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API Code coding in the future programming languages Tech Tech Trends

Is Low-Code the Future of Development?

low code development

Low-code development is increasingly being used in the marketing of a wide range of software products. The term refers to the use of a graphical user interface to build something that a developer would usually have to custom code.

“Low-code development� is somewhat deceiving. One might think it is going to solve all our development problems but in reality, each low code platform has a very specific set of capabilities.

These low-code sites are domain-specific and target areas like web or mobile applications, BPM or CRM, and give us large pieces of predefined functionality to build with. This makes us more efficient at delivering functionality as long as we stay within the platform domain.

To put it into context, let’s have a look at how code evolved. Low-code is after all just code with an adjective indicating we will somehow have less of it. Maybe the past can give us a glimpse of the future.

Machine code

A long long time ago we had to think in machine code, 0s and 1s, and toggle switches or feed punch cards into room-sized computers.

“Hello world� on a punch card:
punch card

Obviously that’s not ideal. Imagine having to find a bug amongst 1000s of those.

So — the assembly languages are born. A thin abstraction on top of machine code where every line represents an instruction to the computer. Now we can write code in something that is slightly easier to understand.

“Hello world� in assembly:

hello world in assembly

That’s better. At least something we can read it — Sort of.

Assembly languages are the most granular way of giving a computer instructions. The assembly languages are specific to a particular computer architecture and obviously not very human friendly.

What this means is that developing your new creation in assembly and then porting it every time Intel/Apple/AMD brings out a new chipset is not going to be very pleasant.

Language

Then comes the third-generation general-purpose programming languages (GPL). Languages like C, C++ and Java. With more human-like syntax and a compiler to translate it to machine code, they express computing concepts in a human-friendly way.

“Hello world� in C:

hello world in C

That’s more like it. In later languages like Python that five-line code is reduced to a single line:

Fantastic, now we’re down from 13 lines of gibberish to 1 line of English.

But that doesn’t mean we have more time for coffee and croissants, instead, we use the efficiency gains to just produce more complex systems.

Soon we find that languages that express computing concepts do not necessarily translate well to other domains. Drawing a user interface pixel by pixel or adding data to disk bit by bit soon becomes unfunny.

What is born next is domain-specific programming languages (DSL). Languages like HTML and SQL are created to solve problems in a specific domain. They can’t do everything a GPL can do but they are easier to understand and work within their domain.

“Hello world� in HTML:
hello world in html

The domain-specific programming languages look more verbose but now it’s not just about the language but also about the domain.

HTML, and its friends CSS and Javascript, tells browsers what to render. It takes a modern web browser more than 20m lines of code to render what HTML, CSS and Javascript describes.

A slightly contrived “Hello world� in SQL:

hello world in SQL

Nice. Reads like English. Mostly does exactly what it says. But you need a database server to make it work and a very small one like SQLite has 139,000 lines of code. Once again the domain language is just the tip of the iceberg.

Up to this point we’ve evolved from Assemblers to GPLs by giving computing concepts a human language at the cost of losing a tiny bit of granularity. Still a huge net productivity win.

We’ve simplified programming for specific domains by adding DSLs that work with pre-built infrastructure. A big productivity win in those domains.Something that we haven’t changed is the medium of communication. Lots and lots of text in lots and lots of files.

Visualization

Low code development platforms take the evolution forward by adding a visual way of representing computing and/or domain concepts. They come with the underlying infrastructure to support their visual language and remove any friction between the building and the running of the final application.

We can now create a mobile application by dragging and dropping some controls, filling in properties, and then publish it with a couple of clicks.

 

“Hello world� in Microsoft Power Apps:

hello world in power apps

No low-level developer tools, SDKs or infrastructure concerns. There are limitations to what our application can do, but for the domain, it covers it seems pretty nifty.

Another example is in the world of APIs. We can now create a REST endpoint by filling in properties, implement it by dragging and dropping some components, and then publish it to a server with a couple of clicks.

“Hello world� REST endpoint in Linx:

hello world in Linx

No syntax to remember, build steps to run or servers to set up. We’re never going to develop Photoshop with Linx but it makes building an API easy.

The future or just a fad?

Is this the future or just a fad? Well — maybe a bit of both.

If we think of low-code development platforms as visual DSLs then there might be a future where standards emerge, and a handful of big players capture each domain.

There might even be a long tail of products catering to/for niche domains. The value proposition of low-code productivity gains combined with bundled infrastructure is certainly compelling.

However, if we think of them as replacing programming or solving all our development problems then we will be disappointed.

The more programmable the low-code platform the more complex it has to be, and the more our low code developer will have to know about the underlying concepts that are being abstracted away. The developer is still coding, just with bigger pieces.

History shows that we’ve made impressive productivity gains by making it easier to code.

Hopefully, some of these low-code development platforms will emerge with the right recipe to give us another boost.

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Code coding Learn Software The Right Stack

8 Mistakes Keeping You from Landing Your Dream Coding Job

coding

Software development has been the most in-demand job in the country for three years running — yet many coders struggle to break into the industry. Here are eight mistakes that are keeping you from landing your dream coding job.

Landing Your Dream Coding Job

I hear the same complaint come up again and again.

“I’ve applied to hundreds of positions, and I haven’t gotten one interview!�

“Do people even hire software developers anymore? They only want web developers.�

“I’m a great coder, but no one will hire me, because I don’t have experience.�

Does any of this sound like you? If so, there’s a good chance one of these eight mistakes is keeping you from landing your dream coding job.

#1: You Learned the Wrong Stack

A top mistake coders make is learning the wrong coding language stack.

By far, the most useful, most popular, and most employable stack is the .NET stack, which includes several languages (C#, HTML, CSS, JavaScript, and SQL), a design pattern (MVC), and a program (Visual Studio).

The .NET stack allows you to design web applications, which is the most in-demand position in America, and it’s the number one choice for almost every business with a web development shop.

With its popularity and practical applications, .NET is the first stack you should learn if you want a career in this industry.

#2: You Didn’t Learn Practical Coding Skills in School

A lack of practical skills is a huge problem preventing many coders from being hired.

Employers want coders who can solve real business problems. Unfortunately, many universities focus on theories, not practical skills.

Many universities fall into the trap of teaching a little bit of everything. There’s a class on C#, one on HTML, one on databases, and so on. All these classes are interesting and informative, but the skills are never brought together and connected to full-stack coding projects.

If you didn’t learn practical coding skills in school, you need to develop them on your own.

#3: You Have a Weak (or No) Portfolio

Especially if you’re a new coder, without experience, having a weak portfolio or no portfolio at all is a major mistake.

Without a portfolio, you have no proof that you’re as good at coding as you claim to be, which immediately puts you at a disadvantage. With a portfolio, you can show employers just what you’re capable of.

For your portfolio to be effective, it must include projects that demonstrate skills a business would need. Useless, silly programs can be fun to code, but for your portfolio, focus on projects that show off useful, real-world skills.

#4: You Struggle in Interviews

Many skilled coders struggle to get hired simply because they’re not good at interviewing.

One of the problems is that interviewers often play a “code trivia� game, quizzing you on obscure code lingo and knowledge. If you suffer a brain blank and flub even one question, you can be taken out of the running.

Instead of playing this game, take control of the interview by shifting the focus to your portfolio.

Talking about your portfolio demonstrates you have the necessary coding skills and knowledge, making the “code trivia� questions less important.

#5: You Don’t Have a Recruiter

Not having a recruiter is a mistake that puts you at a significant disadvantage.

In most cases, by the time you apply for a position online, a recruiter has already submitted ten qualified candidates. This is part of the reason you could apply to hundreds, even thousands of positions and never receive a reply back—it’s because a recruiter already filled the position!

The simple truth is, if you want a job, you need a recruiter. Without one, you may never even get in the door for an interview. That’s just how it works.

#6: You’re Too Picky About Your First Job

Many coders’ careers are derailed because they’re too picky about their first job. For instance, many coders don’t want to work in web development, but this is the most in-demand position.

If you turn down your first job offer, there’s no guarantee you’ll get another one, and your coding skills can quickly grow rusty and out-of-date, making you unhirable.

To get your dream job, you first need to build experience. So when you receive your first job offer, take it. You may need to start in web development, but once you gain some experience, you can get a different job.

#7: You Need to Work on Communication

Poor communication can hold you back from your dream career.

To improve your communication, engage in conversations with a desire to be helpful. When your boss needs to know the timeline of your project, give her your best estimate. When asked about your code, explain how it works and how you arrived at your decisions.

Also, try to make your work seem less mysterious to those without a technology background. Avoid technical terms and acronyms, and compare your work to something others find more relatable.

When you can code and communicate well, you become a rock star coder.

#8: You Need to Specialize

To get your first job, you just need to know the most popular coding languages, but if you don’t eventually specialize, you’ll never advance to your dream job.

Web development jobs—the most common coding jobs—most often focus on the .NET stack. However, apps, games, AI, and phone operating systems usually work with different, more specialized languages. If you want to compete for these high-demand jobs, you need to learn the relevant coding languages.

Look up job postings for your dream job to identify the languages you need to learn, and then start studying in your free time.

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Big Data Analytics Code data analysis Data and Security ReadWrite

6 Skills You Need to Become a Data Analyst

become data analyst

Data analyst is the process in which the judgment, refining, changing, and modeling data takes place with the aim of making a better conclusion. Data analysis takes part in an important role, it helps in generating scientific conclusions and also increases the regulations in an organization. Here are six skills you need to become a data analyst.

Data-Driven Business Strategies

Today, with the deepening of the concept of data operation, more and more companies are aware of the value of data-driven business strategies and emphasize the participation of all employees in data operations. Developing the ability to analyze data is also a future trend.

A data analyst is someone who accumulates data, processes, and performs analysis of data. He or she can interpret numbers and data into simple English in order to help organizations and companies understand how to make better business decisions.

At its core, data analysis means taking a business question or need and turning it into a data question. Then, you’ll need to transform and analyze data to extract an answer to that question.

Companies of today realize how data-driven strategy is important to them and they are in need of talented individuals who can provide insights into a constant stream of collected information.

Types of Data Analysts

  • Finance
  • Data assurance
  • Business Intelligence
  • Marketing
  • Sales

The types of data analyst listed above are some of the most challenging fields which require a data analyst in their management.

Here are 6 skills you need to become a data analyst.

1. MATHEMATICAL SKILLS

Mathematical skills are important for making a profession in data analysis, as you have to play with numbers and figures all through the day. Necessary mathematics skills include:

*One should be good at calculations

*Measuring and analyzing data

*Having the ability to organize information

*One should know how to schedule and budget

Mathematical skills help in having better problem-solving skills — and also helps in financing our individual businesses.

2. ANALYTICAL SKILLS

As it is called, It is clear that analytical skills are the huge significance of data analysis. These skills include assembling, sorting, and analyzing all kinds of raw data in particular. It also helps you to view provocation or situation from a different angle. Hence, If you want to be a professional data analyst then you need to widen your analytical skills and thinking. Analytical skills help in viewing a challenge or situation from different perspectives. It is one of the most important and efficient parts of the skill you need to become a data analyst.

3. TECHNICAL SKILLS

Good computer and technical skills are very important. The main knowledge of statistics is quite helpful as well as producing languages like python or Mat Lab, and analytical languages like R and SAS are very favorable to know as a data analyst.

The more language you know, the faster and better for you to get your dream job. Examples of technical skills you need to know are:

  • Programming languages.
  • Common operating systems.
  • Software proficiency.
  • Technical writing.

4. DETAILS SKILLS

Data analyst is all about details, It helps a data analyst to be able to find and see any basic unseen errors and links, that is especially important at the point of solving problems and making decisions.

5. BUSINESS SKILLS

Aside from the technical skills, there are some business skills that one should acquire to function as a Data analyst. Some of which are:

Communication Skills:

As a data analyst, you are expected to work with different sets of people in your team which makes your communication a really important part of your job. Working well together as a team for the benefit of your organization is also one of your main responsibilities and skill.

You should be able to communicate effectively with the teammates to prepare, present, and explain data. Communication is key in collaborating with your colleagues. For example, in a kickoff meeting with business stakeholders, careful listening skills are needed to understand the analyses they require.

Similarly, during your project, you may need to be able to explain a complex topic to non-technical teammates.

Time Management and Organizational Skills:

As it is expected of you to work with different people in your team. You should be able to manage your time including your responsibilities, as well as meet your deadlines.

Decision-making and Problem-solving:

These skills are the bottom line of data analysis. The main job of a data analyst is to give the right information for decision-making and problem-solving process. Which is why it occurs to be a perfect skill required to be a data analyst.

You might need to research a quirk of some software or coding language that you’re using. Your company might have resource constraints that force you to be innovative in how you approach a problem.

The data you’re using might be incomplete. Or you might need to perform some “good enoughâ€� analysis to meet a looming deadline.

6. DOMAIN KNOWLEDGE

Domain knowledge is understanding things that are specific to the particular industry and company that you work for. For example, if you’re working for a company with an online store, you might need to understand the nuances of e-commerce. In contrast, if you’re analyzing data about mechanical systems, you might need to understand those systems and how they work.

Domain knowledge changes from industry to industry, so you may find yourself needing to research and learn quickly. No matter where you work if you don’t understand what you’re analyzing it’s going to be difficult to do it effectively, making domain knowledge a key data analyst skill.

Remember that Domain knowledge is certainly something that you can learn on the job, but if you know a specific industry or area you’d like to work in, building as much understanding as you can upfront will make you a more attractive job applicant and a more effective employee once you do get the job.

Once you have the skills mentioned above — you can use them to attract attention to yourself so as to get your dream job. These are the criteria you need for the data analyst skills checklist.

Academic Background

A bachelor’s degree is often necessary, not all the time though. To work as a data analyst you need to acquire a degree in any of these Science courses.

  • Economics
  • Business information systems
  • Mathematics
  • Statistics
  • Computer science

As much as the above degrees are important for your success — what matters the most are the skills that you possess. Right now during the COVID-19 problems may be the time for you to upgrade yourself and your skills.

Conclusion

Data analysis is speedily growing field and skilled data analysts are huge in demand throughout the country, in every industry. This means that you would find many opportunities only if you are exceptional and show your excellent data analytic skills.

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