Do you want to become a data scientist? You’re a self-motivated person who is very passionate about data science and bringing values to companies by solving complex problems. Great. But you have ZERO experience in data science and have no clue how to get started in this field. That’s why this post is dedicated to you — enthusiastic and aspiring data scientists — to answer the most common questions and challenges faced by most people.
We keep hearing about new solutions for test automation and continuous testing. There are plethora of tools evolving these days aiming to solve test authoring, analysis, and maintenance problems. While these are all awesome initiatives that will position testing higher and smarter in the overall DevOps processes, this does not translate into the extinction of the tester. Each of the tools, as well as new tools that will rise are rising to help the existing testers to become more agile, smarter, and efficient.
For decades, AI scientists and researchers have been trying to recreate the logic and functionalities of the human brain. And for decades, they have dismayed themselves and the general public. Today, we’ve reached a point where artificial intelligence algorithms can solve very complicated problems, and in many cases with speed and accuracy that is far superior to those of humans. But whether contemporary AI works likes the human mind is up for debate.
Previous industrial revolutions have always been identified by some or other major event. With the advent of steam power, mechanisation made it possible for machines to take over a lot of the heavy lifting. The second industrial revolution saw electricity, petroleum, and steel creating the mass production of goods. The invention of the microprocessor kick-started the third industrial revolution. We suddenly found ourselves with wearables on our arms. And it's this convergence of technology and humans that's not only currently driving the fourth industrial revolution but quickly barrelling us into the fifth: the era of artificial intelligence (AI).
The value of AI is the promise of AI improving your everyday work life, and hopefully improving your everyday social life. AI walks the tight rope of enabling and taking away work-life balance. The current driving force for applying Artificial Intelligence solutions, i.e. current AI value propositions – tend to solely focus on the optimization of business functions. How do I either maximize revenue or reduce costs? Or manipulate some factor or indicator that influences either one of those things? The primary driving force is an optimization function that builds upon business objectives.
To secure the value that data can offer, you must manage it in a way that aligns and unifies your disparate sources. Enter master data, the foundation of any data-driven enterprise. It serves as the fuel that flows through the entire ecosystem of your business. It breaks down data silos and allows internal systems to work together. Every type of enterprise, legacy data migration program, and enterprise data management initiative includes five common requirements.
Changes in technology have affected nearly every aspect of our personal and professional life; however, the most prevalent changes are likely in communication. The way that we communicate is constantly evolving, and it is presently being pushed forward by technological advances. When researching the changes that technology has delivered to communication, the focus is often on personal experiences. This is certainly not the full picture. Technology has made significant, permanent changes to how businesses communicate, both internally and externally. The following are just a few of the ways that business communication has changed recently due to technology.
The introduction of RPA has been both a blessing and, in some instances, a potential curse to the workforce. A workforce that is both engaged and happy allows businesses to reap the benefits of the transformative potential of RPA. The study also revealed. On the plus side, RPA restructures and automates existing work, enabling employees to have more human interactions, and helps people focus on more meaningful, strategic tasks. However, badly managed RPA efforts add to existing fears in the workplace, stirring discontent and dissatisfaction that could negatively impact the bottom line.
If you’re doing all of the standard “I want to become a data scientist” things, then this means you shouldn’t expect to land your dream job. The market is currently full of junior talent, and as a result, the median aspiring data scientist is unlikely to get much traction. So if you want to avoid the median outcome, why do median things? The problem is, most people don’t think this way when they embark on their data science journeys.
Almost a year after the ominous GDPR regulation was passed in the EU, it’s time to look back and measure its true impact. The ambitious and incredibly strict regulation was meant to bolster privacy and security for European citizens. It also promised to deliver more control over personal data, allowing citizens to outright delete their information if desired. Did it do that? What has changed between its rollout in May 2018 and now?
DevOps and modern application development processes have made storage and data important to both developers and operations teams. Much of today’s application development happens within Linux containers. Those applications require persistent and native container storage to continue to function unabated after the container spins down. Simultaneously, developers’ storage needs require operations teams to act more quickly than before while still maintaining control over storage resources. At the end of the day, storage may not solve every single DevOps challenge. But it’s certainly a good place to start.
Through this post you will learn all about decision trees, non-linearity, overfitting and variance, and ensemble models like Random Forest. A decision tree is a super simple structure we use in our heads everyday. It’s just a representation of how we make decisions, like an if-this-then-that game. Data has linearity of some sort when data points can be separated into groups by using a line (or linear plane). Non-linearity is really just the opposite of this. You can think of non-linear data and functions in a few different ways.
One of the more exciting technologies to come out of the artificial intelligence (AI) space recently is Robotic Process Automation, or RPA. RPA enables IT groups to configure software “robots” to capture data and perform routine tasks or streamlining process flows and business operations. And while RPA presents a compelling opportunity for companies to speed operations and find new organizational efficiencies, many still struggle to justify the investment. Here are five steps to consider as you get started with your RPA business case.
AI in healthcare is an overhyped concept inappropriately attributed to programs that do not fit any reasonable definition of AI tools. It was described that many instances where operational clinical decision support tools touted as AI were, in reality, expert systems driven by algorithms built by human experts. Without transparency into the processes, organizations using these tools are unable to evaluate the quality and reliability of these “AI” systems. In addition, they cannot determine if they are based upon AI principles or more simplistic, static, rule-based algorithms.
Emerging and innovative technologies are constantly changing the world of business, and more often than not it’s for the better. While we regularly hear about how things like IoT and smart devices change industries such as manufacturing, logistics and even healthcare, there’s one area we don’t hear a lot about: non-profit. Believe it or not, the very same technologies are having an impact on the non-profit sector too. Platforms such as big data, the blockchain, artificial intelligence, and yes, even the Internet of Things all have a role to play in the future of the industry.
Convolutional neuronal networks are widely used in computer vision tasks. These networks are composed of an input layer, an output layer, and several hidden layers, some of which are convolutional, hence its name. In this post, we will present a specific case that we will follow step by step to understand the basic concepts of this type of networks. Specifically, together with the reader, we will program a convolutional neural network to solve the same MNIST digit recognition problem.
2018 is likely to be remembered as the year that artificial intelligence and machine learning finally took root in the DevOps consciousness. Organizations are still struggling to increase the degree of their test automation for desktop web apps, whether responsive or progressive, as well as native mobile apps. Mastering agile and DevOps processes and implementing stable continuous testing strategies are amongst the top challenges teams are facing. For 2019, we predict that there will be few significant advancements in the software industry that will enhance overall maturity in DevOps and Continuous Testing.
Industrial Internet of Things is the next industrial revolution, bringing dramatic changes and improvements to almost every sector. But to be sure it’s successful, there is one big question: how can organizations manage all the new Things that are part of their organizations’ landscapes? The industrial IoT is all about value creation: increased profitability, revenue, efficiency, and reliability. It starts with the target of safe, stable operations and meeting environmental regulations, translating to greater financial results and profitability. But there’s more to the IoT picture than that. Building the next generation of software for Things is a worthy goal.
There are huge social and financial benefits that businesses and economies can realize if they can successfully leverage Open Data. Despite this, there are still some hurdles for data professionals to leap. A great way to start is to consider whether your data meets the criteria for what’s known as the FAIR principles. While the FAIR principles are not equal to the idea of Open Data, they are a great start for ensuring that data sets are available en-masse to the data professionals who need them.
Big data Is just one tool for managing risk. As we mentioned above, there are three pillars to effective risk management in modern times: oversight for regulatory compliance, a strong company culture focused on making the right hires and training on the right principles, and the wise application of useful technologies. One of the most important advantages of bringing data analytics into the mix versus relying on the other two pillars alone is that the company's analytics platform gets smarter with each new data point it receives.
A Dockerfile instruction is a capitalized word at the start of a line followed by its arguments. Each line in a Dockerfile can contain an instruction. Instructions are processed from top to bottom when an image is built. In this article, I’m assuming you are using a Unix-based Docker image. You can also use Windows-based images, but that’s a slower, less-pleasant, less-common process. So use Unix if you can. Let’s do a quick once-over of the dozen Dockerfile instructions we’ll explore.
To reduce risk, IT teams need to minimize the threats they're exposed to, the vulnerabilities that exist in their environments, or a combination of both. From the business side, management may also decide to evaluate the business impact of each data asset and take measures to reduce it. The central risk team must assign risk values of high, medium, or low for the potential loss of each valuable data asset. Using this process, a company can determine which data asset risks need to be prioritized.
There is no single technology that will redefine IT operations or business strategies. Instead, technologies must be stacked together to produce new and innovative solutions. Emerging technologies hold a great deal of promise, but only if the right skills are in place to leverage them. Building skills from the ground up will help IT pros understand new trends and incorporate those trends into existing architecture so that the business can grow. Three different trends show how emerging technologies will show up in technology solutions.
Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. In this post, discover the different data types that are used throughout statistics. Learn the difference between discrete & continuous data and learn what nominal, ordinal interval and ratio measurement scales are. Know what statistical measurements you can use at which datatype and which are the right visualization methods. This enables you to create a big part of an exploratory analysis on a given dataset