Data science is still a young area, and there is a strong demand for qualified candidates and a profitable employment market. However, getting started in the data science sector may be difficult for anyone.
Regardless of the path you take, data science requires advanced coding skills. And, like with many technological industries, skill demand and expectations keep on changing. In 2021, the following AI languages will be necessary to learn data science.
What is Data Science?
Data Science is the study of information, and most businesses use it to assist them in making business choices, solving complicated challenges, and developing strategies to enhance outcomes and performance. Deep learning, Machine learning, and artificial intelligence are also strongly involved in data science. Let’s learn how the AI languages tie in, with this, in this introduction to Data Science.
Which programming language to choose for Data Science?
Before going ahead, you should think about the ideal programming language for your data science career path.
The first thing to examine is the aim you are attempting to achieve. Different activities will need varying degrees of knowledge, and several languages may be more suited to the tasks at hand.
The next step is to determine how data science can assist you in completing the work at hand. Many activities that you may already be conducting may be automated or streamlined using data science, which can save you a significant amount of time and money. You must also determine your degree of expertise in the programming languages you are already familiar with. Then, decide whether you are prepared to take your expertise to the next level.
The last thing you will need to figure out is how big your company is employing data science. This will help you decide which languages to study and learn to utilize them.
1. Python
Python is one of the most widely used data science programming languages among data scientists. This is due to its vast variety of applications, including machine learning, deep learning, and artificial intelligence. All of this is accomplished by using Python’s data science from scratch packages such as Keras, scikit-Learn, matplotlib, and TensorFlow. Python can help with data collecting, modeling, analysis, and visualization to work with large data. This programming language for data science is best suited for automation, and this is the greatest programming language for students studying data science.
2. JavaScript
Another popular data science programming language to learn is JavaScript. This is utilized in web development because of its ability to create interactive web pages, and it has the potential to be the greatest solution for developing and designing visualizations. Although it is a wonderful language to learn, it is more useful in data science for learners than for those interested in primary data science programming languages.
3. Java
Java is also known as write once, run anywhere. It is yet another prominent data science programming language that top companies utilize to protect their corporate development. Java is also used for data analysis, machine learning, and data mining jobs. This data science programming language for data scientists can create complicated applications from the ground up while also delivering findings considerably swifter than other languages. Java is distinct from other languages in that it employs garbage collection, which makes the programming language more effective.
4. R
R is growing prominence as a data science programming language and attracting a lot of attention from data scientists these days. R is also simple to learn for statistical computation and graphics. All of this makes R an excellent choice for data science experts working in data science, big data, and machine learning. Because R is a sophisticated programming language, it can handle huge and complicated data sets.
5. C/C++
C is an excellent data science programming language for learning data science applications since it was one of the first computer languages, and C/C++is its codebase. Because of the code base, many data scientists are not good with C/C++. This programming language is far more versatile in terms of its applications. The advantage of C/C is that it allows developers to delve deeper and fine-tune program areas that would otherwise be impossible.
6. SQL
SQL is the most important data science programming language used to train data scientists. This programming is required to work with structured data. SQL provides access to data and statistics, making it a valuable resource for data research. A database is required for data science, which necessitates using a database language such as SQL. Those working with large data must have a strong grasp of SQL to query databases.
7. MATLAB
MATLAB is a sophisticated tool for mathematical and statistical computing that enables the implementation of algorithms and the construction of user interfaces. MATLAB makes UI building simply because of its built-in graphics for making data plots and visualization. Because of its deep learning features, understanding MATLAB is a useful approach to transition into deep learning effortlessly.
8. Scala
Scala is a sophisticated data science programming language that is ideal for data scientists. Scala is best suited for working with large amounts of data, and it enables compatibility with Java, which opens up a plethora of possibilities for data scientists. You may also use Scala in conjunction with Spark to manage enormous volumes of siloed data. This data science programming language also comes with a plethora of libraries.
9. Julia
Julia is another popular language that is becoming increasingly popular. It is a general-purpose programming language designed for numerical analysis and scientific computation. As a result, several high-profile companies are working on time-series analysis, space mission planning, and risk analysis. Even though Julia is a dynamically typed language, it may be utilized as a low-level programming language if necessary.
10. SAS
SAS is a statistical analysis tool that is used for analyzing statistical data. The tool’s primary function is to retrieve, report, and analyze statistical data. SAS is likely to provide a plethora of chances in the near future.
Conclusion
Data science is a rapidly developing and in-demand field with several coding languages involved for various specializations and disciplines.
You will dabble in each of the languages listed above at some point along your journey to becoming a data scientist, but if you’re genuinely eager to get started, an introduction to data science can help you to boost your career. Various free data science courses can help you start and lay the foundation for advanced courses in the domain.