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The Role of Programming Languages in Data Science

The Role of Programming Languages in Data Science

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The field of data science has grown rapidly in the past decade as organizations increasingly embrace data-driven decision-making. Data science professionals use numerous analytical tools and statistical techniques to gather, model, and understand data.1 Programming languages are pivotal for completing these processes.2

Much like how humans use language to communicate with each other, we can use programming languages to communicate with computers. Programming languages are mainly used in data science, computer science, and engineering, although new use cases are discovered frequently.

Most data science jobs require proficiency in at least one programming language. Professionals frequently use Python, R, SQL, and more. Each system has different uses and limitations, so many people study multiple languages to broaden their skill set.1 This article explores popular data science programming languages, their applications and best practices.

Overview of Data Science Programming Languages

Data scientists use programming languages to perform various tasks, such as:1

  • Data Storage: Professionals use SQL and other languages to build, access, and extract information from databases
  • Data Modeling: Programming languages like Python enable data scientists to create models simulating real-world problems
  • Data Visualization: Create visual representations of datasets with data science libraries, statistical analysis tools and programming languages

Programming languages have varying functions and capabilities that make them suitable for different projects. They can be used for web development, mathematical and statistical computing, functional programming, machine learning algorithms, data analysis, deep learning, and much more.

Programming languages are essential in the data science industry, so it's important for tech professionals to understand each language and its distinct capabilities. Some people may even choose to specialize in one data science programming language to become an expert and expand its applications.

No matter which route you take, here are the top six languages frequently used for programming in data science.

Python for Data Science

According to the Data Science Skills Survey 2022, 90.6% of data science professionals use Python for data science and statistical modeling.3 This popular programming language has simple syntax—or rules—that make it easy to read and learn. Data scientists can write code in Python for projects of all sizes and levels of complexity.2

Python has a robust ecosystem of data science libraries, including NumPy, Matplotlib, and pandas. These libraries have pre-written code that data scientists can adapt for their own projects, such as data manipulation and processing.2

R for Data Science

Approximately 38% of professionals use R for data science.3 This open-source language has a large community of enthusiasts who provide technical assistance and resources for other users.2

R currently has 14,000 packages, which are bundles of user-written code that perform various functions. Data scientists use specific R packages for data manipulation, statistical analysis and other processes.2 They also extend R’s capabilities by integrating it with other programming languages, such as C, Python, Java, and JavaScript.4

SQL for Data Science

SQL is an abbreviation for Structured Query Language.5 Around 53% of professionals use SQL for data science, making it the second most popular programming language behind Python.3

SQL allows data scientists to build relational databases with information organized into tables. They also use this language to query and retrieve specific data from databases for analysis. Additionally, scientists use SQL to manipulate data by aggregating and reorganizing it to answer specific questions.5

Java for Data Science

Java is a versatile programming language with a simple syntax that’s relatively easy for beginners to learn. This language processes vast amounts of data quickly and can create scalable applications. Additionally, data scientists use the Java Virtual Machine to write cross-functional code for multiple platforms.6

Professionals who use Java for data science can also code machine learning models and write artificial intelligence algorithms. These tools enable data scientists to process and analyze big data more efficiently.6

Scala for Data Science

Scala is a unique programming language that incorporates functional and object-oriented programming elements. Developers have created several Scala data science libraries, including Breeze, Smile, and Vegas. Professionals also integrate Scala with other programming languages, such as Java.7

Programmers use Scala for data science projects that involve enormous quantities of data. This programming language performs multiple processes simultaneously, improving performance.7

Julia for Data Science

Julia is a relatively new programming language that can process data faster than Python and R. It can also be used with C and Python libraries, so data scientists can draw on their previous coding knowledge and integrate Julia into existing databases.7

Professionals typically apply Julia for data science projects involving data modeling and numerical analysis. As a result, this language is popular in the finance industry.7

Choosing the Right Data Science Programming Languages

Data scientists must consider many factors when selecting data science programming languages, including:8

  • Industry norms
  • Suitability of the language for specific project requirements and tasks
  • Ease of use
  • Time requirements
  • Community support and available resources

Language Integration and Hybrid Approaches to Programming for Data Science

Data scientists leverage multiple languages and tools to develop complex data science projects. For example, you can integrate R and Python to analyze a data set. R allows you to clean data, analyze it, and transform it into stylish visualizations that represent trends with graphics. You can also use Python to build machine-learning models. This hybrid approach to data science coding allows you to optimize workflows and use appropriate languages for each task.9

Best Practices and Tips for Efficient Data Science Coding

Current and aspiring data professionals use many strategies for efficient programming for data science.

Learn Data Science Software

Data science software increases productivity and accuracy by streamlining programming and other tasks. There are many popular software applications, such as:10

  • Jupyter Notebook: Collaborate on data projects and create visualizations
  • Microsoft Power BI: Analyze and visualize data
  • Apache Spark: Perform advanced analytics on structured and unstructured data
  • RapidMiner: Collect data, build models, and develop visualizations

Use Data Science Libraries

Developers have created many data science libraries that streamline coding by providing pre-written functions and tools. Here are three popular Python libraries:11

  • Scikit-learn: Tools for machine learning tasks, such as data preprocessing and predictive modeling
  • TensorFlow: Used for natural language processing and image recognition
  • PyTorch: Enables professionals to develop and train neural networks

Stay Ahead of the Latest Trends in Data Science

Accelerate your career advancement by learning Python, R, SQL and more. New York Tech’s Online Master’s in Data Science teaches you about the latest trends in data science and allows you to study emerging programming languages like Julia. Our renowned faculty also helps you learn in-demand skills like data visualization and machine learning that you can apply in many industries. Develop your expertise further by completing an optional thesis on a topic of your choice.

Don’t delay your professional development. Get in touch with an admissions outreach advisor today to learn more.

New York Institute of Technology has engaged Everspring, a leading provider of education and technology services, to support select aspects of program delivery.