Big Data, Cloud & DevOps

It seems strange then that the United Nations has coined digitization as an instrumental component for achieving sustainable development goals. After all, the concept does have some requirements for moving away from digital mediums as a means to lower dependency. The reality is, many digital tools exist for improving sustainability levels, particularly when it comes to tracking resource consumption. Certain IoT devices, for example, can monitor and report water and energy usage. The incoming information can then improve processes or operations, cutting down on total consumption.

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.

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.

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.

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.

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.