Big Data, Cloud & DevOps

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. 

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.

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.

How to reduce the gap between having the courage to use data for clinical purposes and actually using data? There are some restraint and slowness in using data and advanced analytics to reach better results in the health care sector for the benefit of patients. The time to build up analytical structures has come, and therefore the health care sector should aim at becoming an analytical organisation. This article is an introduction to how health care data can be collected and used in the health care sector to ensure improvements in the patient safety and quality area.

While skill gaps widened in a variety of domains—marketing, sales, business development, accounting and finance, etc.—perhaps none of these deficits is as troubling as the one in the IT realm. Workers with a technologist’s mindset, which optimally blends hard technical skills and relationship acumen (often called “soft skills”), are well-suited for today’s fast-paced, continuously evolving digital business environment. However, there are issues at work that confound and complicate the task of raising the next generation of technologists. Seven myths about technology careers discourage potential technologists and their parents. 

While DevOps is a well known and popular term, DataOps is now emerging as a practice that is of equal importance.   DataOps is a blend of data science, DevOps, business intelligence, and data engineering.  The goal is to produce agile, actionable, repeatable practices within big data to allow companies to see true value from big data.   As data grows exponentially year over year the infrastructure and skills sets to manage that data are becoming more complex.  By building competency in DataOps, companies can have groups that can work alongside their existing teams augmenting their existing capabilities pre-emptively.   

Relocation can offer a wide variety of benefits, but before you make that move you need to ensure that your decision isn’t just grass-is-greener syndrome, and you’re doing it for the right reasons! Data Science is a relatively young industry. The attraction of moving to a new country is more appealing to a younger workforce than a ‘veteran’. No matter where you have your sights set on, what should you consider before taking the plunge and moving for work? Find out what is on offer in terms of relocation benefits and package. Have you relocated for work? How was your experience?

The biotech field has been held back by the technological limitations, but ML and AI programs have programs have broken through the barriers into new possibilities impacting recent biotech and healthcare developments. Security and healthcare trends suggest future generations will utilize biotech on a daily basis, either to thwart identity theft or to cure cancer. We may still ask the big questions, but AI programs are finding the solutions. Public opinion is mixed about biotech. The manipulation of organic materials at the microscopic level has already produced new drugs and treatments while ethics committees discern what is acceptable.

Despite the mounting demand for Data Science professionals, it’s still an extremely difficult career path to break into. The most common complaints from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role requirements. There are also challenges for people with heaps of experience getting rejected due to lack of applicable experience to the role they’re applying for. Make sure your skills, experience, and projects tell the hiring manager that you have the tools necessary to make an impact on their business and how when applying these techniques in the past, you’ve had x y z results. 

Computer Vision is one of the hottest research fields within Deep Learning at the moment. As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery. Why study Computer Vision? The most obvious answer is that there’s a fast-growing collection of useful applications derived from this field of study. Here are the 5 major computer vision techniques as well as major deep learning models and applications using each of them. They can help a computer extract, analyze, and understand useful information from a single or a sequence of images.