Imarticus Learning - Big Data Analytics Training Institute | Certification in Big Data

Unlike other areas, the rate of disruption in the data platforms and analytics is not so disruptive. Looks like it may take its own course of journey and for now the Presence of large existing vendors shall loom large. There are some evident trends which shall remain significant in affecting the growth of disruptions – The trends shaping data platforms and analytics in the coming year – Gradual enhancement of maturity index of predictive analytics; Clients users interest in a self-service methodology for data readiness Open source vendors and users are recognizing various parameters of data governance; Of course, most of these trends have been visible for some time now and none of them has the potential to disrupt the market in 2016, but all of them can be viewed as potential dashboards for some real time and imminent   changes that are happening within enterprises as they look to take advantage of the opportunities for generating business intelligence. The mass...

Effective presentation is one of the most important skills which we need at a corporate level.  Mastering the art of presenting a topic needs lot of practice and delivery. Below are some of the points that can be considered while preparing for a presentation. Stick to your time limit – It is strongly recommended that the speaker should stick to the time limit. At the end of the day, nobody wants to be part of a presentation which is boring and taking too long to finish. You will lose the audience connect in this process. Understand your target audience – As a speaker, you should have a fairly good idea of who your audience is. If the crowd primarily consists of younger lot of people, you have to connect to them in a different way and make it very interactive to get the best result.  Don’t include lot of text in your PPT, use bullet points and then validate your point Articulate the major points rather than c...

Risk management faces new demands and challenges in the IT age. In response to the spate of recent financial crises, regulators are insisting on ever more detailed data and increasingly sophisticated reporting. Do you want to meet regulatory requirements for credit risk? Or do you want to go beyond the requirements and improve your business with your credit risk models? If your credit risk is managed effectively, you should be able to do both. Big Data represents the  future of risk management  and can help Risk teams gain better intelligence drawn from a variety of data sources, in almost real-time. Join us to learn more about how Big Data can help you manage  risk in our power-packed 2-day executive development program ! Checkout summary of  our past Executive Development Program on Anti-money Laundering was held on 28th and 29th July at Novotel Hotel, Bangalore.  The 2-day workshop was conducted by our resident AML/KYC experts Ratan Postwalla and S...

The amount of money laundered globally in one year is estimated at 2-5% of the global GDP, or between a staggering USD 800 billion – USD 2 trillion, according to United Nations Office on Drugs and Crime. The expression ‘Money Laundering’ is of a fairly recent origin, with the term first appearing in the US in a newspaper in 1973. The term “money laundering” owes its existence to Laundromats in the United States that were owned by the Mafia. This was done by purchasing outwardly legitimate businesses and mixing their illegal earnings with the legitimate earnings received from these businesses. But other historians differ from this version. According to them, money laundering is called so, because it perfectly describes how dirty money is put through a cycle of transactions, or washed, so that it comes out at the other end as legal or clean money. In other words, the source of illegally obtained funds is disguised through a series of transfers and...

As more companies recognize the need for a data science platform, more vendors are claiming they have one. Increasingly, we see organizations describing their product as a “data science platform” without describing the features that make platforms so valuable. A good data science platform should be able to … Find and understand past work, so that  data scientists  do not need to begin from scratch when asking new questions. Explore data on large machines, without dealing with development ops / infrastructure setup. Use new packages and tools,  safely , i.e., without breaking past work or disrupting environments for other business users. Scale out compute resources to run many computationally intensive and complex experiments at once. Track your work (i.e., your experiments) so they are reproducible. Share work with peers and non-technical users (with other areas of expertise), to get feedback on evolving research and results Data science  work is only v...