VideoBy Jonathan Gordon (@JW_Gordon), Manish Goyal, Tim McGuire LinkedIn maps data visualization. Luc Legay $50 billion. That’s about how much marketers are spending on Big Data and advanced analytics (according to a BMO Capital Markets report) in the hopes of improving marketing’s impact on the business. This commitment reflects a belief that big [...]

Stay up-to-date with the latest technological advancements using our extensive list of active blogs; this is a list of 100 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.

When people think of “data science” they probably think of algorithms that scan large datasets to predict a customer’s next move or interpret unstructured te...

If you were learning (and / or partying) with us at the Tableau Customer Conference 2012 in Beautiful San Diego – then you’ve probably been playing around with the Tableau 8 Beta over the past few weeks. Tableau 8 bring a whole new slew of features - INCLUDING 2 new APIs... I'm going to go…

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When recommending items to users, it is important to consider many performance metrics and not just the accuracy of a rating. Often research groups attempt to obtain the smallest difference between a…

Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current visualization packages are rudimentary and not immediately helpful to the novice. For example, we couldn't find a library that visualizes how decision nodes split up the feature space. So, we've created a general package (part of the animl library) for scikit-learn decision tree visualization and model interpretation.

Introduction Inspired by this Netflix post, I decided to write a post based on this topic using R. There are several nice packages to achieve this goal, the one we´re going to review is AnomalyDetection. Download full -and tiny- R code of this post here. Normal Vs. Abnormal The definition for abnormal, or outlier, is an element which does not follow the behaviour of the majority. Data has noise, same example as a radio which doesn't have good signal, and you end up listening to some background noise. The orange section could be noise in data, since it oscillates around a value without showing a defined pattern, in other words: White noise Are the red circles noise or they are peaks from an undercover pattern? A good algorithm can detect abnormal points considering the inner noise and leaving it behind. The AnomalyDetectionTs in AnomalyDetection package can perform this task quite well. Hands on anomaly detection! In this example, data comes from the well known wikipedia, which offers an API to download from R the daily page views given any {term + language}. In this case, we've got page views from term fifa, language en, from 2013-02-22 up to today. After applying the algorithm, we can plot the original time series plus the abnormal points in which the page views were over the expected value. About the algorithm Parameters in algorithm are max_anoms=0.01 (to have a maximum of 0.01% outliers points in final result), and direction="pos" to detect anomalies over (not below) the expected value. As a result, 8 anomalies dates were detected. Additionally, the algorithm returns what it would have been the expected value, and an extra calculation is performed to get this value in terms of percentage perc_diff. If you want to know more about the maths behind it, google: Generalized ESD and time series decomposition Something went wrong: Something strange since 1st expected value is the same value as the series has (34028 page views). As a matter of fact perc_diff is 0 while it should be a really low number. However the anomaly is well detected and apparently next ones too. If you know why, you can email and share the knowledge :) Discovering anomalies Last plot shows a line indicating linear trend over an specific period -clearly decreasing-, and two black circles. It's interesting to note that these black points were not detected by the algorithm because they are part of a decreasing tendency (noise perhaps?). A really nice shot by this algorithm since the focus on detections are on the changes of general patterns. Just take a look at the last detected point in that period, it was a peak that didn't follow the decreasing pattern (occurred on 2014-07-12). Checking with the news These anomalies with the term fifa are correlated with the news, the first group of anomalies is related with the FIFA World Cup (around Jun/Jul 2014), and the second group centered on May 2015 is related with FIFA scandal. In the LA Times it can be found a timeline about the scandal, and two important dates -May 27th and 28th-, which are two dates founded by the algorithm. Our Twitter and LinkedIn Group More posts here. Thanks for reading :)

Exploring and venting about quantitative issues

Just a few posts to share my work as a data scientist with you.

Download Big data complexity visual representation. Cluster analysis visualization. Advanced analytics. Beauty of data graphic abstract background. Stock Vector and explore similar vectors at Adobe Stock

This Big data and visualization hands-on lab blog will provide you exposure to many of Microsoft big data and advanced analytics services.

This Hadoop Tutorial will teach you everything you need to know about Hadoop, from the basics to the advanced concepts including big data analytics, machine learning, and data warehousing.

IoT data streams, big data and advanced analytics collide to create new challenges.

Big data is a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."

Explore the seamless operation of a data center equipped with high-performance networking gear. Dive into the world of server racks, big data, hosting storage hardware, and cutting-edge cyber data solutions. Learn how advanced topology and optimized systems keep your data secure and efficient. cyber data server, unik pic, server racks room, big data computer, hosting storage hardware, data server, cyber data, topology, data center, high-performance networking, storage solutions, big data analytics #DataCenter, #BigData, #ServerRacks, #HostingStorage, #CyberData, #UnikPic, #NetworkingGear, #DataTopology, #TechInfrastructure, #ServerRoom.

Explore the seamless operation of a data center equipped with high-performance networking gear. Dive into the world of server racks, big data, hosting storage hardware, and cutting-edge cyber data solutions. Learn how advanced topology and optimized systems keep your data secure and efficient. cyber data server, unik pic, server racks room, big data computer, hosting storage hardware, data server, cyber data, topology, data center, high-performance networking, storage solutions, big data analytics #DataCenter, #BigData, #ServerRacks, #HostingStorage, #CyberData, #NetworkingGear, #DataTopology, #TechInfrastructure, #ServerRoom,

In Supply Chain 4.0, supply-chain management applies Industry 4.0 innovations—the Internet of Things, advanced robotics, analytics, and big data—to jump-start performance, and customer satisfaction.

Advanced analytics, big data, data science; they're all hyped topics nowadays. They're not new however, and one specific subject area has always had some presence in Microsoft's ETL tool: text mining. Integration Services offers two transformations that c

A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India). A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India). A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India). A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India). A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India). A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India). A Practical Guide to learn HIVE, PIG, SQOOP Key Features ● Overview Of Big Data ● Basics of Hadoop ● Hadoop Distributed File System ● HBase, MapReduce ● HIVE: The Data-ware House Of Hadoop ● PIG: The Higher Level Programming Environment ● SQOOP: Importing Data From Heterogeneous Sources ● Flume, Ozzie, Zookeeper & Big Data Stream Mining. Description The book contains the latest trend in IT industry ‘Big Data and Hadoop’. It explain about how big is ‘Big Data’ and why everybody is trying to implement this into their IT project. It includes research work of various topics, theoretical and practical approach, each component of the architecture is described along with current industry trends. Big Data and Hadoop taken together are a new skill as per the industry standards. Readers will get a compact book along with the industry experience, and would be a reference to help readers. What will you learn ● Big Data, No SQL Data Management ● Basics of Hadoop, its Installation ● MapReduce Applications ● Hadoop Related Tools Who this book is for This book promises to be a very good starting point for beginners and an asset to advanced users too. Difficult concepts of Big Data-Hadoop is given in an easy and practical way, so that students can able to understand it in an efficient manner. Table of Contents 1. Big Data- Introduction and Demand 2. No SQL Data Management 3. Basics of Hadoop 4. Hadoop Installation (Step-by-Step) 5. MapReduce Applications 6. Hadoop Related Tools-I (Hbase & Cassandra) 7. Hadoop Related Tools -II (PigLatin & HiveQL) 8. Practical & Research based Topics 9. Appendix: Hadoop Commands 10. Chapter wise Questions 11. Previous Year Question Paper About the Author MAYANK BHUSHAN, Asst. Prof. in ABES Engineering College, Ghaziabad with an experience of more than eight years of teaching. He has done his graduation and post graduation in Computer Science & Engineering. He holds global certification on Big Data Analytics and Salesforce-Cloud computing, certification from Indian Institute of Technology Kharagpur on Linux platform. He has delivered many guest lecturers in private and government engineering colleges on big data-hadoop. He is a member of research initiative CIRG India (Scientific Research Organisation Reg. under society registration act XXI of 1860 Govt. of India).