BIG DATA WITH DINO PEDRESCHI: THE AGE OF DATA


The age of data

Dino Pedreschi, Professor of Computer Science at the University of Pisa and co-lead of KDD Lab - Knowledge Discovery and Data Mining Laboratory, talks about big data, machine learning and intelligent systems.

Emīlija Bonomo

In response to Lizaveta Hersch

Big data analytics is the often complex process of examining large and varied data sets -- or big data -- to uncover information including hidden patterns, unknown correlations, market trends and customer preferences that can help organizations make informed business decisions.

On a broad scale, data analytics technologies and techniques provide a means to analyze data sets and draw conclusions about them to help organizations make informed business decisions. BI queries answer basic questions about business operations and performance.  Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.

Lizaveta Hersch

Big data analytics is the often complex process of examining large and varied data sets -- or big data -- to uncover information including hidden patterns, unknown correlations, market trends and customer preferences that can help organizations make informed business decisions.

George Waters

Along with rise in unstructured data, there has also been a rise in the number of data formats. Video, audio, social media, smart device data etc. are just a few to name.

Careen Levi

Data science is an interdisciplinary field that includes statistics, predictive analytics, machine and deep learning and aims to get extra insights from data. The idea of data science is to run data experiments in order to reveal hidden patterns and dependencies.

Luned Birutė Mag Raith

In response to Dardan Dragić

Supervised learning is a method used to enable machines to classify objects, problems or situations based on related data fed into the machines. Machines are fed with data such as characteristics, patterns, dimensions, color and height of objects, people or situations repetitively until the machines are able to perform accurate classifications.

Supervised learning is a popular technology or concept that is applied to real-life scenarios. Supervised learning is used to provide product recommendations, segment customers based on customer data, diagnose disease based on previous symptoms and perform many other tasks.

Dardan Dragić

Supervised learning is a method used to enable machines to classify objects, problems or situations based on related data fed into the machines. Machines are fed with data such as characteristics, patterns, dimensions, color and height of objects, people or situations repetitively until the machines are able to perform accurate classifications.

Neelam Szczepański

In response to Esperanta Tomàs

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions while a data analyst finds answers to the existing set of questions.

Esperanta,

Data science includes retrieval, collection, ingestion, and transformation of large amounts of data, collectively known as Big Data. Data science is responsible for bringing structure to big data, searching compelling patterns, and finally advising decision makers to bring in the changes effectively to suit the business needs. Data analytics and machine learning are two of the many tools and processes that data science uses.

Esperanta Tomàs

Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources. A data scientist creates questions while a data analyst finds answers to the existing set of questions.

Hrœrekr Franzese

A data analyst is usually the person who can do basic descriptive statistics, visualize data and communicate data points for conclusions. They must have a basic understanding of statistics, a very good sense of databases, the ability to create new views, and the perception to visualize the data. Data analytics can be referred to as the basic level of data science.

Aisha Kamila Kuhn

AI systems are either weak AI or strong AI. Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Apple's Siri, are a form of weak AI.  Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities so that when presented with an unfamiliar task, it has enough intelligence to find a solution.

Valerija Vroomen

Ultimately, the value and effectiveness of big data depends on the human operators tasked with understanding the data and formulating the proper queries to direct big data projects. Some big data tools meet specialized niches and allow less technical users to make various predictions from everyday business data.

Oberto

The popularity of the term "data science" has exploded in business environments and academia, as indicated by a jump in job openings.  However, many critical academics and journalists see no distinction between data science and statistics.  Is there a difference between the two?

Mariana Lichtenberg

An interesting application of AI is the so-called Robo Reader.  Essay grading is very labor intensive, which has encouraged researchers and companies to build essay-grading AIs. While their adoption varies among classes and educational institutions, it’s likely that you (or a student you know) has interacted with these “robo-readers’ in some way.

Lucas Jessen

In response to elvira eva becket

Using big data, Telecom companies can now better predict customer churn; Wal-Mart can predict what products will sell, and car insurance companies understand how well their customers actually drive. Even government election campaigns can be optimized using big data analytics. Some believe, Obama’s win after the 2012 presidential election campaign was due to his team’s superior ability to use big data analytics.

I think this is a bit scary, knowing that a non-government company can predict my next buy makes me think of all the other use cases of big data analytics. Have you heard of the 24th frame? You can read more about it here.

elvira eva becket

Using big data, Telecom companies can now better predict customer churn; Wal-Mart can predict what products will sell, and car insurance companies understand how well their customers actually drive. Even government election campaigns can be optimized using big data analytics. Some believe, Obama’s win after the 2012 presidential election campaign was due to his team’s superior ability to use big data analytics.

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