How can you future-proof your Process Automation skills with programming languages?

As we are moving from automating simpler process to more complex ones, it’s important to have working knowledge of any object-oriented language. I would say C# and Python must have. In my honest opinion, to future-proof your automation skills programming is essential and regular practice of these skills are mandatory.

I strongly believe in learning programming fundamentals and constructs rather than focusing on any specific programming language. The rest, such as syntax, best practices, and a few features, can be understood by staying updated with the latest developments. Once you have a solid grasp of these fundamentals, you can program in any language.

Contrary to most views and with an expectation of backlashes, my 2-cents are that the basics of any programming language(Algo, DS) should be suffice to get one started with *RPA*. RPA products were always projected as Low Code/No code for a reason however knowing processes, VB and Linq should bring one to advanced level and then sky is the limit.

- In the current automation era, solely possessing knowledge of the automation tool is insufficient for tackling complex scenarios. - Utilizing a programming language becomes crucial for achieving unique and advanced features. - Learning a programming language facilitates seamless implementation of exception handling, debugging, and other essential concepts. - Proficiency in programming enables the creation of reusable components, leading to significant reductions in development time and cost. - Acquiring programming skills contributes to the enhancement of analytical and problem-solving abilities. - Mastery of programming languages results in a drastic improvement in workflow speed.

At times we can configure and deploy vendor independent queries, automations and also at times these languages also add parallel skill set to increase additional benefits from tool customisation to very high extent

While there is no definitive answer to this question, we can still choose a programming language that suits our preferences and is easy to learn. Any object-oriented language can be considered. Some quick examples include Python, Java, and C#. My personal favorites are C# and Python. Once we have a deep understanding of the concepts of one programming language, grasping a different language will not be a challenge. We can simply refer to the syntax documentation and build our logic accordingly.

1. Process automation is becoming increasingly important in the modern workplace, making it a valuable skill to have. 2. In order to future-proof your process automation skills, learning programming languages is essential. 3. While there are many programming languages out there, some are more relevant and useful for process automation than others. 4. For beginners, Python is a great language to start with as it has a simple syntax and is widely used in process automation. 5. Java is another popular language for process automation, especially when working with large datasets and complex workflows.

It is better to secure the concepts of programming and structured languages...but to achieve implementation skills it is suggested to learn these skills using a language comfortable to master

1. Understand RPA Platforms: Familiarize yourself with popular RPA tools and their supported languages. 2. Stay Updated with Trends: Follow trends in automation, AI, and emerging technologies to understand which programming languages are gaining traction. 3. Problem-Solving Skills: Develop strong problem-solving skills alongside programming languages. 4. Specialize When Needed: Consider specializing in a particular programming language or tool if it aligns with your career goals or industry demands.

For a quick start, it's always beneficial to utilize high-quality video tutorials that provide a clear roadmap & structure, rather than succumbing to procrastination and failing to initiate the learning process. Once we've gained a theoretical understanding of the concepts, it's time to put our knowledge into practice. Begin with simple exercises & use cases, but don't limit yourself to these. Once we've gained a thorough understanding of the concepts through practical application, it's crucial to identify opportunities and scope within our current work environment to truly master the subject matter & witness the power of our learning in action. Proactive problem-solving in real time is the most effective way to solidify our understanding.

1. Identifying the most relevant programming languages for process automation (e.g. Python, Java, C++) 2. Utilizing online resources and courses to learn programming languages 3. Hands-on practice through coding challenges and projects 4. Importance of staying updated with new developments and updates in programming languages 5. Networking with other professionals in the field for learning and sharing knowledge 6. The role of mentorship and seeking guidance from experienced programmers 7. Creating a personalized learning plan based on career goals and industry demands

I have applied to adapt new, based on requirements specific. Having only a single approach in the solution is not good. Process optimization requires new learning. Understanding a platform that supports multiple programming languages, learn them. Start with C#, Python.

Future-proofing your automation skills with programming language requires a combination of continuous learning, adaptability, and strategic skill development. Stay abreast of emerging programming languages and trends. Specialize in a relevant automation niche. Never stop learning.

The truth is that, at the current rate of evolution of AI, future proofing is more a wish than a reality. The points stated above are all very valid, but we have seen this year how your work can become obsolete with any new release of the AI platforms it is based on. This is a good and a bad thing, as it means increased potential for additional benefits. But it also causes disruption on the solutions developed, with potential extra cost to constantly adapt.

As AI quickly develops, we are moving quickly from low-code to no-code. At the same time as learning low-code applications keep abreast of developments and remain adaptable. Change is the only constant.

Knowing any one language in depth is a strength. Foundation and concepts should be strong. With the AI disruption, it’s impossible to keep abreast of all the various advancements. At the same time, one should be able to use the new AI tools to work for us to create code/solutions in new languages using new tech stack. Since our foundation is strong, it requires minimal effort to understand and test the new outcomes.