WHAT HAPPENS WHEN A JAVA DEVELOPER SWITCHES TO DATA SCIENCE ROLE?

WHAT HAPPENS WHEN A JAVA DEVELOPER SWITCHES TO DATA SCIENCE ROLE?

A specialized programmer who collaborates with web developers and software engineers is a Java developer. They are responsible for multiple duties throughout the development cycle of applications. They are helpful for implementing and maintaining java application phases and application software through the  best Java Training in Chennai. What will happen if a Java developer switches to a Data science role after any Data Science Course in Chennai?

LACK OF FUNDAMENTAL UNDERSTANDING

When a java developer switches to Data science, there must be a lack of fundamental understanding. A Java programmer when learning the foundation of Machine Learning concepts, always felt they never get a full understanding of the algorithms. Learning Data science, one should be good in linear algebra knowledge, good calculus background or statistical experience and also math behind it. In these cases, the logistical barriers are reduced which are easier set up in a web-based environment through any PHP Training in Chennai. when the practitioner lacks in the fundamental understanding of algorithms, they would feel detached from their work no matter how high-level machine learning APIs are used.

CHALLENGES DURING IMPLEMENTATION

Each practitioner faced a lot of struggles during implementation. JavaScript developers may choose web-based frameworks because they have reduced some challenges to getting started, such as installation and programming language barriers in these hurdles. Simple datasets could be challenging too for beginners. As a result, most of the non-ML developers fumbled with implementation which can be something as fundamental as deciding the number of units needed while adding the suitable activation functions or loss functions.

 So, what to do to reduce hurdles for non-ML developers?

Some frameworks offer pre-made machine learning modules which are useful to non-specialists to make the project easily with the help of the resources. They are used if only they come equipped with best practices on how to adapt them to example problems and when and where they can be applied. For example, TensorBoard which is a embed implantation tips into the programming workflows.

TensorFlow is the most popular amongst the developers for making their chores of ML easy. But, there is still the looming question of explainability in Artificial Intelligence, which is a concern for people on both non-ML developers and customers. So, the team of TensorFlow has come with TensorBoard which has specifications like visualizing the layers, tracking and visualizing metrics such as accuracy and loss, displaying images, text, and audio data and checking how weights, biases change over time. 

According to the survey of 2020 on emerging jobs, AI-based roles have witnessed an annual growth of 74% in the past four years. There is no doubt for non-ML developers to adopt and understand Machine learning algorithms, frameworks like TensorFlow and programming languages such as Python. These are the efforts to reduce the barrier and hurdles for the non-ML developers for easier adoption. Even AngularJs Training in Chennai and RPA Training in Chennai  is in peak at current situation