WHAT ARE PROGRAMMING LANGUAGES?
Programming languages continue to evolve in both industry and research, as systems and applications change. Today there is a wide variety of programming languages with different languages, syntax, and features. Developers can now use a language based on either the client's preference or their own. Language technology developed between 1967 and 1997, giving birth to some of the most popularly used programming languages even now. The swift growth of the internet in the mid-1990s was a major boost to the programming language world.
If you are interested in getting into the field of data science, you need to become proficient in several programming languages because a single language can’t solve problems in all areas. Without mastering the specific ones frequently used in data science, your skillset will be incomplete. Demand for these languages, like Python, started surging in the 2010s along with the rise of data science.
A lot of these demands are directly associated with a set of thriving technologies that are now gaining mainstream adoption. The momentum from the cloud, artificial reality (AR), virtual reality (VR), artificial intelligence (AI), machine learning (ML), and deep learning is driving the demand for certain languages. Moreover, specific languages complement different job roles in data science, like business analyst, data engineer, data architect, or machine learning (ML) engineer.
WHICH ARE THE MOST POPULAR PROGRAMMING LANGUAGES?
For at least the next five years, Python proficiency will top the required skill set in data science. By knowing Python, combined with a strong aptitude for quantitative reasoning and experimental analysis, you can strike gold in the industry. One of the factors that make Python stand out from the rest is its flexibility. If you have Python in your toolset, you can build solutions for a wide range of use cases. Currently, Python is mostly used to:
- Perform data mining with modules like NumPy, SciPy
- Create web services with the Django and Flask frameworks
- Classify, sort, and categorize data
- Develop ML algorithms such as decision trees and random forests
For the past three decades, Java has remained a favorite among desktop, web, and mobile developers. It runs on the back of a highly sophisticated environment, known as JVM (Java Virtual Machine). Java is used extensively by enterprises in favor of other modern languages, mainly due to the degree of scalability that it provides. Once a project is launched in Java, it can scale without any compromises on performance. Hence, it is viewed as a popular choice to create large-scale machine learning systems. Some of the popular Java libraries for machine learning include:
- DL4J – To engage in deep learning
- ADAMS – To perform data mining
- Java ML – To implement machine learning algorithms
- Neuroph – To create and train neural networks
- Stanford CoreNLP – To execute NLP (natural language processing) tasks
PROGRAMMING LANGUAGES CERTIFICATION COURSES AND EXAMS PROVIDED BY iLEARN
iLEARN offers the following certification courses and exams in the Programming Languages area:
- Java certification training
- Python Training
- Introduction to Robotic Process Automation (RPA)