Data Science

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."

Some years ago, at Gramener, we built a customer churn modeling solution for one of the largest global telecom operators. The machine learning solution predicted which of their customers would leave, one month before they stopped usage. In test pilots, the solution helped reduce customer churn by more than 56 percent compared to the earlier process. We were amazed at the impressive results and stellar accuracy. But the celebrations were a bit premature, for the solution was never used.

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.

Data Science refers to the multi-disciplinary field that extracts knowledge and insights from data using processes, algorithms, and systems based in the scientific method. Data science is a melting pot of statistics, machine learning, and data analysis, using each to their strengths to understand and conceptualize real-world phenomena. Referenced as early as 1960, the term "data science" didn't really come into its own until 1990's. As technology became more powerful, and the access to information became more widespread, the applications of data science proliferated. Turing award winner Jim Gray refers to data science as the "fourth paradigm" of science, along with empirical, theoretical, and computational.

Be careful which skills you put on a pedestal, since the effects of unwise choices can be devastating. In addition to mismanaged teams and unnecessary hires, you'll see the real heroes quitting or re-educating themselves to fit your incentives du jour. A prime example of this phenomenon is in analytics. The top trophy hire in data science is elusive, and it's no surprise: "full-stack" data scientist means mastery of machine learning, statistics, and analytics. When teams can't get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Today's fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning darlings of the hiring circuit.

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.

I will be sharing my perspective on which is actually more sought after in the current industry. Let me ask you one question. If you were the tech lead of data science, and there already has a lot of Ph.D. people working for you, at the same time, you would like to expand your team. You have two candidates in mind, one is better in coding and one is better in math concept, which candidate will you prefer? There is no right or wrong answer to this question, but from what I observed, usually, they will prefer the ones who have better skills in coding.

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.