Experfy Insights

Data analysts turn data into information. They play a vital role by making data actionable for decision makers.  Data analysts often take data provided by data engineers, analyze it, and make recommendations. They create visualizations to display their findings in dashboards and presentation.  Unlike data scientists, data analysts don’t usually create predictive models based on machine learning algorithms.If you want to become a data analyst or make yourself more marketable, this article suggests you learn the certain technologies, in order of priority.

Machine learning comes in many different flavors. In this post, we will explore supervised and unsupervised learning, the two main categories of machine learning algorithms. Each subset is composed of many different algorithms that are suitable for various tasks. One of the benefits of unsupervised learning is that it doesn’t require the laborious data labeling process that supervised learning must go through. However, the tradeoff is that evaluating the effectiveness of its performance is also very difficult.

Internet of Things doesn’t really have any meaning. What things? And what’s “Internet” about them? Fortunately, there’s a growing movement to change the definition of the tech term, to give it more meaning and set us up for the next decade of innovation.  The last decade was about connectivity, and we describe that dynamic with the Internet of Things, This decade is really about adding intelligence to different devices, services, etc. We’re confronted with a new IoT: The intelligence of things. Here’s what that shift will bring – and how everything’s going to change in the decade ahead.

ML models interpretability can be seen as the ability to explain or to present in understandable terms to a human. Regardless of the simple definition, technical challenges and the needs of different user communities have made interpretability a subjective and complicated subject. To make it more objective, a taxonomy was adopted that describes models in terms of their complexity, and categorizes interpretability techniques by the global or local scope of explanations they generate, the family of algorithms to which they can be applied, and their ability to promote trust and understanding.

Compared to the five JEPs in Java 13, the new version of Java 14 contains 16 major enhancements, also called JEPs (Java Enhancement Proposals).The updates touch various areas. Most likely, the most interesting updates for Java developers are going to be the new switch expressions and the enhanced NullPointerExceptions. Don’t forget to try out the new language preview features and provide your feedback to the JDK developers. Enjoy the new Java 14!

Culture and governance are key to drive change around cyber security behaviours, but too many awareness programmes focus simply on superficial technical gimmicks. Stay clear of empirical and ready-made solutions: Start with focus groups, questionnaires, and interviews and measure upfront levels of staff security maturity and engagement with corporate values. There are 3 clichés that have been dominating the security awareness arena for the past decade. And here are 5 key points to build a successful cyber security culture change programme.

The Internet of Things is a flourishing field for innovations and it’s primarily designed to make our life easier. Yet, there are spheres which are only discovering IoT benefits. The financial industry is one of them. You cannot expect finances and banking to stay the same as they have been. Our reliance on the bank as a building, the bank as a place, has become less important because now we can bank 24/7. We start rethinking the way financial services should work.

AI made us think again about the ethics and politics of computerized systems. While computerized systems have been around and influential in our lives for half a century at least, their increased use of our data and increased power to make decisions indeed justifies to think again about their ethics and politics. And, actually, it is the ingredients of “data” and “decision” in this newly reborn notion of “algorithm” that explains why there is reason for ethical concerns and political debate, and hence for the call for regulation.

Although a wide range of traditional optimization methods are available for inventory and price management applications, deep reinforcement learning has the potential to substantially improve the optimization capabilities for these and other types of enterprise operations due to impressive recent advances in the development of generic self-learning algorithms for optimal control. In this article, we explore how deep reinforcement learning methods can be applied in several basic supply chain and price management scenarios. This article is structured as a hands-on tutorial that describes how to develop, debug, and evaluate reinforcement learning optimizers using PyTorch and RLlib.

Cold chain logistics have additional challenges when compared to traditional logistics systems. IoT is already transforming traditional logistics and supply chain systems and can bring the same revolution for cold chain systems too. With cut-throat competition and obstacles in the logistics industry, businesses can’t ignore the benefits of IoT for cold chain logistics systems. While IoT devices do involve additional investment, the benefits and savings provided by them are huge in the long run.

The term Internet of Everything is a fairly new term, and there is a difference between the Internet of Everything (IoE) and the Internet of Things. The Internet of Everything (IoE) ” is bringing together people, process, data, and things to make networked connections more relevant and valuable than ever before-turning information into actions. The Internet of Things (#IoT) is the network of physical objects accessed through the Internet. 

Data quality is critical because data is used for decision making and powering AI models. Models and decisions are only as good as the data behind them, so any lack of confidence in the data means they are less useful in predicting and providing insights, slowing down, and undermining fast decision making. Trust in data is hard to get and easy to lose, so data quality must be maintained for models and dashboards to be useful at all times.

In earlier versions of SQL Server, storage of unstructured data posed many challenges in terms of maintaining consistency between structured and unstructured data, managing backup/restore procedure, performance issues, scalability, etc. This post explains to you how to use SQL Server FILESTREAM to store unstructured data. Here you can also read the positive and negative sides of FILESTREAM. Here are some other SQL queries to help you use it.

Take a look at the latest stats and facts that show how popular and commonly used Ruby on Rails is. Ruby on Rails is known for its multiple already inbuilt solutions that are really beneficial for rapid software development.  It is up to you how to get these gems in order to make your development faster, safer and easier. You can try to use these gems but at the same time, you are suggested to explore more and more new tools that are related to this field and are recommended to you. 

It’s easy to poke fun at others who pondered the automation “crisis” of his era. But as we experience a fresh wave of alarm over the rise of artificial intelligence and other new technologies, it’s important to ask why and how so many clear-eyed, serious people could have been so wrong, what we can learn from their mistakes—and how, in unexpected ways, they were at least partly right. To understand how tomorrow’s technology will change our lives, we need to look at what yesterday’s futurists got wrong—and right.

Data governance encompasses responsibilities and processes for the security and quality of data, which is used by an organization. Policies for data governance have to grow with upcoming technologies, business practices, and emerging laws. Today, companies have to think about how they are going to use data in terms of storage and processing.  The inclusion of AI can change things for the better. With automation, they can enhance the implementation of security and compliance in their data centers. 

Machine learning’s growing adoption in business across industries reflects how effective its algorithms, frameworks and techniques are at solving complex problems quickly. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. Learn from this article about the Key takeaways from the series of machine learning market forecasts and market estimates from the last year from different sources.

There are many types of SaaS business models. Some serve consumers directly while others use a channel partner to go to market. To achieve today’s most valued status (the platform unicorn), SaaS companies need to begin to think about their business not just as B2B but also B2C. And the restructuring of relationships is the single largest challenge of the change. It will require specialized sales and marketing skills that tune the company’s offerings to meet the needs of each member of the network simultaneously.

The python programming language has a large number of both built-in functions and libraries for data analysis. Combining some of these libraries can produce very powerful methods of summarising, describing and filtering large amounts of data. This article shares some tips on how to combine pandas, matplotlib  and some built-in python functionality to very quickly analyse a dataset. All the methods described can be extended to create much richer and more complex analyses.

Real-life Data Science never finds you working alone on a project and your workmates or clients usually won’t know much about the data you’ll be using. Being able to explain your thinking process is a key part of any data-related job. That’s why copying and pasting are not enough and charts personalization becomes key. This blog goes through 5 techniques to make better charts that are useful. Some of them are day-to-day tools, while others you’ll use them every now and then.

Data downtime refers to periods of time when your data is partial, erroneous, missing or otherwise inaccurate, and almost every data organization struggles with it. Data downtime refers to any time data when data teams find themselves answering “no” to common questions such as is the data in this report up-to-date, or is the data complete, and more. This blog post will cover an approach to managing data downtime that has been adopted by some of the best teams in the industry.

Data engineers play a vital role for organizations by creating and maintaining pipelines and databases for injesting, transforming, and storing data. They are responsible for storing and making data usable by others. Data engineers set up pipelines to injest streaming and batch data from many sources. Eventually the data finds its way into dashboards, reports, and machine learning models. Which tech skills are most in demand for data engineers? How do they compare to the most in demand tech skills for data scientists? Read on to find out!

AI needs to be applied such that it augments us, not compete with us, is long. Yet the supply of reports warning that AI threatens jobs doesn’t seem to have an end. On the other hand, a new report looking at a technology called Swarm AI may provide a much more benign fix. Swarm AI can take individual humans, whom one would like to think are more intelligent than shiners, and create something truly insightful.

As we kick of a new decade of software development and testing, and as digital continuous to challenge test automation engineers that are trying to fit the testing within shorter than ever cycles, here are the top recommended software testing books to consider – the order isn’t the priority, they are all awesome books and equally recommended, and that there are plenty of other great and practical books,