Financial services firms are consolidating data traditionally managed in silos in order to analyse risk exposure, comply with regulatory mandates, and use the data for multiple purposes. Traditional technologies such as relational database management systems make it challenging, if not impossible, to process growing volumes of data and make it accessible, actionable and flexible to changing needs in terms of queries and analytics.

The market has evolved from technologists looking to learn and understand new big data technologies to customers who want to learn about new projects, new companies and most importantly, how organizations are actually benefitting from the technology.

According to John Schroeder, executive chairman and founder of MapR Technologies, Inc., the acceleration in big data deployments has shifted the focus to the value of the data.

The leading minds in sports convened in Boston at the annual MIT Sloan Sports Analytics Conference to share ideas about how big data will be a game-changer for fans, players, coaches, officials and front-office personnel.

Analytics and big data have potential in many industries, but they are on the cusp of scoring major points in sports. From coaches and players to front offices and businesses, analytics can make a difference in scoring touchdowns, signing contracts or preventing injuries.

The following Benchmark aims to give a comparaison between US, US, France, Germany and Japan in terms of Market revenues , annual growths and market penetration.

The best-selling book Moneyball by Michael Lewis changed the way people thought about sport, particularly for those owners, managers, and players with the biggest vested interests. Lewis’s book helped bring about a revolution in which player performance was measured and assessed using an evidence-based approach rather than a tradition dominated by anecdote and intuition.

By 2020, it is estimated that more than 40 ZB of data will be generated annually. This “Big Data” is transforming every single industry. In this blog, I will talk about how Big Data is transforming Public Transportation, especially Rail Transportation.

Big Data is transforming both the Plan phase and Operations phase of the Rail transportation. City authorities who are in-charge of this transportation service, should consider leveraging Big Data for 4 key use cases:

Big data comes with a lot of new terminology that is sometimes hard to understand.

Hereafter a the main list of Big Data terminology

This article was originally published by International Business Times.

Within the silos of incumbent financial services, so-called fintech companies are good at picking off one thing only and doing it well.

This approach is also taken within data science, where a lot of the properly intelligent work is about understanding the domain (problem) and how best to use the information/data for the problem you have. In doing so, a fintech approach—collaboration, open-sourcing code—is helping to gradually change the culture of finance, even in some hitherto heavily guarded domains.

Technology developments play a big role in transforming the Rio Olympics, whether improving result accuracy or increasing athletes’ performances. The next transformation and even greater impact are expected to come from the massive use of sensors combined with big data analytics.

The world has changed a lot since March 2020, and the coronavirus pandemic has affected nearly every aspect of our lives. While we've seen massive changes in technology already, another change happening right now is in big data and its role with DevOps.

This is the first video lecture in a series of data analytics topics and geared to individuals and business professionals who have no understand of building modern analytics approaches. This lecture provides an overview of the models and techniques we will address throughout the lecture series, we will discuss Business Intelligence topics, predictive analytics, and big data technologies. Finally, we will walk through a simple yet effective example which showcases the potential of predictive analytics in a business context.

This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.

This lecture introduces the concept of EDA, understanding, and working with data for machine learning and predictive analysis. The lecture is designed for anyone who wants to understand how to work with data and does not get into the mathematics. We will discuss how to utilize summary statistics, diagnostic plots, data transformations, variable selection techniques including principal component analysis, and finally get into the concept of model selection.

This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.

This lecture provides an overview of Time Series forecasting techniques and the process of creating effective forecasts. We will go through some of the popular statistical methods including time series decomposition, exponential smoothing, Holt-Winters, ARIMA, and GLM Models. These topics will be discussed in detail and we will go through the calibration and diagnostics effective time series models on a number of diverse datasets.

This lecture provides an overview of some modern regression techniques including a discussion of the bias variance tradeoff for regression errors and the topic of shrinkage estimators. This leads into an overview of ridge regression, LASSO, and elastic nets. These topics will be discussed in detail and we will go through the calibration/diagnostics and then conclude with a practical example highlighting the techniques.

This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.

This lecture provides an overview of the Fourier Analysis and the Fourier Transform as applied in Machine Learning. We will go through some methods of calibration and diagnostics and then apply the technique on a time series prediction of Manufacturing Order Volumes utilizing Fourier Analysis and Neural Networks.

This lecture provides an overview of Image Processing and Deep Learning for the applications of data science and machine learning. We will go through examples of image processing techniques using a couple of different R packages. Afterwards, we will shift our focus and dive into the topics of Deep Neural Networks and Deep Learning. We will discuss topics including Deep Boltzmann Machines, Deep Belief Networks, & Convolutional Neural Networks and finish the presentation with a practical exercise in hand writing recognition techniques on the MNIST dataset.

This lecture will focus our attention towards understanding the Big Data landscape from a Data Scientists perspective. The presentation will start off with a brief overview of the need for large scale data processing technologies and then introduce the underlying technologies which drive the modern big data landscape. The techniques pioneered by the Apache Foundation will be discussed in some technical detail, however, the emphasis will remain on creating a broad awareness of the Hadoop 2.0technologies as it relates to data science and machine learning. We will then introduce some mechanisms for applying the MapReduce framework, accessing HDFS data, and creating analytics within the R programming language. Finally, we will bring all of the Big Data concepts into focus through working a practical example of New York Taxi Cab data within R.