Why exactly are more and more businesses embracing RPA technology and how can they ensure a successful transformation? What has quickly become clear is that RPA has the power to modernise how businesses operate. Deploying a virtual workforce can enable organisations to drive a whole host of workforce advancements, with robots taking over many of the more mundane, rules-based processes. For example, RPA robots can complete tasks such as processing transactions or filling out forms faster, meaning employees will no longer have to make repetitive, transactional decisions.
For programmers, this is a good time to think about new skills you want to learn or interesting projects you want to take part in. Below we present some of the major programming trends to prepare for to help you stay at the top of your game in 2019. The top three programming trends to watch in 2019 are the rise of Python, TypeScript, and Go. All three are great choices if you’re looking for a new language to learn.
Because of the technology, quality standards have improved considerably across the board. Engineering processes have become much more efficient and reliable, operations are better controlled, and the related system architecture is much easier to maintain and manage. While QM technology may be tried and true, various applications are still considered relatively young. These trends are on the bleeding edge of the field, offering their own sets of advantages. Provided the technology and necessary infrastructure is already in place, here are the top quality management technology trends you should be keeping an eye on.
Linear regression is one of the most popular and best understood algorithms in the machine learning landscape. Since regression tasks belong to the most common machine learning problems in supervised learning, every Machine Learning Engineer should have a thorough understanding of how it works. This blog post covers how the linear regression algorithm works, where it is used, how you can evaluate its performance and which tools & techniques should be used along with it.
Advanced analytics is the logical tool to help a business optimize its investments and achieve its goals. But, when an organization is ready to consider the implementation of an Advanced Analytics solution, it is difficult to know what it needs to ensure that it can satisfy current and future requirements and ensure user adoption. If a business wants to assure that it has full coverage for its Advanced Analytics needs and can leverage all the benefits of advanced analytics, it should consider a solution with the necessary capabilities.
Decision tree is a type of supervised learning algorithm that can be used in both regression and classification problems. It works for both categorical and continuous input and output variables. The major advantage of using decision trees is that they are intuitively very easy to explain. They closely mirror human decision-making compared to other regression and classification approaches. They can be displayed graphically, and they can easily handle qualitative predictors without the need to create dummy variables.
Thanks to emerging laws, there seem to be more ways than ever to run afoul of compliance and reporting requirements. Compliance represents a number of different types of challenges. Some of these, like social and political pressures, are the inevitable consequence of globalization. Others, like technological and regulatory pressures, require that companies balance self-interest and convenience with the needs of the environment and human institutions. Here's a look at how technology helps to address each of these in turn.
Data breaches are causing companies and organizations everywhere to re-examine the things they procure, the services they use, the individuals they hire, and the people and firms with whom they partner and do business. Across the board, organizations have sunk money into staff, strategies, and equipment to comply with new, tighter customer privacy rules and sidestep major fines and other penalties. Privacy laws and regulations worldwide are evolving and expanding. And the ones that took early action to address security concerns are seeing positive results from their investments.
Everyone wants to be innovative. Innovation refers to the successful conversion of concepts and knowledge into new products, services, or processes that delivers new value to society or the marketplace. Most of the project management professionals have no idea how to systematically foster innovation or make it an integral part of the DNA of their projects. As a project manager, you have to actively solicit ideas that add value throughout the project lifecycle in order to ensure that the desired innovative result is achieved. Let’s start with the fundamentals.
Cybersecurity has developed a high profile in many organisations over the past few years. But, who wants to be a Chief Information Security Officer these days? And at which stage in your career should you consider the move? What balance of managerial and technical experience do you need to have? And where do you go from there? Those would be valid questions for many executive positions but when it comes to the role of the CISO, they seem to acquire a different meaning.
Despite a demand for faster release, cycles of weekly rather than monthly, development teams can find it difficult to integrate the necessary set of tools into their pipeline to make updates ahead of a deadline. How do we give teams the capabilities to keep up with this industry wide trend of accelerated release cycles? Successful DevOps involves a tuneup of Agile processes. A thorough spring cleaning can help teams get there. Here is how.
There are many ethical controversies surrounding artificial intelligence algorithms in the past few years. In tandem with advances in artificial intelligence, there is growing interest in establishing criteria and standards to weigh the robustness and trustworthiness of the AI algorithms that are helping or replacing humans in making important and critical decisions. With the field being nascent, there’s little consensus over the definition of ethical and trustworthy AI, and the topic has become the focus of many organizations, tech companies and government institutions.
Each day brings a new set of challenges for early stage ventures. Decisions impact where you spend money, who you hire, and where to focus. The start-up roller coaster makes maintaining a disciplined decision-making process seem impossible. Everything looks important. But they are not. And everything is not urgent. Making good business decisions starts with having a clear understanding of who you are and what you want to be. Start with a target end state and then build a mission and vision statement. Here are 4 simple steps to help make fast, high quality decisions.
You want to learn data science on your own. Then, set explicit learning goals. Aim for short-term goals, which can be scoped out more realistically. Commit to your goals publicly. If you can, reach out to an industry professional, and ask them if you can keep them in the loop on your latest work with a weekly newsletter. This is an easy way to give yourself some extra motivation to make sure you have something to show for each week’s work, and doubles as a great way to grow your professional network.
Cloud computing, containerization, and container orchestration are the most important trends in DevOps. Whether you’re a data scientist, software developer, or product manager, it’s good to know Docker and Kubernetes basics. Both technologies help you collaborate with others, deploy your projects, and increase your value to employers. In this article, we’ll cover essential Kubernetes concepts. T Kubernetes is an open-source platform for managing containerized apps in production. Kubernetes is referred to as K8s for short. here are a lot of Kubernetes terms.
Whenever we talk about data science, chances are we’ll first think of those fancy stuff like AI, deep learning, machine learning, etc. But nobody talks about documentation. Documentation is often not one of the most interesting things for data scientists. But still, it’s importance is no less than other data science workflow, especially in terms of data science project management. In fact, documentation is no longer just the task done by programmers or developers. It’s something that we as a data scientist should know and be able to perform this task only a regular basis.
Finding a tax preparer in the US is not at all a cake walk. And you all share your personal information with the tax preparer including your income, marriage, kids and even your social security number. Are you feeling anxious now? In this article, I’ll be sharing 7 effective tips that will help you find the right tax preparer for your business and personal purpose as well. These tips can greatly help you to pick the right tax preparer, alerting you about common frauds.
The technology could have devastating economic consequences, or it could create the happiest workforce in history. The technology could destroy jobs, but it could put an end to tedium in the workplace. Part of the problem with studies proclaiming job losses is that they tend to focus on tasks and not the overall activities a worker might carry out. So sure, some tasks might become the preserve of automation, but that does not mean the jobs will.
What would happen if the apps that help run our lives and keep our businesses on track suddenly stopped working? You wouldn’t have easy access to things that affect your daily life, like your checking account or health-monitoring systems. From a business perspective, processes would delay, and tasks would take longer to complete. That’s why it is more important now than ever before for developers to assure continuous quality throughout the entire software development life cycle (SDLC). And it all starts with testing.
As a leader, today, both Technical Intelligence and Emotional Intelligence, often referred to as Emotional Intelligence Quotient or EQ are essential skills for long-term career success. The importance of EQ for the modern day leader cannot be underestimated. It is one of the fundamental pillars upon which your leadership is built on. If you recognize that a higher EQ would be beneficial for you, your team, and your organization, then today is the day to take those critical first steps in improving it. Enjoy the journey. The destination is far less exciting.
In the current business paradigm, replicated since by a number of online platforms, individuals willingly provide their personal information in exchange for a service. Personal data is subsequently repackaged and sold to advertisers and marketers. The unavoidable rise of the Internet of Things will only make the issue more complex, as increasingly more intrusive and personal data will start to be collected about each of us. This poses new challenges around the issue of consent and privacy:
The Chimp Paradox uses a simple analogy to explain functional brain types, dubbing one the ‘Human’ and the other ‘Chimp’. We all have them both, the Human side is logical, calm and emotionally assured brain, whereas the Chimp brain can be fiery, spontaneous and alert to help you avoid trouble. Same is the case with data chimp in Energy Management. In a vast and growing array of data feeds from energy meters, data can be hugely distracting and create emotional unease should it highlight unexpected results.
Transfer learning is all the rage in the machine learning community these days. It serves as the basis for many of the managed AutoML services and now figures prominently in the latest NLP research. We’re also starting to see examples of neural networks that can handle multiple tasks using transfer learning across domains. The main question at hand is: could transfer learning have applications within reinforcement learning? Compared to other machine learning methods, deep reinforcement learning has a reputation for being data hungry, subject to instability in its learning process.
Machine learning/Deep Learning/AI are fancy number crunchers and they can have some amazing results given good data, however, the first step is to properly understand your data so you can make informed decisions about what algorithms and data cleaning methods to use. One of the first things in understanding your data is to know what kind of data you have! Here are the 4 most common types of data that you will come across.