Image Source: Statistical Aid: A School of Statistics

Definition

In statistics, the statistical…

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What are the Importances of Data Science in the Modern Age?

Data Science is considered one of the best lucrative job fields of the 21st century. It is nothing other than the combined study of mathematics, statistics & computer science (including domain and programming expertise); which is used to…

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The constant evolution of technology has benefited every single industry on the face of Earth much more than you and I could imagine. This holds especially true for the financial sector, where it has given rise to FinTech, which has now been around for a while now. However, the advancement of technology has now given rise to yet another exciting development: The integration of IoT in FinTech. While IoT was already growing, it was only a matter of time until it made its way into this sector.…

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Across industries and enterprises, AI has been the key growth driver, and this is mainly due to the services offered under this technology umbrella. Every industry is now witnessing enhancements in business processes, costs, and efficiency due to AI's introduction in their field. Depending on the industry, AI offers something unique and has helped the key decision-makers to make fast and accurate decisions based on various implementation solutions.…

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11 Nov, 2020, Washington D.C.: Since the launch of the World Data Science Initiative (WDSI) in January of 2020 institutions in more than 20 countries have applied to be a part of the initiative, which is the world’s biggest ever talent development project in Data Science. The African continent can now boast its first institution to join the initiative; University of Rwanda.…

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Farming is the primary source that brings food to our tables. It is one of the most essential occupations to also supply humans with employment options all over the world. However, traditional farming methods like plowing the fields with the help of livestocks and ancient tools are now being replaced with advanced and latest technologies like…

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By

• Priya Sharma – Sr. Data Scientist -IoT Analytics, SAS Institute Inc.
• Saurabh Mishra – Product Management, IoT, SAS Institute Inc.

June 12, 2020

Description: Majority of AI approaches are based on the construct of training against historical data and then inferencing new data. While this is a sound and proven approach, a lot of IoT assets coming online don’t have historical data and we don’t necessarily have the time to wait.

Modern Machine…

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We always knew that the evolution of technology would lead us to newer avenues and opportunities in life. But little did we imagine that there would come a day that we would have a technology that would allow us to turn off electric appliances in our house from our office. No more “Did I turn it off” conundrums! That is the magic of the Internet of Things. And here’s an interesting statistic about this rockstar of technology: The number of connected devices in the world was 8.4 billion,…

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In today's hyper-competitive business world, B2B & B2C businesses rely on data to achieve an edge in their markets. However, many companies do not realize that customer data decays rapidly and create challenges in achieving business goals. In the postmodern era, customers simply change base too often, and to maintain a complete and accurate record of their whereabouts is a challenging task. Dirty data not only damages your credibility but also costs you money…

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Machine Learning (ML) models are increasingly being used to augment human decision making process in domains such as finance, telecommunication, healthcare, and others. In most of the cases, users do not understand how these models make predictions. The lack of understanding makes it difficult for policy makers to justify their decisions. Most of the ML models are black boxes that do not explain on its own why it reached a specific recommendation or a decision. This forces the users to say…

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Traditional programmers hit a stone-wall when they start working on Machine Learning (ML) development projects. They were used to coding based on design derived from requirements, logic/rules and when they move into a new paradigm in which rules are automatically generated from data, they are surprised. In ML paradigm, we do not find any output code and we see an output in the form of a binary object called model that goes into production. You may be wondering without measuring lines of code,… Continue

Summary:  99% of our application of NLP has to do with chatbots or translation.  This is a very interesting story about expanding the bounds of NLP and feature creation to predict bestselling novels.  The authors created over 20,000 NLP features, about 2,700 of which proved to be predictive with a 90% accuracy rate in predicting NYT bestsellers.

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Summary:  Communicating with your Board of Directors about AI/ML is different from conversations with top operating executive.  It’s increasingly likely your Board will want to know more and planning that communication in advance will make your presentation more successful.

 

As AI/ML becomes increasingly…

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The Merriam-Webster dictionary defines the word 'augment' this way: 'to make greater, more numerous, larger or more intense'. If you are wondering how this applies to the term 'augmented analytics', you are not alone. Let's take a closer look at Augmented Analytics and talk about why it has gotten so much attention in the business intelligence world.

What is Augmented…

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Players in ecommerce, entertainment, finance, Internet and social media space drive and influence risk indicators for not just for themselves but for users of Internet and increasingly to larger society as side effects spread to social, political and economic scenarios. Any risks from above domains are dominant and may easily be extrapolated in domains too.

There are five risks that may influence society negatively and turn in to resistance force for adoption of digital…

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Blockchain, which is a form of a decentralized digital ledger technology, has gained enormous attention in the recent years, precisely, since 2004. In fact, it has disrupted how major industries operate both locally and internationally.

Blockchain and security,…

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I built a scenario for a hybrid machine learning infrastructure leveraging Apache Kafka as scalable central nervous system. The public cloud is used for training analytic models at extreme scale (e.g. using TensorFlow and TPUs on Google Cloud Platform (GCP) via Google ML Engine. The predictions (i.e.…

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Summary: Now that we’ve detailed the four main AI-first strategies:  Data Dominance, Vertical, Horizontal, and Systems of Intelligence, it’s time to pick.  Here we provide side-by-side comparison and our opinion on the winner(s) for your own AI-first startup.

 

In our last several articles we’ve taken a tour of the four major…

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Summary:  The fourth and final AI strategy we’ll review is Systems of Intelligence (SOI).  This is getting nearly as much attention as the Vertical strategy we previously reviewed.  It’s appealing because it seems to offer the financial advantages of a Horizontal strategy but its ability to create a defensible moat requires some fine tuning.

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The age of technology is way past its nascent stage and has grown exponentially during the last decade. In my role, travelling to events and meeting with thought leaders I am aware of the developments made across different technological fronts.…

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