Summary: If you’re planning your AI/ML business strategy watch out for the confusion in categories and overly risky ratings given by some research and review sources. Read the research, then consult with your own data scientists for a better evaluation of risk. It’s likely not as bad as you think.Continue
Added by William Vorhies on March 2, 2020 at 12:42pm — No Comments
Bayesian Probability is like a reaction to the Mathematical Probability: what about our…Continue
Added by Marcia Ricci Pinheiro on February 16, 2019 at 11:30pm — No Comments
The use of formal statistical methods to analyse quantitative data in data science has increased considerably over the last few years. One such approach, Bayesian Decision Theory (BDT), also known as Bayesian Hypothesis Testing and Bayesian inference, is a fundamental statistical approach that quantifies the tradeoffs between various decisions using…Continue
Added by Kostas Hatalis on March 15, 2018 at 12:00pm — No Comments
When I was beginning my way in data science, I often faced the problem of choosing the most appropriate algorithm for my specific problem. If you’re like me, when you open some article about machine learning algorithms, you see dozens of detailed descriptions. The paradox is that they don’t ease the choice.
In this article, I will try to explain basic concepts and give some intuition of using different…Continue
Added by Luba Belokon on October 26, 2017 at 6:00am — No Comments
INTRODUCTION TO THE RESEARCH QUESTION
The research was conducted to find out what price maximises profit without sacrificing the high demand for the product due to the price being too high nor sacrificing the margins on the product due to the price being too low.
The goal is to experiment with different price levels for the same product in one market place and country to see how sales volumes change with prices and which volume level of…Continue
Search any data related posting and you’ll soon be up to your eyeballs in reports on the promise of the new data era, techniques to help build a better data engine or incorporate new data widgets, and infographics and visualizations showing the kinds of insights you can get with the right mix of techniques.
Got it. Data is big. It’s expected to grow in volume to 35 to 45 zetabytes by 2020. As in seven sets of three zeros big.
It’s also transforming business processes big.…Continue
Added by Anne Russell on August 3, 2016 at 11:00am — No Comments
"Abstract Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges. We propose a novel sparsity-aware algorithm for sparse data and…Continue
There are a number of movies that I consider much more suitable for television than theatre: Alien Versus Predator; Doom; Snakes on a Plane; The Cave; The Colony. The stories of these movies play out in environmentally-limited sometimes enclosed settings. In the theatre, I considered Alien Versus Predator the worst movie I ever had to sit through in my entire life, second perhaps only to the original Tron. But at home on a 36-inch display, it has become one of my all-time favourites. On the…Continue
Added by Don Philip Faithful on September 28, 2015 at 6:24am — No Comments
I am a newbie to Bigdata and would like to highlight some significant advantages if incorporated in a company's supply-chain management strategies, expecting the reader's views and suggestions.
Because, in recent past I have developed a online supply-chain management systems in which sellers and customers are matched using an algorithm. It acted as a decision support system and I needed to dig deeper on the available data to get more insights over the data pattern (even for…Continue
One of the marvels that the age of data and technology presents is the ability to analyze and determine the minutest of details in the world today. Several of these innovative breakthroughs pass unnoticed under the gaze of daily life. Yet it is this dissemination of data and integration of innovation that is intrinsic the modern world. One field which has risen from the fore of the data deluge is ‘…Continue
Added by Sumit Prasad on June 9, 2014 at 9:51pm — No Comments
One of the most popular methods or frameworks used by data scientists at the Rose Data Science Professional Practice Group is Random Forests. The…Continue
High Performance Computing (HPC) plus data science allows public and private organizations get…Continue
In following the big data 'buzz' and trends, it appears that there is a disconnect between our analytical goals (i.e., the types of questions our customers are trying to answer) and the computational substrate on which we build in order to answer them.Continue
Added by John Fairweather on December 10, 2012 at 10:42am — No Comments
Working and valorising Big Data is business-as-usual for companies that have built their business model on it. For companies that don’t compete on analytics, that is for whom analytics is not a core element of their strategy, it’s a huge challenge.
But Big Data is the talk of town nowadays. I think that a part of the growing management interest is due to two factors:
Added by Patrick Glenisson on September 23, 2012 at 11:26am — No Comments
Big Data can help in mapping and understanding customer behaviors, and in developing one-to-one marketing programs or innovative services. However, Big Data is too often presented as a technological capability subsequently requiring armies of data scientists to mine and analyse data.
Yes, managing and exploiting the growing amount of internal and external data is a necessary condition to steer business performance. But it is far from a sufficient condition.
In a recent meeting…Continue
Added by Patrick Glenisson on June 16, 2012 at 6:53am — No Comments