Data Mining

In the Learning Systems Group of the Cloud and Information Services Lab (CISL), we research, design and develop state-of-the-art machine learning algorithms, tools and systems. Our mission is to contribute to the democratization of machine learning through research. To do so, we collaborate with Microsoft Research and the academic community. We actively engage in open source software development. Our work directly informs and shapes products such as Azure ML, Azure Data Lake and Microsoft R Server.

This is 4R's sixth consecutive year participating in the show and will present its latest Merchant Analytics approach. NextPoint provides an exclusive annual event which offers retailers and solution providers an experience uncommon from other industry events and trade shows. Mark Garland, Executive Vice President Sales, Marketing & Solutions, said, "It is hard to believe this will be our sixth year presenting at NextPoint. The last five years at NextPoint have provided excellent networking opportunities. We are looking forward to sharing how 4R positions retailers to earn more profit from their inventory with proven success stories."

Logistics is a mature, technologically-advanced, and analytically-sophisticated industry. Still, even after decades of improvements coming from the Industrial Engineering and Operations Research fields, major efficiencies can still be realized by applying advanced analytics, data infrastructure, and computing power. All business processes in logistics rely on accurate demand forecasting in the short, medium, and long-term to inform resourcing, planning, and staffing to support future needs. In three weeks we delivered a functioning production time-series forecasting framework using R and Spark. After six months we had scaled to a refined framework that produces timely forecasts on over several thousand locations in our client's network.

"We are probably one of the last generations of homo sapiens." Those were the opening words of acclaimed historian and best-selling author Professor Yuval Harari, who spoke at the World Economic Forum Annual Meeting in Davos, Switzerland, where politicians, thought leaders and executives from the world's leading companies congregate to discuss solutions to global challenges. What comes after us, Harari said, are entities that are more different from us than we were from our predecessors, the Neanderthals. However, those species will not be the outcome of the organic evolution of human genes, Harari explained, but the outcome of humans learning to engineer bodies, brains and minds. "This will be the main product of the economy of the 21st century."

This headline may seem a bit odd to you. Since data science has a huge impact on today's businesses, the demand for DS experts is growing. At the moment I'm writing this, there are 144,527 data science jobs on LinkedIn alone. But still, it's important to keep your finger on the pulse of the industry to be aware of the fastest and most efficient data science solutions. To help you out, our data-obsessed CV Compiler team analyzed some vacancies and defined the data science employment trends of 2019.

The amazing capabilities of AI, ML, and Predictive Analytics will lead to the transformation of various insurance processes beyond recognition. FREMONT, CA: Technology is transforming various aspects of the insurance industry. Innovative digital tools are streamlining insurance processes. Data science is one of the many areas in insurance which is impacted by technological advancements to a great extent. Data science is fueled by artificial intelligence (AI) and predictive analytics capabilities and offers insurance companies with actionable, concrete insights into a wide range of insurance processes.

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult.

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult.

For senior IT people, 2019 may not look to be the happiest of new years. Many experienced technologists are finding their roles outsourced, with other employers looking for only younger (read: cheaper) employees. "I had three jobs in three years," Mike, a 50-something New York-based IT specialist, told me a year ago. "They've all ended with even new hires being let go and the work outsourced. I had to go before a judge to explain my financial situation, and he said I should take a class to update my skills. As if that would fix it."

A blockchain, originally blockchain, is a growing list of records, called blocks, which are linked using cryptography. Each block contains a cryptographic hash of the previous block, a timestamp, and transaction data . According to Wikipedia, "Big data is a term used to refer to data sets that are too large or complex for traditional data-processing application software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate." Volume: The quantity of generated and stored data.