SQL is a critical skill. What is SQL anyway? SQL is a query language for talking to structured databases. Pretty much all databases that have tables and rows will accept SQL based queries. SQL has many flavors but the fundamentals stay the same. Professionals and amateurs alike use SQL to find, create, update and delete information from their sources of record. Understanding SQL queries can be fundamental to your work. Professionals and amateurs alike use SQL to find, create, update and delete information from their sources of record.
The most common questions asked is how do I become a data scientist? It is a fair question for those that are deciding to pivot that direction because they want to eliminate the learning waste that traditional educations are full of. The number one reason I think you will never be a data scientist is your lack of passion. Honestly, passion fixes everything. The biggest flaw in most people attempting to break into data science is their lack of breadth. People who are passionate have great breadth.
Here we focus on blockchain, Identity Management, and Corporate Social Responsibility, a timeless subject that has taken on added meaning. Identity management technology exists that can obviate the need for passwords or storing personal information in servers all over the place. There has also been a movement to try and give individuals a single, universal digital identity. With regulations on the wane (for now) as an inhibitor of bad behavior, it is heartening to see business leaders stepping up to the plate to try and create cultures and actions of corporate responsibility.
The field service component, distribution, channel, aftermarket service/repair, and integrator are the glue to the entire IoT value chain. The opportunity service organizations have to leverage IoT to transform their business is incredible. It is a tool that will allow field workers to spot issues continuously as an expanded “remote monitoring” opportunity, maybe as a SaaS model. This could be a new revenue stream, but really a chance to see more issues with continual data streaming in instead of quarterly route-based maintenance. The value lies in domain specific expertise and vertical specialization, but delivery is the key.
It is necessary to differentiate between indirect attacks and direct attacks on IoT devices. In indirect attacks, the goal of compromising IoT devices is to use them to conduct cyberattacks against other external targets. In direct attacks, the goal is to conduct some sort of ‘local malfeasance’ right there at the device itself. The IoT security asserts that manufacturers and deployers of IoT devices and systems, especially potential targets for direct attacks, have a moral obligation to vigorously and comprehensively address security. The following seven principles can serve as guideposts to enable stronger IoT security.
For an IIoT project to be successful, you need to have customer-centric IIoT approach. Start by gaining internal agreement on your target customer such as what business outcomes do they expect from your IoT-enabled products and services. Then the targeted segmentation will deliver more engaging customer experiences, higher customer lifetime value, and more valued customer outcomes. As you bring your products to market, learn from your wins and losses. Continuously flesh out user issues, new product features, and integration requirements. So put the odds of IIoT success back into your favor. Take a customer-centric, integrated team (IT) approach.
In the fast-changing world, it has become difficult to catch up with all the new concepts and technologies. It is even more complicated to distinguish which of them are really useful and which ones is just hype. In the field of data analytics, it was big data that started this era of doubt. Now, when this concept is clearer, a new wave is coming: Big data for IoT. Despite all the hype around IoT, it is just one of multiple big data sources. But how exactly IoT is connected to big data?
With Big Data increasing and the time-consuming task of data preparation, the industry is looking for better ways to improve efficiency and speed up the change of data into meaningful information. With that said, when analyzing the data journey and the constituents data serves, metadata is the common recurring theme that enables an organization that replaces “data wrangling” with data discovery. Metadata management must be a core competency, a place of innovation and of strategic importance. Data regulations and sensitive data will need to be managed, and it all starts at a metadata level in order leverage, yet protect those it intends to serve.
IoT can positively change the way we do business and the way we live our lives. IoT is a new revolution in our society, or it is just one more step in the technological evolution of the digital revolution. Today, the debate continues but whether evolution or revolution, The Internet of Things is here to stay. IoT is often presented as a revolution that is changing the face of society or the industry in a profound manner. It is an evolution that has its origins in technologies and functionalities developed by visionary automation suppliers.
IoT security breaches are expected to reach an all-time high. It’s important to differentiate between indirect attacks, using IoT devices to conduct cyberattacks against another target, and direct attacks, where the end goal is to compromise and access the IoT device itself. With direct attacks, the goal is access to the IoT device – and by extension the sensors, machines, and environment that the device is connected to. As such, this type has the potential to be even more disruptive and destructive. Criminals, terrorists, and malicious foreign governments may use connected devices to cause havoc or harm. Seven principles can serve as guideposts to enable stronger IoT security.
Everybody loves DevOps. That’s because DevOps promises to satisfy the deepest longings of digital business—including fast execution on innovative ideas, competitively differentiated customer experiences, and significantly improved operational efficiencies. But who does DevOps love? It’s a fundamental challenge for anyone leading a DevOps initiative. What passions and motivations are driving your DevOps teams? How do you know? And if those motivations aren’t the right ones, how do you re-direct them? Metrics, it turns out, may hold the answers.
As companies embark on their journey toward the 4th Industrial Revolution, the ones that are willing to bring in new leadership and make the organizational enhancements to power new digital customer relationships will be the ones that rise above the fray. Digital transformation is both a technology and a management challenge. Focusing on one without the other is not a recipe for success. Instead, companies should adopt a more holistic approach – one that starts with the target customer and ends with organizational alignment based on your targeted outcomes. Remember, start your organizational changes slowly.
Hard work has always been an important competency for aspiring students to become data scientists. Despite having studied there was still a noticeable gap between what they had studied and what industry wanted. You can be a great data scientist, but you can't if you stay in a silo. So going to Meet-ups, reading Kaggle forums, reading recommended data science books, following technical thought leaders, can help ensure you are at least heading in the right direction. Finding an industry mentor can also be very helpful. Lastly, fall in love with it. Passion for the topic and intrinsic motivation will help you stand out from the school of fish in the market.
Metrics are important for any manager seeking to continuously improve critical work processes and the resulting work-product. That’s why DevOps leaders need DevOps metrics. With the right ones, those leaders can guide their organization’s adoption of DevOps best practices—progressively optimizing staff productivity, business agility and customer experience. But, what are the right metrics for DevOps success? And, what are the wrong ones? Useful metrics must enable DevOps leaders to make better decisions about workflows, incentives, policies, training, tools or some other “lever” of transformation.
We hear a lot about the Internet of Things, but what is the Internet of Data? When people talk about the “Internet of Data”, what they are referring to is the collection of data from edge devices and performing deep analysis of that data to gain insights. To truly enable the “Internet of Data”, machine learning and AI processing need to be moved directly to the edge. In order to do this companies need to look for solutions that can handle the entire data value chain directly on the edge and do not create throughput bottlenecks, but use a more democratized architecture.
While the progress towards smarter building infrastructure is impressive, it is important to remember that it is not without risk. Unfortunately, the diverse range of IoT systems within smart buildings are still running old, unpatched software and frequently communicate using nonstandard protocols. This makes malicious activity and potential security threats much harder to detect. Moving forward, it is imperative that the building industry and developers strictly deploy smart systems that have security built in from the start. When it comes to connectivity, the implementation of VPNs is critical for protecting smart buildings and ensuring device data is kept private and secure.
There are three foundational elements which seem to be consistently ignored in from IIoT projects themselves. One of these three foundational elements is more engaging customer experiences, higher customer lifetime value and more valued customer outcomes. The second one is to establish a target organizational end-state and a roadmap. Start with a minimum viable organization (MVO) to get started and make course corrections as needed. The third one is to ensure that valuable and measurable outcomes are not only delivered to your customers, but also to your employees, your company, and your partners.
A data scientist skills framework should take the big, messy data-scientist-by-data-scientist’s-skills matrix and try to reduce it to a few informative dimensions that minimally overlap. A skills framework establishes common ground for conversations, even when those conversations are among people of wildly diverging perspectives. A good framework doesn’t guarantee that a conversation will be productive, but a bad framework comes pretty darn close to guaranteeing that it won’t be. If we can be more clear and precise about what a data scientist needs to be able to do, we can make both groups happier than they are now.
To capitalize on emerging blockchain opportunities, many IT leaders are looking to jump-start some development pilots. But if you start squeezing out blockchain code without first achieving DevOps mastery, you’ll wind up with a very counter-productive digital value bottleneck. DevOps maturity is a prerequisite for reliable integration of your new blockchain apps and these core systems. To quickly, easily, and iteratively test integration of new blockchain code, your developers and QA staff need a DevOps toolchain that provides them with convenient access to some sort of application modeling – such as application “stubs,” virtual instances, or API sandboxing.
Organizations around the world are investing enormous amounts of resources in pushing computing to edge devices. There are use cases across most industries like self driving cars, smart grids, healthcare, and many more. These solutions are beginning to take on similar architectural patterns as they evolve from concept to reality. The data value chain is going to move directly to the edge over the course of the next few years as more and more organizations see the need for real-time analytics directly on an intelligent edge.
Good design achieves firmness, usefulness, and delight. It’s relatively easy to translate the first two of those ideas to workflow automation design: whatever we build, it should consistently do the job we want it to do at the scale, speed, and quality that we need. The first step of a good design is to decide what we want to accomplish. Encoding workflow saves time and effort over the long-run, but it also requires being explicit about what workflow is in the first place.
In this turbulent legal environment, corporate governance and ethical standards are critically important for care provider organizations. Staff members must have a clear understanding of proper ethical and moral behavior and the innate, or learned, fortitude to make the right decisions. This kind of decisiveness may involve overlooking opportunities for short-term profit, avoiding programmed work behaviors and making choices that require more effort among available alternatives. This kind of ethical decision-making improves the standing that a facility has in the community and makes a health care institution appear more appealing to potential employees and patrons.
What should every aspiring data scientist do to find a job? Start a blog, and write about data science. One of the great thrills of a data science blog is that, unlike a course, competition, or job, you can analyze any dataset you like! Whatever amuses or interests you, you can find relevant data and write some posts about it. And the purpose of blogging isn’t only to advertise yourself to employers. You also get to build a network of colleagues and fellow data scientists, which helps both in finding a job and in your future career.
When working as a data scientist, nobody tells us what’s the ML/DS problem that we need to solve or the prediction that we need to make, we need to understand the business process first and identify the problem and qualify the problem suitable for a ML/DS solution. Then we need to collect underlying data being used by the business and assess whether it’s enough & useful to convert this business problem to ML/DS problem. This article covers these aspects to give you a holistic view of Data Science Framework built on CRISP/DM methodology.