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Securing applications is not the easiest thing to do. An application has many components: server-side logic, client-side logic, data storage, data transportation, API, and more. With all these components to secure, building a secure application can seem really daunting.
Thankfully, most real-life vulnerabilities share the same root causes. And by studying these common vulnerability types, why they happen, and how to spot them, you can learn to prevent them and secure your application.
Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.
While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more.
The canonical, “Python is a great first language”, elicited, “Python is a great last language!” — Noah Spurrier
A rising programming language in the IT scenario, one of the most popular amongst the developer community — Python needs no introduction. Python programming has been in demand and so has been the demand for skilled Python developers. Simply learning Python may not suffice for a developer, understanding Python in detail, learning about the roles and responsibilities, skills, and salaries are important too.
The global FinTech market was valued at $127.66 billion and is expected to reach $309.98 billion at a CAGR of 24.8%, a report by PRNewsWire suggests. The staggering growth is the result of digital payments and transactions. From investors and traders to personal loan customers, everyone is hooked to FinTech.
If you are building FinTech products to cater to this multiplying need, you are on the right track.
Like many software developers, I have learned my share of software languages over the years. Two years ago, I wrote a post on LinkedIn where I identified 29 software languages I had learned in the 37 years prior; everything from Basic to Ruby to JavaScript and obscure languages like APL and Clipper. As all developers know, in the software engineering field it’s important to keep up with emerging programming languages, so even to this day, I’m learning new ones. Similarly, like many other developers, in my eagerness to quickly learn a new language, I start with similar and familiar code syntax based on programming languages I already know. Using familiar syntax from programming languages you’ve already worked with is initially easier and can jump-start your use of a new language, but there’s a price to pay for that. When doing so, you often avoid learning some of the subtleties and specific features unique to the new language you’re learning, and you may miss some of the differences between the new one and the current languages you know.
At Solution Street, we ask candidates interviewing for developer positions to write some code during the interview. Sometimes we will describe a candidate’s code after an interview as, “He writes Ruby like a Java developer.” This is just another way of saying that, if the candidate was writing in Ruby, he didn’t really make use of some of Ruby’s advantages. I am no different and I often will lazily rely on familiar syntax and control structures when I use a newly-learned language.