As the adoption of IoT began to ramp up, more things became apparent — particularly regarding the benefits it can offer the average operation. By utilizing the data collected and transmitted from connected devices, companies have seen unprecedented efficiency and cost improvements, but also many boons to oversight. These changes are largely positive, and they’re reported across all industries, from manufacturing to modern health care. However, all this change means the entire business world is in a constant state of flux, and it will stay that way, at least for the foreseeable future.
You have trouble finding a data science job, but don’t understand why? For every person who has a question, and asks it, there are ten people who have the same question but don’t ask it. If you’re one of those ten, then this post is for you. Hopefully, you’ll find it helpful.
Software development is one of those fields which is changing at an extremely fast pace. Every year brings some brand new surprises for software developers. 2018 was the year of complete acceleration for the software development companies worldwide. From blockchain to Artificial intelligence, software technologies have remained a hot topic throughout the year.There are many software development trends in 2019 that need your attention. We are discussing some of the most popular ones here
The tech industry is booming in America and with unemployment the lowest it’s been in 50 years, companies are clamoring to scoop up well-qualified talent for non-executive tech roles. It probably doesn't come as a surprise to you that tech is in demand. However, if the technology industry has taught us anything, it's that modern-day career development doesn't adhere to the rules of generations past. People change jobs more frequently and for different reasons than they once did. So how are today's top companies finding and acquiring tech talent? Here are seven trends we've noticed for 2019.
What makes structure and flow of data science projects unique for a startup? Both managers and the different teams in a startup might find the differences between a data science project and a software development one unintuitive and confusing. If not stated and accounted for explicitly, these fundamental differences might cause misunderstanding and clashes between the data scientist and her peers. The aim of this post, then, is to present the characteristic project flow. Hopefully, this can help both data scientists and the people working with them to structure data science projects in a way that reflects their uniqueness.
Knowing exactly where your company needs to tighten things up can be difficult. CEOs and executive teams spend large sums on consultants and evaluative tools to understand what exactly keeps their business from growing. To help businesses take the first step in this process, the International Organization for Standardization offers a set of standards you can build your company around and a certification process to ensure you're adhering to the policies and procedures outlined.
AIOps is an emerging technology that combines the usage of artificial intelligence with operations to help solve critical issues that can bring your business to its knees. AIOps solutions help organizations cut through the alerting noise and gain critical control by proactively managing the health and performance of their enterprise IT services. By applying data science and computational techniques, AIOps tools can accurately predict a range of incidents across infrastructure, common IT management tools, and enterprise processes - and resolve them autonomously.
In order to evolve certain levels of business process automation, the software robot, or bot, was formed. This was a simplistic technology aimed at delivering automation. However, over the next 10 years, this resulted in companies having all these automated processes that weren't intelligent and that couldn't evolve themselves. The next evolution required to make BPA work better was intelligent bots, which is where we are today, with robotic process automation (RPA).
Creating an AI Project always involves answering the same questions: What is the value you’re adding? What data do you need? Who are the customers? What costs and revenue are expected? This post is part of an ongoing series aiming to educate Data Scientists in the area of customer-centric thinking and business acumen. We’re encouraging Data Scientists to get rid of the “Let’s implement this paper and see what happens”-attitude towards a “What value can we generate”-attitude. We’re entering the decade of AI implementation and need champions to productionalize Machine Learning models.
Blockchain salaries have risen to be among the highest in the tech industry. The demand for blockchain talent is still strong. Despite the bear market and recent industry layoffs, the number of blockchain job postings have been on the rise, and searches for roles involving Bitcoin, Ethereum, blockchain and cryptocurrency have increased. Startups are offering top compensation packages, in particular for blockchain developers, as they compete for talent in an industry where supply is limited. The demand for blockchain talent has also grown as established companies.
The scope and capabilities of augmented reality has grown with leaps and bounds. In fact, every industry sector that is betting with digital transformation has augmented reality in their arsenal of digital tools. Augmented reality would blur the lines between physical and digital worlds. Once AR reaches maturity, users may not be able to differentiate between real and the virtual world. We would live in a seamless world where digital information blends naturally into the physical world thus creating an augmented world. Those days of augmented future is not far from now.
Gradient descent is by far the most popular optimization strategy, used in machine learning and deep learning at the moment. It is used while training your model, can be combined with every algorithm and is easy to understand and implement. Therefore, everyone who works with Machine Learning should understand it’s concept. After reading this posts you will understand how Gradient Descent works, what types of it are used today and what are their advantages and tradeoffs.
I have loved being a data scientist. The job is challenging. The job market is great. I can't imagine any other job that would provide more career fulfilment for me now. This is my ideal career. Across the way, I see people that I care about trying to break into this field. Wondering what to study, what skills they need, feeling clueless about how to get that first real data science job in this market. This article is for them.
A report analysis that caught my eye was a comparison of Data Scientist salaries on the intersection of experience and education, with experience levels of 1-3 years, 4-8 years, and 9+ years, and educational attainment of Masters vs PhD. Not surprisingly, there’s an over 25% difference in median salary by category of advancing experience, along with a consistent differential between Masters and PhD of roughly 10%. Makes sense to me: obviously, experience is critical and PhD’s may well bring more of the Science/Research Methodology component of DS to the table from the get-go than do most Masters-trained practitioners.
Behind every technological innovation that hits the market, there is a team of highly skilled professionals working jointly to make the innovation a success. And it is obvious that when technologies come in, employees are expected to update their knowledge and skills to be able to leverage the technology. From bridging the talent gap to incubating the right culture to modifying the infrastructure, every small thing should be taken into account when companies plan to harness the power of any new technology.
Knowledge isn’t power. Almost everything we know is either currently on the internet, or will be soon. If we reach a stage when every person and machine has access to the same information, what will set you apart from the pack? Your power is through connection. One way you’ll stand out is by cultivating an ability to communicate knowledge in a more compelling way than other people or machines can do it. We yearn for human connection, yet few people develop their skills in this area.
What is the purpose of your resume? In my opinion, the main purpose of your resume is to land you the first interview. Have you ever applied to many tech jobs that suit your experience and skill but never heard back? Does it feel like you are sending your resume to a black hole? Then this is something you absolutely must read. After you know what the tech recruiters are looking for you can tailor your resume that lands you an interview.
Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do. Until recently, computer vision only worked in limited capacity. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labeling objects.
How supercomputing could contribute to improving Machine Learning methods?The tasks of training Deep Learning networks requires a large amount of computation and, often, they also need the same type of matrix operations as the numerical calculation intensive applications, which makes them similar to traditional supercomputing applications. Deep Learning applications work very well in computer systems that use accelerators such as GPU or field-programmable gate arrays (FPGA), which have been used in the Supercomputing field for more than a decade within the walls of the supercomputing research centers.
By unifying analytics, building forecasts and accelerating analytic processes, simulation helps companies build a holistic picture of their business to optimize strategy and maximize revenue. Here are the four types of information that companies need to fuel simulation forecasting and monetize their data investments. Once a company identifies sources for these four types of data, it’s time to find an effective way to monetize it. With simulation forecasting, reliable answers are accessible – and you may need less data than you think to get meaningful, trustworthy insight.
Many organizations have grown comfortable with their business intelligence solution, and find it difficult to justify the need for advanced analytics. The advantages of advanced analytics are numerous and those advantages are based on the ability to further improve the business, increase user adoption and therefore user empowerment and accountability and, best of all, improve the bottom line and the accuracy of predictions and forecasts that will dictate the success of the business in the future.
Sometimes an interview goes wrong, and it’s not always the fault of the candidate. This person may be considered the creative/visual type, with strong expertise in building solid UI elements that can be used in web applications, but then gets asked in the interview how to solve a complex algorithm around binary trees or big O on a whiteboard. But most of the time a job doesn’t require such complexity on a daily basis. In all seriousness, having a poor interviewing process reflects very badly on the company.
We all know a friend who works in tech. And they are usually doing quite well for themselves, probably started coding since the beginning of time and often seem to be spoilt with career choices. Or are they? Here are some common myths about careers in tech and how they hold up against data. The data used here comes from a developer survey by StackOverflow, a website that a of developers frequent regularly. It’s a pretty comprehensive survey, with more than a hundred thousand responses from all over the world. Let’s get started!