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Tag: developers

Open Source Is Finally Coming to Financial Services

Open source will catalyze the financial services industry’s biggest evolution to date. This evolution will shift the power in this $25 trillion industry from business executives to developers, not just in fintech companies, but in centuries-old incumbents, as well.   
Until very recently, financial services were notoriously hard and expensive to build. Incumbents and startups alike grappled with extensive regulation, inflexible core systems, complex payment architectures, compliance…… Read more...

Low code/no code increases efficiency in retail and beyond

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

Low- and no-code platforms allow developers and non-developers alike to create software through visual dashboards instead of traditional programming. Adoption is on the rise, with a recent OutSystems report showing that 41% of organizations were using a low- or no-code tool in 2019/2020, up from 34% in 2018/2019.
If the current trend holds, the market for low- and…… Read more...

What Skills to Look for When Hiring a Python Developer

User experience is the core of IT. No matter if you are building software, a website, or an app, its UX heavily impacts the user experience and thus customer retention. Offering a marvelous user experience is not an easy job. You need to choose the right language and hire the best developers.
Python is one of the most demanding languages of 2021. Hence, if you are planning to develop or upgrade an app or website, you need to hire the best Python app developer for that.
As the demand for……

Move Ideas Faster: Incorporating AIOps into your Tech Stack

In this special guest feature, Matt Chotin, Vice President of Product Management at CloudBees, discusses how the visibility AIOps provides combined with automation capabilities allows businesses to be more proactive in changes they make to be more efficient. Matt is responsible for the company’s software as a service product strategy and execution globally. Prior to CloudBees, Matt served as senior director of developer initiatives at AppDynamics. Prior to AppDynamics, Matt held product leadership roles at Chegg and Adobe and co-founded his own startup.

AIOps is increasingly popular as organizations look to become more resilient and agile. In fact, Gartner predicts that AIOps service usage will rise from 5% in 2018 to 30% in 2023, highlighting a sharp rise in the implementation of the technology over the next few years.… Read more...

Awesome Python Video Tutorials Keep You Motivated

Programming languages are one of those topics that we geeks have some very strong and often rather polarised opinions about. As new concepts in computing are dreamt up, older languages may grow new features, if viable, or get left behind when new upstarts come along and shake things up a bit. This scribe can remember his early days programming embedded systems, and the arguments that ensued when someone came along with a project that required embedded C++ or worse, Java, when we were mostly diehard C programmers. Fast forward a decade or two, and things are way more complicated. So much choice, so much opinion.… Read more...

Reasons to choose flutter for app development

Flutter is a cross-platform technology developed by Google, that allows creating application programs for mobile, desktop, and web use. It uses the Dart programming language, which is based on the notorious Java. If you know Java/C#, assume you can program with Dart. The technology keeps up with the latest developments and is used by developers around the world. Most importantly, it is open-source and completely free.

Reasons to choose Flutter

Low-cost app development

An ideal cross-platform should meet two requirements: provide a high-quality user experience (smooth animations, native UI elements without slowing down), and be cost-friendly from the development perspective.

The advantage of Flutter is not only in making one application instead of two.… Read more...

5 Spectacular Features From Julia I Wish Were In Python

One thing that has to be loved about Julia by all programmers is its robust type system. For my personal preference when it comes to the strength of typing, Julia fits the bill quite well. That being said, Python sits around the same area in terms of the strength of the types, although perhaps a little more implicit with type changing. That being said, I think that Julia’s type-hierarchies and base data-types are far superior to that of Python’s.

This is not to say that Python’s type system is not robust, but there are certainly improvements that could be made. This is especially the case when it comes to inheritance of numerical types and iterables.


Weird Python Stuff You Might Not Have Seen Before

If you are a C programmer, it is likely that you are familiar with return types. For all of the non-C programmers out there (or other languages with this feature, Java, C++, etc.) the return type is simply the type of the data that is going to be…

Continue reading: https://towardsdatascience.com/weird-python-stuff-you-might-not-have-seen-before-950a965235fd?source=rss—-7f60cf5620c9—4

Source: towardsdatascience.com

AI Architects: The New Programmers

On the nature of the novel paradigm of programming

Artifical Intelligence success stories have become so ubiquitous that it seems that every piece of software will soon incorporate AI in one way or another. But what does an AI program do? AI programs can learn and adapt with experience. The difference in building AI programs from building non-AI ones is bringing about a paradigm shift in our understanding of what programming is. In this article, I explore the essentials of the new paradigm and possible future routes it can take.

Karpathy, in his article “Software 2.0“, describes an AI-triggered paradigm shift.


Destroying Every Programming Concept You Know With Julia

Before the release of the Lisp programming language, the existence of programming paradigms was really not as defined as it was prior. Prior to this, there were some languages which we would now say are imperative programming languages, but languages and their respective type systems were much like they are today — jumbled.

The Lisp programming language introduced the functional programming paradigm, which was followed later by another well-known and modern programming paradigm, object-oriented programming. Many programmers might be familiar with these two topics, but unsure as to exactly what they mean.

A programming paradigm is the way that different defined data interacts with operations in a programming language.


GitHub Copilot and the Rise of AI Language Models in Programming Automation

Should I Use Github Copilot?

If you are a software engineer, or count any of them among your circle of acquaintances, then you’re probably already aware at some level of Copilot. Copilot is GitHub’s new deep learning code completion tool.

Autocomplete tools for programmers are nothing new, and Copilot is not even the first to make use of deep learning nor even the first to use a GPT transformer. After all, TabNine sprung out of a summer project by OpenAI alum Jacob Jackson and makes use of the GPT-2 general purpose transformer.

Microsoft (which owns GitHub) has packaged their own IntelliSense code completion tool with programming products since at least 1996, and autocomplete and text correction has been an active area of research since the 1950s.


Why Do You Need Data Matching ?

Enterprises need data for making informed decisions, interacting with customers and vendors, and analyze results. Trusted data helps overcome fraud challenges and enables organizations to comply with regulations. High-quality data about key business entities provides the growth funnel for a successful enterprise.

Clean and duplicate free customer records enable efficient sales and marketing and help the organization to grow. Imagine reaching out to the same customer multiple times only because of multiple entries in the system. This is expensive and time consuming for the sales and support staff, troublesome for the data analyst, cumbersome for the BI developer and frustrating for the customer.


10 Essential Tips to Avoid Programming Burnout

Although some of this advice might be simple, I still think it is vital to consider next time you go to sit down for an extra long programming session. The first thing I want to advise is that the actual work you are doing is something you love. This is even the case if the work is not programming; make sure that you pursue your goals. You only have one life.

If you are like me and you love computers, and science, then Data Science is a no-brainer. That being said, Data Science is certainly not for everyone. Furthermore, Data Science is not for every single programmer on Earth.


Publisher of Popular Electronics To Speak at COSM 2021

John Schroeter has many accomplishments as a futurist but also as an author, publisher, and developer:

John Schroeter

➤ He is Executive Director at Abundant World Institute, a think tank for leading technologists, futurists and entrepreneurs seeking to create more abundance in the world: Their foundational book, Moonshots—Creating a World of Abundance, won the 2019 Gold Medal by Axiom Business Book Awards, and was recognized by Kirkus Reviews as a “Best Book of 2018.” After Shock (2020) marks the 50-year anniversary of Alvin Toffler’s Future Shock.

➤ He is also the publisher, at TechnicaCuriosa, of iconic mags such as Popular Electronics and Popular Astronomy.


Power BI and Synapse, Part 1: The Art of the (Im)possible

In the 2nd part of the series, learn what Synapse brings to a table for Power BI developers, and how you can leverage both tools to get an indefinite number of use-cases!

By introducing Azure Synapse Analytics in late 2019, a whole new perspective was created when it comes to data treatment. Some core concepts, such as traditional data warehousing, came under more scrutiny, while various fresh approaches started to pop up after data nerds became aware of the new capabilities that Synapse brought to the table.

Not that Synapse made a strong impact on data ingestion, transformation, and storage options only — it also offered a whole new set of possibilities for data serving and visualization!


The Labor of Love That Is Recovering Lost Software

At the beginning of the home computer revolution, the humble compact cassette was far and away the most popular choice for microcomputer data storage, especially on the European continent. As a volunteer at the Museum of Computing, [Keith] was instrumental in recovering and archiving the early works of Roger Dymond, a pioneering developer of early computer software in the United Kingdom.

In his video, [Keith] goes to great lengths detailing the impact that Roger Dymond had on the early home computing scene. After being let go from his council apprenticeship, Roger turned his attention to developing games for the ZX81, and later the ZX Spectrum.


The Two Most Common Problems with Outsourced Code

I have had many opportunities to work with developers outside the United States in a variety of capacities. To begin with, let me assure readers that there are great developers all over the world. The sun never sets on the current team I work with. The great thing about software development is that you can find great talent wherever the Internet is.

There are great individual developers in every country, but I have found that, in many countries, the culture of software development has not evolved to where it is in America. When hiring individual developers, this rarely matters. The proper developers tend to gravitate to whatever level you are hiring at (or, alternatively, you can have a headhunter screen applicants to make sure only the most qualified apply).


Create a Web App in Under Thirty Minutes with Streamlit, MongoDB and Heroku

An aspiring Full Stack Developer’s guide to quickly developing and deploying scalable web applications

There used to be a time not so long ago when creating web applications was the work of child prodigies the likes of Mark Zuckerberg and Elon Musk. Or alternatively, you could enroll in a fancy college, spend the best four years of your life (and your parent’s retirement savings) learning to program and then end up making subpar 90’s style web apps. Well, we’ve come a long way since then. With the inundation of open source tools and cloud infrastructure, developing and deploying phenomenal applications has been largely democratized.


Why you should consider adding requirements-core.txt to your next Python project


A simple concept with a number of tangible benefits

Courtesy of imgflip: https://imgflip.com/i/5mkvxf

Just about any Python developer from beginner to expert has heard about the requirements.txt file. It documents our package dependencies for Python, and it can be version controlled along with our code.

An example requirements.txt file might look something like this:

[ . . . ]

However, I’d like to introduce a simple but effective concept for long-term maintenance of Python repositories — the requirements-core.txt file.

Most Python developers only intend to use a handful of packages in their project, and the rest of the packages listed in requirements.txt


Fixed-point DSP for Data Scientists

Learn how to create a DSP pipeline in Python and convert it run on an Arm Cortex-M based MCU using C/C++ and Arm’s CMSIS-DSP library.


Artificial Intelligence and the Golden Rule

How does the Golden Rule apply to developers of artificial intelligence (AI)?

To simplify the application let’s assume there are only two people involved. One runs a small trucking company but also knows how to develop sophisticated AI. This business owner develops an AI enabled system capable of driving his truck. The other person is the truck driver, whom the owner no longer needs. If the owner believed in following the Golden Rule, how should he treat his driver?

Let’s assume the driver has worked for the company for forty years but is not yet financially ready to retire. A number of answers are possible.


Image Distribution

As your team invests significant time and resources developing models, it is imperative that processes are put into place to protect and maximize the return on that investment. To that end, in this installment of the ModelOps Blog Series we’ll discuss leveraging functionality provided by continuous integration/continuous deployment (CI/CD) frameworks such as Jenkins, CircleCI, and GitHub Actions to automate the push of model container images to production container registries. As your team develops and containerizes models, it’s important that they don’t just live on your R&D servers or model developers’ laptops where events like hardware failures or accidental reformats could wipe away capabilities in the blink of an eye.


Know When to Hold ’em, Know When to Fold ’em

Computer programmers are a pretty predictable bunch. Every time they approach legacy code, the gut reaction is “let’s rewrite this from scratch.” The reaction is understandable for many reasons.

First of all, code written by someone else (or even yourself a long time ago) is hard to understand. Even good documentation can’t cover every detail you need to know, and there is nothing that helps you understand the problem better than writing the code yourself.

Second, as time goes on, and you think about a problem, you always come up with better (or at least different) approaches. You might realize that some aspect of your code could be factored out.


How to Create Stunning Web Apps for your Data Science Projects

By Murallie Thuwarakesh, Data Scientist at Stax, Inc.

Photo by Meagan Carsience on Unsplash

Web development isn’t a data scientist’s core competency. Most data scientists don’t bother to learn different technologies to do it. It’s just not their cup of coffee.

Yet, most data science projects also have a software development component. Developers sometimes have a different understanding of the problem, and they use discrete technologies. It often causes problems and drains the precious time of both teams unproductively.

Also, visualization tools such as Tableau and Power BI focus more on data exploration. Yet, it’s only part of a complete data science project.