Data science is a field that has attracted a lot of attention in recentyears. It offers the chance to work on the latest programming languages and with different industries, mining their vast reserves of data to extract valuable insights. These could guide the future strategic decisions of a company and take it to new heights of success.
These prospects make a data science career a highly attractive choice. What is important is to have the right mindset and the right skillset to succeed:
- Mindset: an analytical bent of mind, a curiosity to learn, and the ability to deal with challenging assignments are critical elements for a data science professional.
- Skillset: the data science industry requires a professional to master the most important and the newest programming and coding languages to be able to drive the best analysis.
Why are these languages in demand?
The primary factor is the increasing adoption of technologies that were experimental not so long ago. Augmented reality (AR) and virtual reality (VR), artificial intelligence (AI), machine learning (ML), deep learning (DL), and other technologies are seeing much wider usage, which is leading to high demand for certain languages. Certain job roles for data science training also require competence with particular languages, as do factors such as particular job roles and the languages they need. And the rapid evolution of the industry always brings up new developments and trends and hence demands for new languages.
What factors must be considered when choosing a language for a career?
Before choosing a language or languages to learn for a data science career, the following are the key considerations:
- Corporate objectives and organizational usage of data science
- Nature of tasks to be handled
- Programming languages already known
- Career interests
- Difficulty level candidate is prepared to handle
Which are the current languages essential to know in data science?
The top languages presently in demand in the data science industry are as below:
- Python: possibly the top choice, Python could be considered a prerequisite to work in data science. Its USPs include its flexibility, which allows it to be used for a wide range of requirements, such as:
- Classifying, sorting, and categorizing data
- Developing decision trees, random forests, and other ML algorithms
- Creating web services with the use of Django and Flask frameworks
- Data mining through NumPy, SciPy, and other modules
When combined with experimental analysis and quantitative aptitude, this makes for a great skill set.
- R: one of the fastest-growing choices, R is well suited to numerous statistical applications and models. It can be used for regression as well as data visualization, offering support for different types of charts. It finds application in ML, with smart applications being created by using Class, Gmodels, RODBC, and TM. It is a great choice for research reports and papers.
- Java: this is preferred by a number of users over other newer languages, primarily due to the ease of scaling it up to bigger applications. A Java-based project can be scaled up as required without affecting the results or performance, which makes it an excellent option for ML systems set up on a large scale. Java runs on the Java Virtual Machine (JVM), a very sophisticated environment that makes it a favorite of desktop, mobile, and web developers. Popular uses for Java in ML and the corresponding libraries are:
- DL: DL4J
- Data mining: ADAMS
- ML algorithms: Java ML
- Creating and training neural networks: Neuroph
- Executing natural language processing (NLP) tasks: Stanford CoreNLP
What are the new and upcoming languages in data science?
In addition to the established languages, there are a number of up-and-coming choices for data science professionals. These may not yet be popular at scale among enterprises, but they see increasing applicability and are growing fast. Here are the top choices:
- Kotlin: used for developing apps for Android, Kotlin is an open-source, fast language that is easy to learn. It is designed such that it is fully interoperable with Java, and it is a prime choice for Android mobile development. By bringing together functional programming with object-oriented programming, it provides features that developers demand. It is a top choice to address major Java issues, which is why many Java apps are rewritten in Kotlin.
- Swift: this is a general purpose open-source programming language launched in 2014 by Apple. It works beyond the Apple ecosystem, and it can be used by developers to create apps for Linux. It is a great choice for beginners in programming, with a highly-simplified syntax and type-safe variables.