With data science being one of the hot careers to choose today, it is not surprising to see many youngsters wanting to pursue this field. Apart from the challenges and excitement that you get to experience in your day-to-day data science job, you are also paid quite well, compared to other fields. So, if you decide to become a data scientist, we completely understand where you are coming from.
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When choosing a data science career, one of the first things you should know is what programming language to pick up. Contrary to popular belief, there is more to data science than Python and R programming languages. A successful data scientist is the one that knows different skills like math, statistics and computing. Artificial intelligence, machine learning, deep learning, network analysis, natural language processing and geospatial analysis are some of the new skills that data scientists need to equip themselves with in order to stay ahead of the competition.
To carry out their tasks effectively, and to help the management take data-driven decisions, data scientists make use of different programming languages to help them deal with large volumes of data. If you are wondering which programming language you should learn to become a successful data scientist, you have come to the right place. Here are 12 popular programming languages of 2023 you should study to upgrade your skills and get an edge over your competitors:
- Python
When it comes to data science, Python is undoubtedly the most popular programming language used. It is an open-source language, and has unlimited resources like libraries, dictionaries and data sets that keep updated regularly. It is an easy language to learn as its syntax is similar to English. It is flexible and has a wide scope in all the fields of data science.
- R
Though people often consider R as the competitor of Python, it is not as popular or as sought-after as Python. However, knowing R is one of the prerequisites for you to get into the data science domain. Usually used for finance and academic domains, R is also an open-source language like Python. Machine learning, data visualization and deep learning are some of the powerful techniques that can be easily done on R.
- SQL
SQL (Structured Query Language) is a querying language used to extract data from databases. As you know already, knowing how to handle large and complex databases, and applying the latest SQL tools to extract and edit data in these databases are essential skills needed for data scientists. The good thing is that SQL is very easy to learn because, like Python, its syntax is simple, and easy to grasp, especially for beginners.
- Java
Around 10 years ago, Java was one of the most popular programming languages in the world. Though it is still popular in many countries, it has definitely been overshadowed by languages like Python in the recent past. However, to become a successful data scientist, you should be aware of Java and its tools, because many web developments and software apps are still dependent on this language. It’s an open-source language, and is mostly used for ETL (Extract, Transform and Load) operations on databases.
- JavaScript
Known for its versatility and interactive features, JavaScript, which was mainly used in the web development field, has now earned a popular name for itself in the data science field. This is because this language is compatible with many machine learning and deep language techniques that use NLP technology. One of the major highlights of JavaScript is that it supports some important Python libraries like TensorFlow and Keras, which data scientists use extensively.
- C / C++
C and C++ are known for their speed, especially when compared to other programming languages. This makes them a good fit when you work on Big Data and Machine Learning Concepts. Most machine learning libraries that you find in other languages like Pytorch and TensorFlow use C++ for their codes. However, both C and C++ are quite complex; therefore, choosing to learn them first may not be a good idea, especially if you are new to programming languages.
- MATLAB
Developed in 1984, MATLAB, has been used extensively used in data science projects involving complex math, science, statistics, research and academic concepts. However, the main disadvantage of MATLAB is that it requires you to pay an exorbitant fee for getting the license to use the language. Since most other programming languages in this list are available for free, not many data science companies opt for MATLAB, even though it suits complex projects.
- SAS
SAS (Statistical Analytical System) is not as popular now as it was a few years ago. However, some companies still use this software, for projects involving statistics and advanced computing. SAS, like MATLAB, requires you to pay a fee to get the license. So, not many companies and industries opt for it.
- Julia
If there is one programming language that holds a lot of promise, especially in the field of advanced computing, it has got to be Julia. Though it was developed only in 2011, Julia is expected to rise to alarming proportions in the coming years. It is already considered a worthy successor to Python, the most popular programming language used in the field of data science. Since it is a newbie language, you may not find many Julia users currently. It also doesn’t have as many libraries as Python.
- Scala
A multi-paradigm language, Scala, in short words, can be explained as the shorter and crisper version of Java. Developed in 2004, Scala may not be as popular as some of the programming languages explained here, but it is considered one of the best for projects involving machine learning and Big Data. You can use this language along with Java, while working on big projects, because Scala, like Java, is executed on the Java Virtual Machine.
- Swift
Developed by Apple in 2014, Swift has been designed with mobile devices, wearable internet-enabled gadgets and the Internet of Things in mind. This can be used along with Python, and you can access the TensorFlow library through Swift. It is compatible with IoS and Linux Operating Systems as well.
- Go
Introduced by Google in 2009, Go is extremely popular, especially for projects involving machine learning. Though it is not very popular, it is expected to soar high soon, due to its simplicity and ease of operation.
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