SlideShare a Scribd company logo

Big data analytics

•Download as PPTX, PDF•
•
R
Ravi Teja

Unstructured data contains different types of data that is not contained in structured databases, and makes up over 90% of social media data. Analyzing unstructured data from sources like social media, emails, and documents can provide insights into customer perceptions and improve productivity. Common types of unstructured data include text files, photos, videos, and audio. Tools for analyzing unstructured data include R, RapidMiner, Weka, Python, and Hadoop, each with different strengths and specializations. Sentiment analysis of social media data can help companies in sectors like insurance understand customer opinions.

Related slideshows

Data mining with big data
Data mining with big dataData mining with big data
Data mining with big data
 
Big data
Big dataBig data
Big data
 
Big Data Projects Research Ideas
Big Data Projects Research IdeasBig Data Projects Research Ideas
Big Data Projects Research Ideas
 
1 of 19
Big data Analytics
Big data Analytics Introduction  Unstructured data contains different (multiple) types of data  Unstructured data is a generic label for describing data that is not contained in a database or some other type of data structure.  Unstructured data contains everything and presents everywhere globally.  More than 90% of social media data is unstructured.
Big data Analytics Importance of Unstructured Data  Every minute, there are more than 6,000 pictures shared on social media sites and more than 200 million emails sent.  Analyzing social content such as Tweets, Facebook posts and transcripts from support calls gives a clear view of how customers perceive the value and issues.  Unstructured data isn't well organized or easy to access, but companies who analyze this data and integrate it into their information management landscape can significantly improve employee productivity.
Big data Analytics Examples of Unstructured Data  e-mail messages, word processing documents, videos, photos, audio files, presentations, web pages.  Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files.
Big data Analytics Influence of Unstructured data on Social media  The social media needs to be part of the business strategy by interacting with clients on customer.  The statistics contain the number of Twitter followers, Facebook likes, LinkedIn connections, blog subscribers.  Social media like Facebook is growing enormously with the massive amount of unstructured data, they are collecting.  Twitter sees about 175 million tweets each day and has more than 465 million accounts.
Big data Analytics
Big data Analytics Technologies  Data mining  Pattern Recognition  Operations Research  Social Network Analytics (Facebook, Twitter, LinkedIn)  Natural Language Processing
Big data Analytics Tools to analyze  R language  Rapid Miner  Weka  Hadoop  Python
Big data Analytics RapidMiner  Rapidminer provides an integrated environment for machine learning, data mining, text mining, predictive analytics.  It is the most powerful tool, easy to use and intuitive graphical interface for the design of analytic process.  The code is written in JAVA.  Runs on all major platforms and operating system.  Save time by identifying possible errors, and get suggested quick fixes and support .csv, excel and binary files.
Big data Analytics RapidMiner Imported from csv, excel files. Statistics, charts.
Big data Analytics Weka  Weka is a collection of machine learning algorithms.  It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.  It is s written in Java and runs on almost any platform.  Large collection of different data mining algorithms.
Big data Analytics Python  Connect python with R by installing package “Rserve”  High level language and easy to interpret.  Free and open source, runs on all platforms.
Big data Analytics R language  R is very effective statistical tool and well worth the effort to learn.  R is polymorphic, which means that the same function can be applied to different types of objects.  R has more than 4000 packages available from multiple repositories in various specializations.  CRAN (Comprehensive R Archive Network).  R can import data from csv files, excel, sas and produces the output in pdf, jpg, png formats and also table output.
Big data Analytics R langauge Working with R studio, loading packages, extracting the tweets.
Big data Analytics Unstructured data Analysis for Motor Insurance  Extracting the data from social media related to Motor insurance sector.  Company names, keywords.  Getting the tweets from twitter and analyzing the data.  Sentiment analysis.  User interface.  What type of insurance can be given or any fraud detection?
Big data Analytics Extracting data from Twitter using R  Need to create an app  api_key  api_token  access_token  access_secret
Big data Analytics Sentiment Analysis from Twitter
Big data Analytics Comparison between data mining tools Characteristic R Rapidminer Weka Purpose Statistics,Clusteirng and analytics Data Mining, Classification Data Mining, Association. Data Import .xlsx, csv, RODBSC, .txt .csv.xlsx, binary files .csv.arff Specialization It has a large number of users, in the fields of bio- informatics and social science. Specialized for Business solutions that include predictive analysis and statistical computing. Weka is best suited for mining association rules and machine learning techniques. Advantages Purely statistical Visualization, Parameter optimization Ease of use and machine learning
Big data Analytics

More Related Content

Big data analytics

  • 2. Big data Analytics Introduction  Unstructured data contains different (multiple) types of data  Unstructured data is a generic label for describing data that is not contained in a database or some other type of data structure.  Unstructured data contains everything and presents everywhere globally.  More than 90% of social media data is unstructured.
  • 3. Big data Analytics Importance of Unstructured Data  Every minute, there are more than 6,000 pictures shared on social media sites and more than 200 million emails sent.  Analyzing social content such as Tweets, Facebook posts and transcripts from support calls gives a clear view of how customers perceive the value and issues.  Unstructured data isn't well organized or easy to access, but companies who analyze this data and integrate it into their information management landscape can significantly improve employee productivity.
  • 4. Big data Analytics Examples of Unstructured Data  e-mail messages, word processing documents, videos, photos, audio files, presentations, web pages.  Examples of "unstructured data" may include books, journals, documents, metadata, health records, audio, video, analog data, images, files.
  • 5. Big data Analytics Influence of Unstructured data on Social media  The social media needs to be part of the business strategy by interacting with clients on customer.  The statistics contain the number of Twitter followers, Facebook likes, LinkedIn connections, blog subscribers.  Social media like Facebook is growing enormously with the massive amount of unstructured data, they are collecting.  Twitter sees about 175 million tweets each day and has more than 465 million accounts.
  • 7. Big data Analytics Technologies  Data mining  Pattern Recognition  Operations Research  Social Network Analytics (Facebook, Twitter, LinkedIn)  Natural Language Processing
  • 8. Big data Analytics Tools to analyze  R language  Rapid Miner  Weka  Hadoop  Python
  • 9. Big data Analytics RapidMiner  Rapidminer provides an integrated environment for machine learning, data mining, text mining, predictive analytics.  It is the most powerful tool, easy to use and intuitive graphical interface for the design of analytic process.  The code is written in JAVA.  Runs on all major platforms and operating system.  Save time by identifying possible errors, and get suggested quick fixes and support .csv, excel and binary files.
  • 10. Big data Analytics RapidMiner Imported from csv, excel files. Statistics, charts.
  • 11. Big data Analytics Weka  Weka is a collection of machine learning algorithms.  It contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.  It is s written in Java and runs on almost any platform.  Large collection of different data mining algorithms.
  • 12. Big data Analytics Python  Connect python with R by installing package “Rserve”  High level language and easy to interpret.  Free and open source, runs on all platforms.
  • 13. Big data Analytics R language  R is very effective statistical tool and well worth the effort to learn.  R is polymorphic, which means that the same function can be applied to different types of objects.  R has more than 4000 packages available from multiple repositories in various specializations.  CRAN (Comprehensive R Archive Network).  R can import data from csv files, excel, sas and produces the output in pdf, jpg, png formats and also table output.
  • 14. Big data Analytics R langauge Working with R studio, loading packages, extracting the tweets.
  • 15. Big data Analytics Unstructured data Analysis for Motor Insurance  Extracting the data from social media related to Motor insurance sector.  Company names, keywords.  Getting the tweets from twitter and analyzing the data.  Sentiment analysis.  User interface.  What type of insurance can be given or any fraud detection?
  • 16. Big data Analytics Extracting data from Twitter using R  Need to create an app  api_key  api_token  access_token  access_secret
  • 17. Big data Analytics Sentiment Analysis from Twitter
  • 18. Big data Analytics Comparison between data mining tools Characteristic R Rapidminer Weka Purpose Statistics,Clusteirng and analytics Data Mining, Classification Data Mining, Association. Data Import .xlsx, csv, RODBSC, .txt .csv.xlsx, binary files .csv.arff Specialization It has a large number of users, in the fields of bio- informatics and social science. Specialized for Business solutions that include predictive analysis and statistical computing. Weka is best suited for mining association rules and machine learning techniques. Advantages Purely statistical Visualization, Parameter optimization Ease of use and machine learning