5 Data Science Languages to Learn during 2023 – 2025
Contents
#Python, #R, #SQL, #Julia, #C++ , #Jobs, #career, #wheretolearn, #learningresources
For an amateur, it is overwhelming to find so many programming languages and skill sets required for so large data processing. In this blog, I am focusing on the strength and weaknesses of 5 programming languages and tools that a student should focus on to master the art of Data Science. Also, the resources to learn these languages are here from where you can learn them yourself.
There are many programming languages that can be used for data science, but some of the most popular ones are:
Table of Content:
- Python – Python is one of the most widely used programming languages in data science. It has a vast array of libraries and tools that make it ideal for working with data. Some popular libraries in Python for data science include NumPy, Pandas, Matplotlib, and Scikit-learn.
- R – R is a statistical programming language that is specifically designed for data analysis and visualization. It has a range of libraries for data manipulation, modeling, and visualization. Some popular R libraries for data science include ggplot2, dplyr, and caret.
- SQL – Structured Query Language (SQL) is a domain-specific language used for managing and manipulating data in relational databases. SQL is an important language for data scientists who work with large databases.
- Julia – Julia is a high-level, open-source programming language designed for numerical and scientific computing, data science, and high-performance computing. It was developed in 2012 by a team of researchers and computer scientists from MIT, and is designed to be both fast and easy to use.
- C++ – C++ is a high-performance, general-purpose programming language that is used in a wide range of applications, including operating systems, gaming, scientific computing, and now Data Science.
You have covered 25% of the blog. If you wish to dig for more, find the related libraries and the resources to learn from.
1. Python:
Python has a vast community of developers and data scientists who contribute to the development of libraries and frameworks, making it easy to find support and solutions to problems. Its simple syntax and ease of learning, make it ideal for beginners. It has a plethora of libraries specifically designed for data science such as NumPy, Pandas, Scikit-learn, and TensorFlow. Python offers interactive programming through tools such as Jupyter Notebooks that make it easy to test and visualize results in real time. It can be used for a wide range of applications including web development, game development, scientific computing, and data analysis.
However, it must be kept in mind that compared to lower-level languages like C++, Python can be slower, making it less ideal for performance-intensive tasks. It is not as efficient with memory as other languages, which can cause issues when working with large datasets. Python is dynamically typed, which means that it can be harder to catch errors during development compared to statically typed languages. Hence you find an error later when you put that into the application.
The official documentation for Python provides a comprehensive guide to the language and its libraries, including those used in data science. Kaggle: Kaggle is a platform for data scientists to learn, compete, and collaborate. It offers a wide range of datasets and challenges to practice and improve Python skills. Also, DataCamp offers interactive courses on Python for data science, covering topics such as data manipulation, visualization, and machine learning.
2. R for Data Science:
R is open-source software, making it freely available and customizable. It is specifically designed for statistical computing and analysis, making it a powerful tool for data science. R has a large number of packages and libraries for data science, including the popular tidyverse, which includes libraries for data manipulation and visualization. This language has built-in functions for creating high-quality visualizations, making it easy to explore and communicate insights from data. R offers an interactive programming environment through tools such as RStudio, which allows for quick testing and debugging.
The challenge with R can be its less efficient memory management, thereby making this difficult for working with large datasets, hence R can be slower than Python while working with large datasets. R can have a steep learning curve, especially for those without a programming background.
R can be learned from RStudio. This is an integrated development environment (IDE) for R that provides a user-friendly interface and tools for interactive programming and visualization. The Comprehensive R Archive Network (CRAN) is a repository of R packages and libraries for data science. It provides a comprehensive guide to R packages and their functions.
DataCamp offers interactive courses on R for data science, covering topics such as data manipulation, visualization, and machine learning.
3. SQL
The strength of SQL lies in its Efficient Data Retrieval, Data Aggregation, Data Integrity, Compatibility, and Standardization. SQL is designed for efficient querying and manipulation of large datasets, making it ideal for data science applications. It offers powerful functions for aggregating and summarizing data, making it easy to derive insights from large datasets.
It has a robust mechanism for ensuring data integrity and consistency, which is crucial for data analysis. SQL is widely supported by databases and data processing tools, making it easy to integrate with other data science technologies. SQL is a widely adopted standard for working with relational databases, which provides a common language for data scientists and developers.
While SQL has many strengths, It is limited in its ability to perform complex data manipulation and modeling compared to other programming languages like Python and R. While SQL is ideal for data retrieval and manipulation, it is not well-suited for large-scale machine learning applications. SQL can have a complex syntax, which can be challenging for beginners to learn and master to start with.
One can learn SQL from SQLBolt. SQLBolt offers an interactive tutorial for learning SQL, covering basic and advanced concepts. Another resource, SQLZoo offers interactive SQL tutorials with exercises and examples.W3Schools also provides a comprehensive SQL tutorial covering basic and advanced concepts with examples and exercises.
4. Julia
High-Performance language Julia is respected for its ease of use, flexibility, parallelism, and large and growing package ecosystem. Julia is designed to be a high-performance language for numerical and scientific computing, making it faster than languages like Python and R for many data science applications. It has a simple and intuitive syntax, making it easy for users to write and understand code. Julia is a dynamically typed language, allowing for flexibility in data types and ease of use for data science applications. It has a large and growing package ecosystem with many packages specifically designed for data science, including packages for machine learning, data visualization, and statistics. Julia has built-in support for parallel computing, making it well-suited for large-scale data processing and machine learning applications of modern Neural Network era.
Still, Julia is a relatively new language compared to more established languages like Python and R, which means that there may be fewer resources and community support available. While Julia has a growing package ecosystem, there may be fewer libraries available for some applications unlike Python and R. While Julia has a simple syntax, there may be a learning curve for users who are not familiar with the language. Hence making it difficult to learn for some.
The official Julia documentation is a great and comprehensive guide to the language, including tutorials, guides, and examples. Julia Academy offers a range of courses and tutorials for learning Julia, including courses specifically designed for data science and machine learning. The Julia Data Science Cookbook provides a collection of recipes and examples for using Julia for data science applications. JuliaCon is an annual conference for Julia users and developers, featuring talks and workshops on a wide range of topics related to Julia and data science.
5. C++
Anyone who is someone in the field of Computer Science has complete control on devices through this humble but omnipresent C++.
C++ is a fast and efficient language, making it well-suited for large-scale data processing and machine learning applications. C++ provides low-level control over system resources, which can be useful for optimizing algorithms and improving performance. C++ has a large and mature ecosystem with many libraries and frameworks specifically designed for data science, including libraries for linear algebra, optimization, and machine learning. C++ can run on a wide range of platforms, making it a flexible choice for data science applications.
Weaknesses of C++ for Data Science:
C++ can be difficult for programmers who are not familiar with low-level programming concepts. C++ requires manual memory management, which can make it more difficult to write reliable and maintainable code. C++ is a compiled language, which means that it may be less interactive than other languages like Python or R for data exploration and analysis. But as the Computer Science Engineers say… Life lies at the Command prompt.
C++ for Data Science: A Guide is a guide that provides an introduction to using C++ for data science applications, including libraries and frameworks. There are several books available for learning C++ for data science, including “C++ for Data Scientists” by Jonathan B. Cook. The C++ Standard Library provides a comprehensive set of classes and functions for data processing and scientific computing. Armadillo is a C++ linear algebra library that provides a user-friendly interface for scientific computing and Dlib is a C++ library that provides machine learning algorithms and tools for data analysis.
For those who are using signal processing at the back end, a major tool is MATLAB or SciLab. Those who have been using JAVA for resources can surely use the same for device management and drivers instead of C++.
During 2012 and onwards we started getting access to large data sets and computational speed. Data Science also picked up on that and now is being used extensively for all major business decisions. You can learn these skills separately from online resources and do the competitions on Kaggle, Data Camp, or Github of Stackoverflow. You can also follow a degree that assimilates all this and much more where you learn the fundamentals of Data Science and learn to use these skills in a collaborative environment.
