I finished my open source project rewriting R (core nmath) in Javascript (typescript) language,...front end devs and nodejs devs can now use all functions from nmath.
As we move into 2018, Analytical Data Infrastructure (ADI) is becoming a significant topic in business intelligence and analytics. Where Big Data was once an over-hyped, catch-all term, in the coming year we will see organisations move to a place where business-oriented ‘data strategies’ are the major focus. With that shift comes the need for sophisticated, yet easy to use, data science approaches that deliver results back to the business.
It’s not our fault though – as human beings we are hard-wired from birth to look for patterns and explain why they happen. This problem doesn’t go away when we grow up though, it becomes worse the more intelligent we think we are. We convince ourselves that now we are older, wiser, smarter, that our conclusions are closer to the mark than when we were younger (the faster the wind blows the faster the windmill blades…
Working in Data Science recruitment, we’re no strangers to the mountains you have to climb and pitfalls faced when getting into a Data Science career. Despite the mounting demand for Data Science professionals, it’s still an extremely difficult career path to break into. The most common complaints we see from candidates who have faced rejection are lack of experience, education level requirements, lack of opportunities for Freshers, overly demanding and confusing job role…
The years of hard work and gaining experience in your field has finally paid off. You’re a Data Scientist! You’re a part of an innovative, forward-thinking team working on exciting, world-changing projects. You’ve got a great salary and tasty benefits package, flexible working and not an uncomfortable suit and tie in sight. Time to put down the books and enjoy the ride? Absolutely not! Quite the opposite in fact, and here is…
The level in this article is for college students familiar with calculus, This material will be also of interest to college professors looking for new material to teach, or for original exam questions, as well as for business data scientists with some spare time, interested in refreshing their math skills. The problems cover real analysis, mathematical algorithms and numerical precision, correct visualizations, as well as geometry. The third problem is the most interesting one in my opinion,…
The world is adapting and shaping around IoT technologies. In combination with 5G network connectivity, companies are preparing themselves for this constant shift to the progression of these technologies.
Network operators, telcos, and IoT companies are coming together to promote an ecosystem where customers can have great…
If you read my blog then you'll probably realize there are a few data related topics that I could talk about for days. This blog dives into a tool calledRAW Graphs,which solves for two of them, outlined below.
We need to enable subject matter experts (SMES) to provide…
Summary: Exceptions sometimes make the best rules. Here’s an example of well accepted variable reduction techniques resulting in an inferior model and a case for dramatically expanding the number of variables we start with.
One of the things that keeps us data scientists on our toes is that the well-established…
This is a new series, featuring great content from our top contributors. Some of these articles are rather technical in nature, but many are business-oriented and written in simple English. The entire series consists of about 120 articles. We intend to publish a new set every two weeks or so. Click here to check out the…
The two main data types in business are nominal (categorical or qualitative data) and interval data (quantitative or continuous data). Nominal data are just categories on variables such as customer names, and marital status and you cannot do any mathematical operations on this type of data. Bar chart and Pie chart are usually used to describe nominal data. On the other hand, interval data hold numerical values on variables such as income, age, and invoice amount and you can do mathematical…
Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week.
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Enhance your data analysis skills with an online degree from Penn State World Campus. Our programs focus on data and data management. Learn to collect, classify, analyze,…
MLPs (Multi-Layer Perceptrons)are great for many classification and regression tasks. However, it is hard for MLPs to do classification and regression on sequences. In this Python deep learning tutorial, a GRU is implemented in TensorFlow. Tensorflow is one of the many Python Deep Learning libraries.
We now live in a data-immersed society. What used to be a term that was mostly the domain of folks in white lab coats is now thrown around by just about everyone--salespeople, soccer players, surfers, you name it. “How much data do you get in your plan?” “Do you get unlimited data?” So the burning question is, what is data?
Data is basically just raw information about something--anything--in some form that…
Organizations looking for justification to move beyond legacy reporting should review this little ditty from the healthcare industry:
The Institute of Medicine (IOM) estimates that the United States loses $750 billion annually to medical fraud, inefficiencies, and other siphons in the healthcare system…
“Missile approaching, Every one run for cover”. People sending final goodbye messages to their loved ones. Family members huddling and praying together. Mother clinging to children, while the father hurriedly makes for a safe getaway for the family to the basement. Imagine the state of confusion and chaos every Hawaiian would have faced on Jan 13, 2018…
Today I've just faced one challenge... I work on one project, and we need to decide which assets from this company will be visited initially. As every project, we have limited budget, so we should find some variables to indicate a priority scale. After some discussion, someone said: let's find the assets close to schools and hospitals. Those must be inspected firstly than others. Everybody agreed with the voice, but how would be possible to map every school and…
There was a time when developing a data warehouse was sufficient to quench the thirst for data, reporting, and analytics of most business users. Not anymore. Organizations have discovered that data can be a valuable business asset. It has taken some time, but finally they realize they can do more with all the data that’s available than just produce simple reports. With the right data they can distinguish themselves from the competition, reduce costs by optimizing business processes, and…