How to Learn Data Science Programming Languages

The increasing adoption of artificial intelligence (AI) technologies across industries is one of the key factors driving demand for data scientists, software developers, and database administrators and architects. Knowledge of data science programming languages is a common requirement in all of these occupations, which are all predicted to grow between 2024 and 2034, according to the U.S. Bureau of Labor Statistics (BLS).

Here is a brief overview of what each role entails and its growth projections:

• Data scientists transform raw data into actionable business insights; positions are projected to increase 34%.
• Software developers create and maintain software solutions reliant on data; positions are projected to increase 16%.
• Database architects set up and manage complex data infrastructures; positions are projected to increase 9%.

AI and data science go hand in hand, making data science programming language skills an important competency in both fields. A master’s in data science program teaches students crucial programming languages through hands-on coursework and real-world skills application.

Data Science Programming Skills

Knowing which language is right for the task is a key element of data science programming. Core data science programming languages include SQL, Python, and R. SQL ensures access to structured data, while Python and R provide the flexibility to analyze, scale, and visualize data to develop models that solve complex problems.

Core Data Science Programming Languages

Used together, data science programming languages help data professionals clean, analyze, and make sense of complex data sets. They allow data scientists to turn raw information into meaningful business insights.

Python

Python is often the first language data scientists learn because it is straightforward yet powerful. With access to libraries like Pandas for data manipulation and TensorFlow for machine learning, data scientists have ample tools to transform raw data into useful information. Python’s readability and flexibility also help speed up debugging and collaboration tasks.

R

R is designed for statistical computing and visualization. It’s especially valuable for data-heavy projects that require statistical models and graphics. R is flexible and can be used with other programming languages like C++, which data scientists can link with R for computationally intensive tasks.

SQL

SQL, created in the 1970s to handle data storage and retrieval tasks, is the language of databases. Data scientists still use this language to query, update, and manage data across traditional relational databases and modern data systems. SQL works with cloud platforms, where data analytics and AI systems often live. SQL can also be used to support real-time analytics, machine learning pipelines, and security systems.

Supporting Tools

Data scientists also rely on specialized tools to expand their capabilities:

• Apache Spark for large-scale data processing and analytics
• D3.js for custom, browser-based data visualizations
• Matlab for numerical computing and modeling
• SAS for statistical analysis and business intelligence
• IBM SPSS for advanced statistical analysis and reporting
• Jupyter Notebook for interactive coding, collaboration, and results sharing
• Keras for a streamlined interface for building machine learning models

3 Benefits of Earning an Advanced Data Science Degree

Earning a degree in data science prepares individuals with key skills that can help them advance in a growing field and improve their earning potential. For anyone pursuing career advancement in data science and AI, there are a host of reasons to enroll in a graduate program. Here are just three.

1. Entering or Advancing in the Growing Data Analytics Market

AI and data are the tools that drive smarter decision-making. Skilled professionals are needed to guide the use of these tools toward actionable results. In an advanced degree program, students gain expertise in applying data science methods, interpreting insights, and helping their organization stay competitive. The data analytics market is expected to reach $483 billion by 2032, according to Yahoo Finance.

2. Developing Data Science Programming Language Skills That Drive Impact

In an advanced data science degree program, individuals become skilled in using different programming languages and learn the best use cases for each. The combination of technical ability and strategic thinking skills they gain in the program allows them to connect new ideas with a business’s needs, enabling them to help their organization succeed in its AI and data analytics endeavors in a rapidly changing market.

3. Earning a Competitive Salary

The median annual salary for data scientists was $112,590 as of May 2024, according to the BLS. Professionals in related roles are also well compensated, with the BLS reporting that software developers had a median annual salary of $133,080, and for database architects it was $135,980 in May 2024.

Prepare to Advance in Your Career in Data Science and AI

The demand for data science programming language skills is evident: In 2024, 90.5% of leaders cited AI and data investments as a top priority, up from 87.9% the previous year, according to Harvard Business Review.

To build their expertise in these rapidly evolving fields, individuals can pursue an advanced degree that equips them with deep knowledge of data science programming languages to help them prepare to shape the future of technology. In addition to this essential foundation, students in the online Master of Science in Data Science program at Tseng College at California State University, Northridge, explore data theory and engineering, applying their skills to real-world scenarios.

Find out how CSUN’s master’s in data science can elevate your career in data science and artificial intelligence.