If you’re interested in pursuing a data science career, you’ll need to understand effective programming languages to learn for the daily job responsibilities as a data analyst. As of 2025, jobs for data professionals are expected to grow 34 percent through 2034, according to the U.S. Bureau of Labor Statistics (BLS)—which is much higher than average.1 This gives us an idea of how in demand data scientists are in the current job market, as well as their projected demand throughout the next decade or so.
Learning programming languages for data analysis can help you launch a successful career in the data science field. As this guide will cover, there are no “wrong” languages to learn, but there are several programming languages that stand out for data science. This guide will also cover resources that can help you learn data analysis skills like data visualization, statistical analysis, machine learning, and more.
Key Takeaways
- Programming languages for data analysis to explore are Python, R, SQL, Java, and Scala.
- Python is a versatile, object-oriented language widely used for data analysis, while R is excellent for statistical analysis and data visualization.
- There is no “wrong” language to learn for data analysis, as all have value in the tech industry.
- To learn data analysis, beginners should learn Python as a foundational step, mastering the fundamentals and data analysis tools like Excel, Python, R, and querying languages.
- Some resources for learning data analysis include Khan Academy, Kaggle, KDnuggets, GitHub, and DataCamp.
What Is Data Analysis?
Data analysis involves the process of changing, cleaning, and processing raw data. Part of what data analysts do is extract relevant information to make informed decisions. They provide a risk analysis that helps companies make informed decisions based on current trends. The analysis results in valuable statistics and insights that are typically presented in tables, charts, graphs, and images.
What Are Programming Languages?
Programming languages give instructions to computers. A high-level programming language is typically more user-friendly and easier to read and write than a low-level programming language. The source code of high-level languages uses a syntax that is easy to read.
This is then converted into a low-level language that the central processing unit can recognize. Popular high-level languages include C, C++, Java, and JavaScript. Processors run low-level languages without the need for an interpreter. These are machine languages that computers understand directly.
Best Data Analysis Programming Languages
- Python
- R
- SQL
- Java
- Scala
What Is the Best Programming Language for Data Analysis?
The best programming language for data analysis depends on what your exact needs are. Your domain expertise can also influence which language is most effective for your data analysis tasks. However, when you are looking into languages for data analysis, there are several options that stand out. Nonetheless, all data analysis languages have strengths and weaknesses.
1. Python
Python is one of the most popular programming languages in the tech world. This multi-purpose, object-oriented language is easy to read and works well for data analysis. It can be used to extract information, create coding applications, and build websites.
To use Python for this purpose, you may be required to download libraries to reduce the amount of required coding. The programming language has a wide range of applications and is beginner-friendly; however, it can take time to set it up for data analysis.
2. R
R is an easy-to-learn language that allows you to find trends and patterns in your data without requiring as many extra libraries as Python. R is often considered the go-to language for statistical computing and data analysis. It can be used for data visualization and statistical analysis.
Data analysts use R because it offers statistical packages for quantitative applications. They include neural networks, phylogenetics, advanced plotting, and nonlinear regression. R is also an open source language designed to accommodate changes.
3. SQL
SQL is a powerful scripting language that allows users to communicate with relational databases, search within them, and collect data for use. It is a popular programming language for data science, and its intuitive syntax is quite easy to learn since it is built for a specific purpose. SQL enables users to retrieve specific data and manipulate data efficiently within relational databases.
4. Java
Java is a general-purpose language that runs on the Java Virtual Machine (JVM). This high-performance language offers powerful tools for integrating analytical methods and data science into a codebase. Many modern systems today are built on the Java backend. This popular language is an important tool for data applications.
Java enables seamless portability between platforms. This makes it able to write computationally intensive machine learning algorithms and specific production codes. It is ideal for dedicated statistical applications and ad hoc analyses.
5. Scala
Scala is a programming language with both functional and object-oriented approaches. The multi-paradigm language runs on JVM, which is why many data analysts prefer to use it, especially those who work with high-volume data sets.
Scala performs well with Apache Spark, the cluster computing framework. This makes it easy to work with massive collections of data. Scala is compiled with Java bytecode, making it possible for the language to work with Java. It offers a wide variety of features for both data analysts and data scientists.
R vs Python: Which Is the Better Language for Data Analysis?
When considering Python vs R for data analysis, you first need to think about the end goal. For example, R is the better choice for data visualization and statistical analysis. On the other hand, Python is a more versatile language and can be used for replicability and general data science tasks.
Differences Between R and Python
- Difficulty of learning. Python is the perfect choice for coding beginners, whereas R can be more difficult to learn.
- Maintenance. One of the key differences between R and Python is that Python code typically requires less maintenance than R code.
- Libraries. R has fewer and less complex libraries than Python does, making this aspect of the coding language easier to grasp.
- Uses. As mentioned above, the two languages have different strengths. R is better for making complicated statistical calculations, while Python is better for things like deep learning.
Which Data Analysis Language Is Best for Beginners?
Python is one of the first data analysis languages you should learn to pursue a career as a data analyst. It is one of the best programming languages for beginners in data analysis because it is easy to use. Even though Python is a general-purpose language, it is inherently object-oriented. It supports multiple paradigms, such as procedural, functional, and structured programming.
These features make it useful in several settings and not just for data analysis. The language can be faster for data manipulation. Its packages also make natural data processing easy. Analysts can easily read data in a spreadsheet using a CSV output in Python. Further, it is a powerful tool for a range of tasks, including deep learning algorithms, natural language processing, and scientific computing.
How to Learn Data Analysis
Becoming a data analyst can take some time. This is because you need to learn certain topics that prepare you for the workforce. You can enroll in an online coding bootcamp or an online course to learn data analysis. These programs first help you learn the basic concepts; you’ll then advance to more complicated topics in data analysis. Plus, learning data analysis online gives you the flexibility to continue working while developing new skills.
Learn the Fundamentals
The first step to learning data analysis is mastering the fundamentals. You also need to cover tools for data analysis, such as Tableau, Power BI, and Microsoft Excel, as well as programming languages like R and Python, to help you create easy-to-understand visualizations. Once you have a solid grasp of data analysis fundamentals, you can tackle more complex concepts.
Learn Your First Programming Language
Once you’ve got the basics, focus on one programming language. The most popular programming languages for data analysts include Python, SQL, and R. Every language has pros and cons, but consider what you need to accomplish before selecting a language as a data analyst. You also need to remain up to date with data analysis tools such as querying languages and spreadsheets.
Practice Building Visualizations
Practice will help you master what you have learned so far. You need to use programs like Power BI and Tableau for this step. Begin to build visualizations on your own to see how you can apply what you have learned. Tools like Excel are also important for making calculations and graphs by simply adding information into the cells.
Join Forums
Learning never stops in the field of data science, so joining a community of experts in the field to exchange ideas is important. It helps to join forums where data analysts share their work. You can use the forums to sharpen your skills and network within the industry. Some forums to consider include Reddit, GitHub, and LinkedIn.
Resources to Learn Data Analysis
There is a wide range of resources available to help you learn data analysis, including free options. Learning data analysis can be done at your own pace and remotely with the following tools and platforms.
- Khan Academy. This website offers tutorials on statistics, math, linear algebra, and calculus. It also covers foundational concepts in numerical analysis relevant to data science. It is perfect for people who have no existing knowledge of data analysis.
- Kaggle. This platform offers resources and tools to help you learn data analysis. Users on the platform can publish complex data sets, build models, and explore them. Kaggle is ideal for working with complex datasets in real-world scenarios.
- KDnuggets. KDnuggets offers tutorials in data mining, artificial intelligence, big data analytics, and machine learning. It also contains educational tools for professional development.
- GitHub. GitHub is not just a code repository. It also contains projects and tutorials on machine learning and data analysis. You can also use the platform to build your portfolio.
- DataCamp. This interactive platform offers data analysis courses and can come in handy for complete beginners in the field. It offers a limited free version as well as pricing tiers if you want to dive deeper into mastering data science skills and data analytics tools.
Conclusion
Data analysis is a crucial field that helps businesses get ahead of their competition. This field reduces the risks associated with uninformed decision-making. To analyze data, you need to work with certain programming languages.
Python is one of the best programming languages for data analysis to learn because of its ease of use. However, as we’ve covered, several other languages are also excellent choices for data analysis and advanced analytics. By joining a coding bootcamp or taking online courses, you can work on data science projects to sharpen your data analysis skills and begin working in the tech industry.
Programming Languages FAQ
The best programming language for a data analyst is Structured Query Language (SQL) because of its ease of communicating with databases. However, Python is a better option for other main data analysis functions, such as data analysis, data manipulation, data cleaning, and data visualization.
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Is C++ good for data analysis?
C++ is excellent for data analysis because it has rapid processing capabilities. While it may not be a data analyst’s favorite, the programming language offers a quick compiler that comes in handy for data analysis. Conducting data analysis with C++ is more efficient than using other programming languages.
What are the best data science programming languages?
Some of the best data science programming languages are Python, R, Java, SQL, and Scala. Each of these languages has unique features that are best used in different aspects of data science. Having some programming skills in multiple languages can help you complete a wide range of data science tasks.
Python or Java: Which is better for machine learning?
Python is a better option than Java when it comes to artificial intelligence, machine learning, and data analysis because it is a multi-purpose programming language. Some developers prefer it over Java because it offers accessibility, ease of use, and simplicity. Java may be faster, but Python is considered easier to use overall for machine learning.
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