensorflow.js has gained huge popularity due to its usability. With the advent of JavaScript frameworks like NodeJS, ReachJS, AngularJS etc., it gained huge respect for back-end purpose too. Popularly introduced to MEAN and MERN stacks, its ease of use made it gained importance among data scientists being a glue among the models/ algorithms and web browser. Its core strength lies in building web-based dashboards for interactive data visualization. It can embed everything from electronics to desktop and web applications. It is one of the languages that is easily scalable for larger applications.
- Scala: Scala stands for ‘scalable’ language. Holding position sixth in the list, this is an extension of Java and was originally built on JVM. It was built more recently, in year 2003 to address the issues with Java. It is used to write Apache Spark which is a well-known framework for cluster computing. One of the significant features of Scala is its ability to facilitate parallel processing on a large scale. It is efficient in handling large volumes of data. One of the cons of Scala is the steep learning curve. It is definitely not recommended for beginners and also considering its slow adoption in machine learning domain, not much help is available in public forums. Scala lacks modernization for dependencies as provided by languages like Python. The packages in Python are not readily available in Scala which may sound boon or even curse, depending on the user’s perspective.
In summary, there are many programming languages with practical capabilities when it comes to data science and machine learning. However, the literature study suggests that Python has been leading the position consecutively since last few years with approximately 65% share. R language has approximately 45% share. The next in queue is SQL with approximately 32% share. Java catches the drive with approximately 12% share, followed by Javascript with 7% share. Finally on the list is Scala with approximately 4% share.
Additionally there are other languages which are trying to be in this race and it worth a mention. They are C/C++, Julia, Statistical Analytical System (SAS) etc. Few concluding remarks could be made as below,
- In cases where the end user is analyzing a huge data set with many statistical calculations can use R or Python for better results
- In cases where the end user expects greater performance and integration with existing applications, can go ahead and use Java
- When it comes to data visualization and dashboards, end-user may think of using Javascript
- In cases where multiple data operations are involved, SQL can be used
- When it comes to using Apache spark, Scala may be thought of!