High-profile businesses need data scientists to spot new trends through data analysis and analytics, which has contributed to the increase in demand for these professionals. If this profession interests you, you’ll need an understanding of mathematical concepts and computer science, with training in the best programming languages for data science.
Throughout this guide on the most popular languages for data science, we’ll explore the top five languages in depth. You’ll also find a simple guide on how to learn data science. Read on to discover the best programming languages to learn for data science.
What Is Data Science?
Data science is a field that combines programming skills, domain expertise, and knowledge of statistics and mathematics to get insights from data. Data scientists use machine learning algorithms and apply them to text, numbers, image processing, audio, and video to produce systems that perform tasks that do not require human intelligence.
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The systems are then used to generate insights for business intelligence purposes. The processes of data science include statistical analysis, high-performance numerical analysis, scientific computing, predictive analysis, and statistical computing.
What Are Programming Languages?
Programming languages are computer languages used by developers or programmers to communicate with computer systems. The instructions are written in specific languages such as Python, Java, and C and are used to perform a wide variety of tasks. They are used in developing websites, desktop apps, and mobile applications.
What Programming Languages Do Data Science Professionals Use?
Data science professionals use programming languages such as R, Python, SQL, JavaScript, and C/C++. Each one has unique functions with advantages and disadvantages. Some languages are better-suited to data science than others.
Best Programming Languages to Learn for Data Science
- Python
- R
- JavaScript
- C/C++
- SQL
Which Programming Language Is Best for Data Science?
Python
Python is quite popular among data scientists, particularly because of its wide range of uses. This high-level programming language is the go-to language for deep learning, machine learning, and artificial intelligence tasks. It also has powerful libraries that make tasks easier to complete. Popular libraries for machine learning include sci-kit learn, Keras, TensorFlow, and matplotlib.
Python supports analysis, data collection, visualization, and modeling. All these tasks are crucial in big data and data science. This general-purpose programming language is also used for automation, providing valuable data and helping data scientists save time. The statistical programming language is also used for software development and desktop application development.
R
R is a powerful scripting language. It is used to analyze, clean, and graph data before using it. Researchers from different disciplines use this programming language to estimate results and display them. It has become more popular in data science spaces for several reasons.
It fosters an environment for computing and graphics, which is why it is used in big data, machine learning, and data science. Since R is a scripting language, it can handle complex and large datasets. It comes in handy while performing statistical operations.
JavaScript
JavaScript is a popular open-source language that is used for web development. It is preferred because it offers the capability of developing rich, interactive web pages. It is also useful in the data science space because it helps create visualizations to cover big data. Even though JavaScript doesn’t have data science packages, it offers tools like TensorFlow.
C/C++
C is a high-level, general-purpose programming language. Many new programming languages use C as their codebase. This programming language offers the ability to compile data quickly.
C++ programmers can have more command over applications with this language. Since it is more of a low-level language, it allows developers to fine-tune aspects of applications that are not normally possible.
SQL
Structured Query Language (SQL) is an excellent language to learn if you want to pursue a career in data science. SQL gives access to statistics and data and handles structured and unstructured data. A database language like SQL is essential in handling databases. Also, the programming language is non-procedural, meaning that it doesn’t need to follow traditional programming logic.
Which Programming Language Should I Learn First?
Python is the first programming language you should learn to become a data scientist. It has large libraries that are able to deal with the complexities of data science applications and the largest community of users. It is a versatile language with a simple syntax, enabling learners to grasp it quickly.
Is It Possible to Choose the ‘Wrong’ Programming Language?
Instead of looking at it as the wrong language, you need to look at which language would best suit your project. Python and R are the most popular when it comes to data science because they are open-source languages that have huge community support. Not all programming languages are suitable for handling and analyzing data.
How to Learn Data Science
Learning data science is easier than it’s ever been with the help of the Internet. You can enroll in college and study data science or simply attend a data science bootcamp. The latter option is quicker and cheaper and will give you a good start in the data science field.
Learn Python
Python is the most widely used language among data scientists. This programming language is simple and versatile and comes with powerful libraries to reduce your need to code. Once you grasp the basic concept of Python through hands-on tutorials, you can move on to other more complex languages.
Learn Statistics
Statistics is an important part of data science, particularly in the collection of data. Once the data is collected, it is analyzed and interpreted. Once interpreted, statisticians will draw a conclusion that helps business owners to make informed decisions according to trends.
Learn From Others
The world of data science is ever-changing. To stay on top of new trends and learn new ideas, it’s a good idea to join a community. Kaggle has become a popular community for data scientists. You can share your work, find data sets, and even enter competitions. Open Data Science is another community that brings scientists, professionals, and students together. It’s a great place to learn about new trends as well as job listings.
Build Projects
It is essential to build projects to practice what you are learning. It helps to code from scratch to see how it really works. This will give you a clearer understanding of the underlying mechanisms. In no time, you will have mastered the skills you need to pursue a career as a data scientist.
How to Learn Data Science: Top Resources
- Codementor. This website offers professional and beginner tutorials. Some of the topics it covers include guides on how to analyze data, machine learning, and other basics of data science.
- Analytics Vidhya. This website offers tutorials for data science with R. Learn the basics of programming, data manipulation, predictive modeling, and data exploration.
- KDnuggets. There are several tutorials for data science students on this site. Learn about data science processes, as well as the basics of data visualization. The website also covers data scientist interview questions to help you find entry-level jobs.
- Flowingdata. This website teaches readers how to analyze, present, and understand data. It includes practical guides, as well as real-time examples to help you practice what you are learning.
- Reddit. Reddit is a well-known forum to learn everything under the sun. It offers a resource for members to share research papers and data mining resources. You can also use this forum to ask any questions you may have while learning.
Ready to Break into Tech?
Data science involves gathering, cleaning, analyzing, and presenting data to find useful business insights. This field is growing because it can be applied to many different industries. If you want to pursue a career in data science, start with mastering a programming language.
Python is the recommended programming language for beginners because it is easy to use and useful for statistical analysis. Master Python, then move to other languages like SQL, JavaScript, R, and C/C++. Data science involves learning a programming language, analytical thinking, statistics, and math. Lastly, practice can help you perfect your craft.
Best Programming Languages for Data Science FAQ
C++ is good for data science because it has rapid processing capabilities. Even though it’s one of the earliest programming languages, the compiler offers great speed and can be used to develop big data applications.
R is better for data science when it comes to data visualization and statistical calculations. However, Python is a better option for artificial intelligence, big data, natural language processing, algorithms, and deep learning.
Python is the most popular data science programming language because it offers many features that make the work easier. Python is an older programming language with high-performance data science frameworks. Due to its popularity, it has a huge support network.
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Both SQL and Python are important for data science. However, Python is a better language to learn for beginners. It has easy syntax so you’ll have no problem learning it quickly. Python is a good base for learning other languages.
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