The paradigm has completely shifted for programming languages in the last 4-5 years. Not long ago, programmers would stick their hands out and showcase skills in an industry recognized coding and scripting skills. But now, the whole dynamics have shifted toward Cloud computing and open source development. A majority of modern day programmers prefer to stick to open source opportunities such as those offered by Python data science courses. This helps retain career growth without actually sacrificing due to the need to upskill every 3-4 years around the industry trends. Python, in itself, has been the driving force for Big Data and Automation growth in the recent years.
In this article, I have tried to evaluate the various avenues within programming language and how mastering Python for Data Science could prove to be a game changer in your career roadmap.
Let’s rock.
Why Learn Programming?
There are two ways to answer this question.
Programming is both a professional avenue for coders and programmers, as well as a hobby for testers. 90% of the programmers take up coding as a hard skill to grow their career. The remaining 10% are more likely to do testing and experiments and find new ways and applications for the programming language. In fact, hobby programmers account for almost all the open source development patches and new libraries. The reason, they don’t have to abide by corporate laws and ethical issues of double programming jobs. Plus, it pays well to be a hobby programmer.
You can start hobby programming in less than 3 months with a basic course in Python for data science and Big Data management.
I have been following the Python data science foundations from close quarters for over a decade now. I understand that in recent times, there has been a rampant rise of a third party coder group, called Bug Bounty hunters. These bug bounty hunters help companies identify and mitigate critical issues in their programming channels, ensuring these are not left exposed as vulnerable risks to external threat agents.
How well is the programming skill represented in the industry?
Most prospective programmers begin their journey in Python coding with lots of If and Else situations. In the game of comparison with C, Java, and R, Python tends to come out as a winner for all.
Python data science is a powerful ecosystem and almost all top tier organizations have dedicated Centers of Excellence, also referred to as CoE, to develop Python centric applications. Not only do these programming opportunities simplify an organization’s journey into digital transformation with high-ranking coding tools and platforms but also allow multiple partners and customer groups to understand the programming ecosystem. This superlative availability of free flowing information in programming languages now allows customers to become partners, and then strategic decision making groups within the organization. From simplifying workflows and application development to helping identify the risks and issues associated with programming foundations, coding teams sit for hours with analysts and software developers to address specific applications of their programming tasks.
This is the standard SOP for DevOps and product innovation teams before going ahead with new platforms. I can count everyone in the industry who is doing this in their DevOps applications. Salesforce, Facebook, Netflix, Twitter, ZOHO CRM, and almost every other Fintech application is heavily dependent on the way testers and analysts pursue Python programming tasks to explore alternative means to replace obsolete programming standards that fail to live up to new benchmarks in security assessment, identity management, AIOps and much more.
Intelligent Coders: Smarter Capabilities
Python has steadily grown to enter the list of Top 3 most preferred programming languages for big data and analytics. Java is displaced for sure but remains relevant to mobile app developers and gamers. C retains its number 1 position. Python, however, is growing so fast in India, UK, the USA, and Australia that it will most likely become the Number one programming language for Cloud computing, NLP, and conversational AI development projects by 2025. Maybe even sooner, if we don’t overcome the COVID-19 situation, which for best known reasons is working in favor of massive traction among Python coders.
A good Python programmer is a multi-faceted personality capable of working on highly complex projects on strict timelines. The time size could range between 3 and 50, depending on the size of the company and budget. An effective and diligent Python software program developer will make it extremely easy for companies to grow.
