Aon's Steve Petrevski discusses how Covid-19 has affected the analytics industry and what trends he expects to follow the pandemic. As the senior vice-president and general manager of Aon's data and analytics services, Steve Petrevski is an analytics expert who is responsible for driving growth strategy through new capabilities that leverage emerging technologies for digital businesses. "A key goal is developing Aon's data and analytic services platform, which brings together data, technology, expertise and solutions in a single place," he said. "These solutions include new data-driven products, digital distribution, marketplaces to match risk with capital and analytics-as-a-service capabilities." Petrevski has also recently worked on the integration of CoverWallet, a digital insurance platform for SMEs that Aon acquired at the start of this year to enhance the way it engages with small and medium-sized businesses. 'Product people, data scientists, digital marketing experts, data engineers and DevOps will all be in demand' – STEVE PETREVSKI Outside of his role at Aon, Petrevski also serves as an adviser to a number of start-ups in the areas of security, fraud and analytics.

Are you looking to build a machine learning and AI-based Intelligent app? You must need a huge amount of datasets to train your model. Mostly a machine learning project fails not because of the model and infrastructure but poor datasets . Especially the beginner who just started with data science wastes a lot of time in searching the best Datasets for machine learning projects. To help them out and save their valuable time, We have designed this article which includes a chain of data source links from where you can download Datasets for machine learning projects and start a machine learning project.

Report Scope: The scope of the report includes, a general outlook of the analytics industry, with the scope limited to reports published during the year 2016, 2017 and 2018. This report covers only advanced analytics, artificial intelligence, and cognitive computing technologies. GNW The advanced analytics market covers the following solutions: software tools, integrated hardware appliances, and services. The advanced analytics market comprises applications for the following industries: banking and financial services, telecommunications and IT, healthcare, government and defense, transportation and logistics, and consumer goods and retail. The AI market covers machine learning, deep learning, and expert systems as these are direct derivatives of analytics.Cognitive computing market in this report covers machine learning and expert systems.

Machine learning, artificial intelligence (ML & AI) and big data form up a new niche area that is seeing a fast-paced growth rate in India. To clarify terminologies for a layperson, AI is basically all about mimicking human intelligence in machines, ML is a sub-set of AI and is about techniques that enable these machines to continuously learn on their own through data and perform a desired set of processes. Big Data analytics is about extracting huge data and observing unanticipated patterns from the same, while ML uses the same to provide incremental data/information to help the machine learn on its own. Data science and big data industry in India is growing at 33per cent CAGR (Compounded annual growth rate) and stood at $2.71 Billion in 2018. While the Finance & Banking industry leads the share in the analytics market, travel-hospitality and healthcare saw the fastest growth in recent years, in terms of analytics-use.

"Data Scientist is the sexiest job of the 21st century" – Harvard Business Review "Expect a shortage of over 100,000 data scientists by 2020" – Gartner Unarguably, in today's hyper-competitive marketplace, Data Science plays an indispensable role for organizations to personalize experiences and create value out of their data. Analyzing large data sets without preset defined rules or scope for analysis to uncover insights, a sublime concept till a few years ago, will form the key basis of competition in the future to significantly unlock business value, unleashing new waves of productivity for businesses, enabling a culture of innovation, and reinvigorating internal processes, as long as the right ecosystem and enablers are put in place. Numerous articles today are buzzing with this glamourous new word in the Analytics world i.e. So what exactly is Data Science or this hype around Data Scientist? Frankly speaking, multiple definitions, roles, job descriptions exist making it harder for businesses to understand what truly is the role about and the ROI out of making any additional investments.

The twenty-first century has ushered in a new age of data science and analytics. With the advent of automation, Artificial Intelligence (AI) and Machine Learning (MI) companies are slowly adapting and changing their learning curve towards software analytics. Data-driven innovation forms a key pillar for the sources of growth in the 21st century. The confluence of several trends, including the increasing migration of socio-economic activities to the Internet and the decline in the cost of data collection, storage and processing, is leading to the generation and use of huge volumes of data -- commonly referred to as "Big Data". These large data sets are becoming a core asset in the economy, fostering new industries and ecosystems, processes and products and creating significant competitive advantages.

"Data Scientist is the sexiest job of the 21st century" So, what exactly is data science and why all the hype around data scientists. Frankly speaking, multiple job descriptions and explanations of the same role make it harder for businesses to clearly understand what a data scientist is and does. This complicates the ROI business leaders expect when investing in them. To me, data Science involves mining actionable and sensible insights from multiple data formats by applying mathematics, statistics, machine learning, etc. Data scientists typically analyze data sets, or data depositories that are maintained within an organization and/or they analyze data scraped from publicly available sources.

Self-service hasn't transformed the analytics industry the way it did the gas industry. When data mining work benches first came on the market, I found that executives loved seeing the product demonstrations. They were drawn to the graphics and the speedy setup. That was exactly what data mining product developers had in mind. The object was to put analytics power into the hands of business people, enabling them to discover useful patterns in data and put the information into action independently, without the participation of an analytics specialist.