Big Data, Cloud & DevOps

Data Scientists must communicate effectively, and need to use Ethos, Logos, and Pathos. Ethos establishes the credibility of the speaker, and Logos appeals to the reasoning used. Yet, both are useless without Pathos, the way to the heart of the audience. The Data Scientists need data-driven reasoning when presenting their work. This is generally the right way to argue. But to evoke actions, Data Scientists need to tell stories.

When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? Applied data science is a team sport that’s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience. If you’re keen to make your data useful with a decision intelligence engineering approach, here’s my take on the order in which to grow your team.

Companies are sitting on some of the world’s largest reservoirs of valuable data, but many of them aren’t doing anything with it. It’s pretty hard to “do anything with it” if the data storage method doesn’t allow one dataset to talk to another. Or if tools like Hadoop and Spark process large amounts of data in a distributed fashion using commodity hardware”—can’t readily access said data to work their magic on it. Legacy institutions suffer from infrastructures predating the so-called information age. Yet these siloed institutions are the ones with the most to gain from the efficiencies and insights that data can bring.

Look at the fundamental building blocks for a flexible presentation of data. The real power of this concept lies in uncaging your data from the confines of monolithic charts and setting them free, to tell their own expressive story. Though many visualization tools today don’t adopt a grammar of graphics approach in its entirety, that seems to be the way forward. Meanwhile, there are opportunities for people to start putting this to practice. This is so important that it must be made mandatory education for anyone working with data, whether it is analysts, designers, data scientists or journalists.

Real world data science model is at the heart of our hedging business risk and increasing customer satisfaction. The ecommerce space is going through a major transformation like the Fintech industry, where one-to-one marketing and personalization are driving innovations through technologies like machine learning. Companies are now able to leverage this fascinating convergence of technology and consumer demand. Attaining personalized engagement with your consumer has to be on top of the list for every marketer in today’s crowded marketplace.

What exactly is data science? Data is to Data Science, as Elements are for Chemistry. The most basic thing you can have in Data Science is a data point. From data, data scientists can build a model to explain what is going on in the scenario we’re facing, validate it, and test it. But to do all this, we need a little bit of Computer Science, Math and Statistics, and Domains / Business Expertise.

International dropshipping is a very promising business. Dropshipping entrepreneurs will find that there are lots of other untapped markets in other regions too. However, developing a profitable international dropshipping business is going to be much more complex than one that solely serves domestic customers. You will want to take advantage of predictive analytics to develop a lucrative funnel for your international customers. Here are some ways that predictive analytics can be invaluable.

Big data may be a good tool in helping increase employees productivity, but it’s the managers that wield those tools who have the biggest impact. It takes a gifted leader to truly listen and appreciate people. Balancing the needs of the business with the needs of the individual is always challenging. Data can be used to tear people down who aren’t performing, or it can be used to empower and improve performance. Management needs to take care of employees and create a positive environment by using data for good.

Those who control the data control the future not just of humanity, but the future of life itself because today, data is the most important asset in the world. The combination of data and computation creates a power that surpasses that of the most powerful spy agencies of past centuries. This is changing thanks to the rise of machine learning and deep learning, smart artificial intelligence software that can mine huge sets of data and find meaningful patterns that would go unnoticed to the biologically limited minds of human beings.

The divide between consumer health and fitness wearables, and medical-grade devices is slowly being bridged as technology evolves to offer advanced sensors and form factors that combine the best of two worlds. The result is vast amounts of higher quality data to feed the complex algorithms which not only deliver the personalized results the growing market of medical-grade devices, designed for consumer/patient access without the need of a prescription, is enabling a new category of remote patient monitoring. 

Many enterprises that scrambled to put a minimally GDPR-compliant set of privacy policies in place are now lulling themselves into complacency. A closer look at the steps taken by many of these companies reveals a GDPR strategy that it is only skin deep and fails to identify, monitor or delete all of the Personally Identifiable Information (PII) data they have stored. To address these risks, companies need a holistic strategy to manage their data—one that automates the process of profiling, indexing, discovering, monitoring, moving and deleting all of their data as necessary.