Production sphere embraces a wide range of processes related to all branches and stages of creating material goods. In addition, these material goods may be of different values and even have rather contrasting goals.
Added by Igor Bobriakov on December 18, 2019 at 8:30am — No Comments
Data science and analytics are growing in their popularity and range of applications in the modern world. Data science deals with answering questions and uncovering hidden insights, while analytics is rather concentrated on the processing itself and conducting of statistical…Continue
Added by Igor Bobriakov on December 18, 2019 at 8:00am — No Comments
H2O is a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks. The dataset we are using is the Bank Marketing Dataset. Here we need to train a model which will be able to predict if the client of the bank opens…Continue
Added by Igor Bobriakov on December 16, 2019 at 8:00am — No Comments
The history of F1 motor racing and the use of telemetry as a way to monitor car setup and performance dates back to the 80s. The first electronic systems were installed onboard the car, collected information for only one lap and the data were then downloaded when the car was back in the garage. The explosion of computing capabilities, in the 90s, contributed to the growth of intelligent data usage in the F1 and the…Continue
Walking by the hottest IT streets in these days means you've likely heard about achieving Streaming Machine Learning, i.e. moving AI towards streaming scenario and exploiting the real-time capabilities along with new Artificial Intelligence techniques. Moreover, you will also notice the lack of research related to this topic, despite the growing interest in it.
If we try to investigate it a little bit deeper then, we realize that…Continue
Added by Valeria on December 10, 2019 at 7:30am — No Comments
Big Data is probably one of the most misused words of the last decade. It was widely promoted, discussed, and spread around by business managers, technical experts, and experienced academics. Slogans like “Data is the new oil” were widely accepted as unquestionable truth.
These beliefs pushed technologies forward. Its stack, formerly developed by Yahoo! and now owned by the Apache Software Foundation, was recognized as “The” Big Data…Continue
Added by Valeria on December 10, 2019 at 7:21am — No Comments
While training the model, we want to get the best possible result according to the chosen metric. And at the same time we want to keep a similar result on the new data. The cruel truth is that we can’t get 100% accuracy. And even if we did, the result is still not…Continue
Added by Igor Bobriakov on December 6, 2019 at 9:00am — No Comments
Quite often, non-technical executives have difficulties understanding what programming, on a very fundamental level, is all about. Because of that knowledge-gap, they tend to hire and overburden experienced data professionals with tasks which they are hopelessly overqualified for. Such as, for example, doing ad-hoc SQL queries on CRM data: "You're the go-to-guy for all things data, and we need the results for the board meeting tomorrow." That's a quite humbling and frustrating…Continue
Added by Rafael Knuth on December 5, 2019 at 6:30am — No Comments
Summary: A little history lesson about all the different names by which the field of data science has been called, and why, whatever you call it, it’s all the same thing.
Our profession of…Continue
Added by William Vorhies on December 4, 2019 at 3:12pm — No Comments
A successful business requires new approaches to data management in this age. Modern advances in data science area provide an efficient solutions for numerous use cases.
Data science embraces a broad spectrum of tasks in the sphere of…Continue
Added by Igor Bobriakov on November 27, 2019 at 6:27am — No Comments
Summary: Too many solutions. We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems.
Added by William Vorhies on November 25, 2019 at 9:44am — No Comments
Many executives struggle to make sense of machine learning (ML) and deep learning (DL). Having a pragmatic relationship with technology, executives need to know on a very fundamental level: "What problems do ML & DL try to solve?" A simple, high-level answer to that question is: "It's all about building systems that do certain things better than humans, with as little intervention by humans as possible." That being said, the simplest way to distinguish between ML and its branch DL…Continue
Added by Rafael Knuth on November 19, 2019 at 12:30pm — No Comments
In the times when data is of extreme value, industries cannot afford to ignore it. The only right choice for them is to find new ways to use data for their benefit.
These words have become a motto for the sales industry along with the others. As far as the feature of the…Continue
Summary: AI/ML itself is the next big thing for many fields if you’re on the outside looking in. But if you’re a data scientist it’s possible to see those advancements that will propel AI/ML to its next phase of utility.
Actually the unlocking of hidden benefits and true potential of the data is an essential task for business. Also, customers` satisfaction appears to be a pushing force for evolution of services and products. If you have loads of customer data available for analysis the work on the improvement of the customers` satisfaction…Continue
Added by Igor Bobriakov on October 20, 2019 at 9:52pm — No Comments
Q1. Is MSc in Data science/Data Analytics same as ML/AI as some universities don’t have AI but Data Science?
Q2. I am interested in MS Data Science and not MS Analytics as the later is not technical in nature. Are MS Data Science and MS Data Analytics the same?
Q3. How to Choose Between a Master’s in Data Analytics vs Business…
Added by Tanmoy Ray on September 17, 2019 at 9:30am — No Comments
Added by Jesus Ramos on August 19, 2019 at 10:19am — No Comments
Let’s explore the complexity and vulnerability of IT infrastructure and how to build a modern IT infrastructure monitoring solution, using a combination of time series databases with machine learning.
Added by Tamar Gal on August 12, 2019 at 6:11am — No Comments
Data is the new fuel- it drives businesses towards exponential growths. It has the power to transform operational and add intelligent insights with its immense potential. The key, however, lies with understanding data and its insights.
Logistics, like other domains, can also leverage from the several advantages of data. It all begins with what to do with the collected data. Data Science will come into the picture with its amalgamation of statistical &…Continue
Added by Bhushan Patil on July 21, 2019 at 8:37pm — No Comments