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Data Science is a thriving industry because it deals with the abundance of information that is being generated every day on the Internet. This data could be in any format and it comes from multiple sources.
The field’s purpose is to organize data to get something meaningful out of it by using machine learning algorithms. This information helps businesses and institutes to make more informed decisions and it also facilitates predictive analysis and pattern discovery.
According to a report by Dice, the demand for data scientists increased by a whopping 50% in the year 2020. This surge in demand was seen in multiple sectors including:
- Healthcare
- Entertainment
- Telecommunications
- Insurance
- Banking
- Finance
As demand for new data scientists is increasing every year, more people are inclined towards getting into this field by learning the relevant languages and we will be discussing those languages in this article.
5 Popular Languages for Data Science
Although there are dozens of languages that are being used for this purpose, these five are the best options for new programmers.
#1: Python
Python is the most popular programming language, with its popularity expanding over many domains, including data sciences and other relevant technologies such as machine learning, artificial intelligence, deep learning, etc.
It is fairly versatile with its applications in most of the technological domains. Additionally, being a dynamic language, it’s easy to learn. The code readability is also extremely good and any person with basic Python knowledge can understand what’s going on under the hood.
It is also extremely efficient in data collection, modeling, analysis, and visualization, all of which are necessary for this field. The best thing about Python is that it offers a range of libraries for each domain, be it machine learning, game development, or data science.
Here are a few popular, relevant libraries:
- TensorFlow
- NumPy
- Pandas
- SciPy
- Matplotlib
- Keras
There is no doubt that Python has some of the most desirable tools to cater to the needs of data scientists for visualization, analysis, or even modeling. If you want to take a fresh start, then try these impressive Python courses to kickstart your career.
#2: JavaScript
The majority of web pages use JavaScript for the frontend of a website. It is used to make those pages more responsive and user-friendly.
Despite its popularity in the web development space, JavaScript is also used in data sciences. We cannot say that the language was designed with this in mind, but it still works well in some areas.
JavaScript is very reliant when it comes to visualization, and very scalable for large applications. Moreover, it is a versatile language with many different uses in multiple domains. The following are some frameworks dedicated to data sciences, machine learning, and other relevant fields:
- Django
- Next.js
- Flask
The best use case of JavaScript is found in web development. It is used along with HTML and CSS to create interactive websites for users. However, it does not have many libraries to offer when it comes to data sciences.
If you are eager to learn more, then check out these amazing JavaScript courses to start learning the basics.
#3: Java
Undoubtedly, Java is one of the most solid languages, with many real-world use cases. The code is only written once and then it can be executed unlimited times on as many machines as you want. Moreover, it is popular for building complex applications, entirely from scratch.
Java is used almost everywhere and it has many applications, including web app and Android development. Now the language horizons are shifting a bit; despite its popularity in web and mobile app development, it is now more frequently used for data science applications, IoT and even machine learning.
The execution time for Java code is much faster and it is capable of handling many complicated development tasks such as:
- data analysis
- natural language processing (NLP)
- deep learning
- data mining
A few compatible libraries and frameworks include:
- DL4J
- ADAMS
- JavaML
- Mahout
- Neuroph
- RapidMiner
There is no doubt that Java is one of the most reliable languages for this field, with availability of all the required tools. You can also learn it by using these best Java courses to take you from a beginner to an advanced level.
#4: R
The R language was officially made for statistical computing and graphics. Due to its simple syntax, it is fairly easy to learn and is getting popular in the data science field. The language is highly extensible and versatile, just like Python.
Moreover, R has numerous uses in many relevant domains, such as big data and machine learning. The language is highly reliable for handling all the statistical operations happening in this environment. A few of its compatible libraries are listed below:
- Dplyr
- Ggplot2
- Esquisse
- BioConductor
R has an abundance of packages; it’s also quite reliable for machine learning operations. If you want to step into these languages, then start from these best R courses.
#5: Scala
Scala was built on Java, and it inherits many of its features from the senior language. However, Scala was created to cover the flaws of Java which were undesirable for programmers. The code runs on Java Virtual Machine and it has great interoperability with its predecessor.
Just like Java, Scala also has multiple applications in machine learning and artificial intelligence. In data sciences, the language is reliable for handling high volumes of information. The number of libraries are countless, but the ones listed below are dedicated specifically to this domain:
- Breeze
- Spire
- Saddle
- Scalalab
- Smile
- Vegas Smile
If you are not yet familiar with the language, then grab the most favorite Scala courses and start learning the basics.
Bottom Line
This industry is constantly growing and the need for new data scientists is rising day by day. The languages described above are the best fit for all the young programmers who are looking forward to starting their careers in these developing fields where the potential is limitless.
Take the great data science courses to speed up your progress in the niche.