Big Data Analytics - It is here and now!

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1. BIG DATA BIG DATA is not the next big thing It is Here And Now 2. BIG DATA 3. BENEFITS OF BIG DATA ANALYTICS 61% 45% 41% 38% 37% 35% 33% 30% 30% 30% 30% 29% 29% 27% 6%…
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  • 1. BIG DATA BIG DATA is not the next big thing It is Here And Now
  • 2. BIG DATA
  • 3. BENEFITS OF BIG DATA ANALYTICS 61% 45% 41% 38% 37% 35% 33% 30% 30% 30% 30% 29% 29% 27% 6% 4% Better targeted social influencer marketing More numerous and accurate business insights Segmentation of customer base Recognition of sales and market opportunities Automated decisions for real-time processes Definitions of churn and other customer behaviors Detection of fraud Greater leverage and ROI for big data Quantification of risks Trending for market sentiments Understanding of business change Better planning and forecasting Identification of root causes of cost Understanding consumer behavior from clickstreams Manufacturing yield improvements Other
  • 4. •Profit growth than their competition •CIOs have visionary plans that include business analytics •Leaders don’t have information they need for critical decisions •More likely to outperform their competition 2X 1/3 2X83% Big Data is about getting valuable information hidden in data we weren’t able to analyze by traditional approaches BENEFITS OF BIG DATA ANALYTICS
  • 5. 3V of Big Data Structured Unstructured Semi structured All the above Batch Near time Real time Streams Terabytes Records Transactions Tables, files Volume Velocity Variety
  • 6. Source: IBM Website
  • 7. Typical Applications of Big Data Sentiment Analysis Text Analytics Volume Trending Influencer Identification Predictive Analytics In-Memory Analytics Massively Scalable Architectures Forecasting estimating quarterly sales, product demand. Neural networks can assess how likely it is that a credit card transaction is being performed by the cardholder. Response models can predict how likely a particular person is to respond to a particular marketing offer, based on the success or failure of offers made in the past. Predictive scorecards can determine the likelihood that someone will fail to make payments on his or her loan in the coming year.
  • 8. MAJOR TOOLS USED FOR BIG DATA ANALYTICS Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. Scalable, reliable, fault tolerant Provides storage layer and execution layer Heart of Hadoop A programming paradigm that allows for massive scalability across hundreds or thousands of servers in a Hadoop cluster
  • 9. World’s most widely used statistics programming language designed to handle big data through a high-performance disk-based data store called XDF and high performance computing across large clusters An environment for machine learning, data mining, text mining, predictive analysis and business analytics Provides data loading and transformation (Extract, transform, load ETL) data preprocessing and visualization, modelling, evaluation, and deployment It is written in the Java programming language MAJOR TOOLS USED FOR BIG DATA ANALYTICS
  • 10. BIG DATA ANALYTICS – WHO ARE USING IT? Science and Research Government
  • 11. Data Analytics in Education A Case Study
  • 12. The Study • This study examines whether specific instructional strategies are associated with incidence of off-task behavior in elementary school children. These findings can begin to form a foundation for development of research-based guidelines for instructional design aimed to optimize focused attention in classroom settings. The Purpose
  • 13. Methodology – 22 classrooms participated – 5 local charter schools – 5 grade levels (K-4) – Average class size: 21 students (10 males, 11 females) – Each classroom was observed four times (total 84 observations) – Each observation lasted for 1 hr apprx. On-task: If the child was looking at the teacher (or classroom assistant), the instructional activity, and/or the relevant instructional materials, they were categorized as on-task. Off-task: If the child was looking elsewhere, they were categorized as off-task
  • 14. Off-task behaviour • Self-distraction • Peer distraction • Environmental distraction • Supplies • Walking • Other • Unknown
  • 15. Data Analysis: Variables • Predictor Variables – Student characteristics • Gender • Grade – Instructional design • Instructional format – Individual work – Small group or partner work – whole-group instruction at desks – whole-group instruction while sitting on the carpet – dancing, and – Testing • Duration of Instructional format
  • 16. Results Data Analysis: Approach •Regression tree analysis than linear regression •Resultant models were evaluated using six-fold student level cross-validation.
  • 17. Results
  • 18. Conclusion • Instructional format and instructional duration both are related to the overall rate of off-task behaviour. • Certain types of instructional format are associated with more on-task behavior than others. • Instructional activities that take place individually or at the students’ desks may be less engaging or motivating than small-group activities • Better attention in blocks of activities than an activity for a longer duration.
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