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business analytics


Linear Programming for Data Science and Business Analysis

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In this course you will learn all about the mathematical optimization of linear programming for data science and business analytics. This course is very unique and have its own importance in their respective disciplines. The data science and business study heavily rely on optimization. Optimization is the study of analysis and interpreting mathematical data under the special rules and formula. The length of the course is more than 6 hours and there are total more than 4 sections in this course.


What Separates Analytical Leaders from Laggards? Thomas H. Davenport, Nitin Mittal, and Irfan Saif

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Fourteen years ago, one of us (Davenport) wrote an article about how companies were beginning to compete on analytics. In the years that followed, data and analytics seemed to become embedded in business culture. Whether these tools were called analytics, big data, or artificial intelligence, organizations of all sizes and types supposedly embraced these resources as a way to improve decision-making and enhance offerings. How to explain, then, a recent Deloitte survey of U.S. executives that found that only 10% of companies are competing on their analytical insights, and that the most popular tool for analyzing data -- used by 62% of companies responding to the survey -- is the spreadsheet? Our survey results clearly show that analytical competitors represent a minority of businesses today, despite the number of years technologies like big data and analytics have been readily available.


radiant-rstats/radiant

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Radiant is an open-source platform-independent browser-based interface for business analytics in R. The application is based on the Shiny package and can be run locally or on a server. Radiant was developed by Vincent Nijs. For other questions and comments please use radiant@rady.ucsd.edu. There are two youtube playlists with video tutorials. The first provides a general introduction to key features in Radiant.


Artificial Intelligence and Machine Learning Trends in 2020 - DATAVERSITY

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"Artificial Intelligence and Machine Learning have reached a critical tipping point and will increasingly augment and extend virtually every technology enabled service, thing, or application." An article on the future of AI and machine learning mentions that while cloud technology offers "agility to business processes," artificial intelligence (AI) and machine learning (ML) technologies can jointly transform business outcomes. So what are some of the current artificial intelligence and machine learning trends coming soon? Let's begin with some interesting forecasts for the year 2020 and beyond: The Future of Machine Learning and Artificial Intelligence mentions that while cloud technology offers "agility to business processes," AI and ML technologies can jointly transform business outcomes. The Analytics Insights article, Top 10 Data and Analytics Trends to Watch Out in 2020, suggests how interrelated technology trends will impact the business landscape of 2020.


SIU College of Business celebrates business analytics and artificial intelligence programming launch

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In celebration of its newly-launched and highly-anticipated programs in business analytics and artificial intelligence, Southern Illinois University Carbondale's College of Business will host an evening reception for prospective students, business leaders and alumni on Wednesday, Nov. 13, in downtown Chicago. The event will take place from 6 to 8 p.m. on the 27th floor of the Deloitte building (Room 27E047), located at 111 S. Wacker Drive. Attendees will have the opportunity to meet faculty teaching these innovative courses, as well as analytics industry executives serving on the board of the university's one-of-a-kind Pontikes Center for Advanced Analytics and Artificial Intelligence. SIU recently launched an Analytics Concentration for its nationally-ranked online MBA program, a Bachelor of Science in Business Analytics, and will soon introduce a full graduate analytics program. All of these programs are uniquely designed to bridge the gap between data science and business by arming the managers and executives of tomorrow with leading-edge developments in artificial intelligence, prediction and data visualization, combined with a strong business foundation.


Responsible Gaming in the Age of Machine Learning - BizAcuity

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Let's face it, casino gaming is a huge business. It brings more than $500 billion in revenues every year from all around the world and the rise of the internet has fuelled its growth further. Today, online casino games has reached several people across all age groups through popular fantasy games. But with its growth comes another issue: the need to draw the line before excessive casino gaming becomes a problem, or as we like to call it, 'irresponsible gaming'. Being a part of the industry, there's no way we can shy away from addressing this issue.


Business Analytics in 2020: From BI to AI

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Collectively, humans now generate 2.5 quintillion bytes of new data per day. The data we generate in a single year dwarfs every metric ever created between 2015 and the beginning of recorded history. In other words, the BI tools of the past can hardly be expected to keep up with today's demands. Not only is the overall amount of data increasing, the number of types of data are increasing, and the applications that store and generate data are increasing as well. Older BI tools can't cope with larger volumes of data, and they also find it difficult to process data from new applications; it often takes a lot of manual adjustments to make an old BI tool fit a new app.


Artificial intelligence: Cheat sheet

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Many business AI platforms offer training courses in the specifics of running their architecture and the programming languages needed to develop more AI tools. Businesses that are serious about AI should plan to either hire new employees or give existing ones the time and resources necessary to train in the skills needed to make AI projects succeed.


Insight-driven organization

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The amount of data available to organizations every day continues to proliferate at a staggering volume. But technologies such as analytics and artificial intelligence (AI) have the potential to help businesses make better use of these massive volumes of data. In an age of collaboration between humans and machines--what we call the "Age of With"1--organizations can gain advantage by designing systems in which humans and machines work together to improve the speed and quality of decision-making. But not every organization is optimizing the opportunities available in the Age of With. Some do little or nothing with data to aid their decision-making. Others carry out analytics projects in pockets of the business.


Business Analytics Using R – A Practical Approach - Programmer Books

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Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book.