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Experfy Insights provides cutting-edge perspectives on Big Data and analytics. Our unique ability to focus on business problems enables us to provide insights that are highly relevant to each industry.
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The Evolving Role of Big Data in Retail

The retail value chain is impacted by Big Data in many profound ways: retailers use Big Data to collect data on their customers so that they can better serve them and market themselves to them; they interact with payment systems whose information must be secured against sophisticated threats; and their logistics and operations can be markedly improved by using Big Data effectively.

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Internet of Things: Market, Opportunity and Challenges

The IoT is about a world in which physical objects ranging from gas turbines to mobile phones and television sets are connected to the Internet. That connectivity imbues these devices with intelligence and enables them to collaborate with each other and with people to adapt to the contexts in which we live, work, and play. 

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Managing Your Big Data Security Strategy in 2017

In the era of Big Data, as the amount of data available for analysis has exploded exponentially, securing that data has become absolutely essential for corporate success. Firms like Target and Home Depot know first hand the deleterious impact that data breaches can have on the corporate brand and bottom line.

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Claims Severity Prediction with Apache Spark 2.0 and Scala

Allstate, the second largest insurance company in United States, recently launched a Machine Learning recruitment challenge to predict the cost and the severity of claims.

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How Big Data is Revolutionizing Healthcare

Few American industries are as primed to be revolutionized by Big Data as healthcare. In this blog post, Harpreet Singh, the Founder and Co-CEO of Experfy gives us three ways in which Big Data in fundamentally reshaping healthcare.

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The importance of data strategy for startups

Data strategy is the one thing many companies need, especially startups, but they don't know it. So, what is a data strategy? How can we define it?

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Data Science: The Art of Communication

This is a follow-up to the three-part series on 'How to Become a Data Scientist’. It is effectively Part 2a, because it became apparent that the second instalment on ‘Learning’ did not encompass sufficient detail on how to improve the essential, and often overlooked skill of communication.  

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Industrial Machine Learning: Where are We Now?

The rapid growth of Machine Learning and Artificial Intelligence applications is not only in the software domain. As the use of sensors in industrial applications becomes more mainstream, the large amount of data gathered can be used to train algorithms. This, in turn, results in rapid increases in efficiency.

In this article, chemical engineer and researcher Hermes Ribeiro Sant Anna gives an overview of how Machine Learning and Artificial Intelligence are used in industrial production facilities. 

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How to Become a Data Scientist (Part 3/3)

This is Part Three in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone interested in this field, irrespective of background and level of experience. The topic of Part Three is: 'The Job Market'.

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How to Become a Data Scientist (Part 2/3)

This is Part Two in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part Two is: "Learning".

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How to Become a Data Scientist (Part 1/3)

This is Part One in a three-part series examining how to become a data scientist. Supported by extensive research and expert opinions, it aims to provide a comprehensive guide to anyone looking to move into this field, irrespective of background and experience. The topic of Part One is: "What is Data Science?".

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Creating a Data-driven Culture Within an Organization: Sports Clubs

This fun-to-read post describes the similarities between implementing a data-driven culture within a sports team and within a company. 

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Moneyball: Sports Analytics in Soccer to Predict Performance and Outcomes

Soccer's dynamic nature makes it a notoriously difficult game to predict. But if you have enough data and attributes, you can increase the accuracy of your predictions by using advanced machine learning and artificial intelligence methods.

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Talent Analytics: The Art of Predicting Employee Retention

As the volume of employee data at organizations' disposal increases, novel ways to make use of this data can be defined. By using simple metrics to define your current, past and potential employees, you can increase retention and make sure you hire people who are a good fit culturally. 

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BI on Steroids! Providing Value to Barcelona Residents

This post gives an extensive overview of the development process and the capabilities of Infobarris, a tool developed to support the analysis of health and its determinants in the neighborhoods of the city of Barcelona. 

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Are Businesses Ready To Take Advantage of The Growing Number of Data Scientists?

There's significant growth in the number of data scientists in the past four years, but this growth is not matched with a growth in the number of data-savvy managers. Will this mismatch lead to problems in the near future?

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The Case for Using Data Simulators to Drive Big Data Success

Data simulators can be an important asset to a company because they can emulate the data you don't have access to, and help you gauge the compatibility of your tools accordingly.

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Practical Text Analytics

This post dives into different types of text analytics and gives three different examples from the industry to better convey the ideas behind these methods.

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Time Series in Atmospheric Sciences

Predictive analytics can save lives, and Majid Khattak demonstrates how to do just that by using an index of the El-Nino-Southern Oscillation phenomenon to predict weather patterns that lead to severe storms. 

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Suggestion and Opinion Mining from Qualitative Surveys

Analyzing traveler feedback on a newly opened airport in Asia, Ravi gives us a walkthrough of his approach to a specific sentiment analysis problem.

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Short-Term Forecasting of Electricity Demand

Forecasting demand for electricity is of immense importance both for researchers and for industry experts. By accurately forecasting demand, power suppliers can prevent overloads, and can operate more efficiently with a higher profit margin.

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Bringing HR Analytics to Life - Unleashing the Power of Your Organization

A detailed article on HR Analytics, explaining what it is, how it works, and how organizations can leverage it to improve their culture and effectiveness.

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Piecewise Linear (Affine) Programming

Linear programming is a mathematical problem solving technique widely used in various industries to optimize operations and is widely recognized as one of the core concepts of Operations Research. In this post, Professor Lahiri gives a summary of the technicalities of this method. 

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Demystifying Facebook's Ad Match Algorithm

How Facebook's Ad Matching algorithm works and how marketers can use this knowledge to their advantage.

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