Big Data in Education Sector
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Big Data, Data Science, Machine learning creating tremendous value in the education sector. Combination of open source with IBM value adds create compelling value. Artificial intelligence will revolutionize the sector with making education more relevant with Cognitive capabilities of students.
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Big Data in Education Sector
- 1. IBM Institute for Business Value How higher education can remain relevant in today’s Digital world with Data Science and Machine Learning Karan Sachdeva Sales Leader, Big Data, Asia Pacific karan@sg.ibm.com Follow me at https://www.linkedin.com/in/karan20
- 2. IBM Institute for Business Value “Education is the most powerful weapon which you can use to change the world.” - Nelson Mandela
- 3. Challenges Use Cases The Solution Why IBM Call To Action Agenda
- 4. ©2016 IBM Corporation 9 March 20184 Education ML and Data Science Use Cases Data-driven technologies can enable personalized education and improve outcomes for stakeholders across the skills ecosystem Real time virtual assistants can be used by teachers to evaluate learning styles and needs and help develop personalized learning plans tailored to individual students Anonymized skills data is shared between skills ecosystem partners to inform policies, programs and curriculum changes Career advisor systems are available to advise individuals which opportunities they should pursue based on their lifelong record of skills and interests Source: Source: Cave, R., Foden, N., King, M., and Stent, M. Personalised education: From curriculum to career with cognitive systems. IBM Corporation. April 2016.
- 5. ©2016 IBM Corporation 9 March 20185 Education Institution And Industry Gap is Widening Mean Perspective spectrum Source: IBM Institute for Business Value 2016 Global Skills Study
- 6. IBM Institute for Business Value Millennials comprise the bulk of students, and are demanding deeper, richer experiences Baby Boomers want SPEED Generation X wants great SERVICE Millennials want- RELEVENCE+ SERVICE +SPEED Organizational change Student experience gap Time Change Technology change
- 7. IBM Institute for Business Value Technology is disrupting and driving new requirements and opportunities by… Increasing competition through greater options Forcing rapid changes to curriculum to remain relevant Creating new opportunities for enhancing the learning experience Rapidly changing student expectations
- 8. ©2016 IBM Corporation 9 March 20188 Disrupt the Disruptor! Become a Data Driven Education Provider with IBM Governed Data Lake
- 9. IBM Institute for Business Value IBM Data Science and Machine learning can help Improve the Student Experience Promote Personalized Education Provides greater access to classes, curricula, and other educational content Deepen Student experience Integrates physical and digital worlds for more engaging experiences Expand Industry alignment Enables pursuit of educational experiences beyond traditional higher education institutions Improve Career outcomes Enables improved decision making by focusing on patterns that improve student success
- 10. Clearly Articulate Use Case Gather all the Data Apply Machine Learning Prepare Data Digital Application Evaluate How does it all work with IBM Governed Data Lake and Data Science
- 11. IBM Governed Data Lake as Ladder to your success AI Data The “AI Ladder” Governanc e Machine Learning Data Science • Fit for purpose Persistence • Common SQL Engine using IBM BigSQL • Integration and Governance for Big Data, Data Lakes, and Hadoop. • Shop for Data using IBM Information Governance Catalog • Help deliver unknown useful insights from data assets • Accelerate ML and Data Science projects using IBM Data Science Experience • IBM Governed Data Lake is end to end “Value driven” Big Data Platform journey • IBM Governed Data Lake simplifies every step on the “AI Ladder” by delivering end to end cohesive Big Data Stack • The “AI Ladder” is an evolutionary process with various starting points
- 12. Built-in learning to get started or go the distance with advanced tutorials Learn The best of open source and IBM value-add to create state-of-the-art data products Create Community and social features that provide meaningful collaboration Collaborate http://datascience.ibm.com Introducing- IBM Data Science Experience • Find tutorials and datasets • Connect with Data Scientists • Ask questions • Read articles and papers • Fork and share projects • Watson Machine Learning • SPSS Modeler Canvas • Advanced Visualizations • Projects and Version Control • Managed Spark Service • Code in Scala/Python/R • Jupyter Notebooks • RStudio IDE and Shiny • Apache Spark • Your favorite libraries
- 13. Open source is a powerful engine, but as with any engine, it needs the full system to accomplish any work Security – SSO and code hardening to reduce security gaps Version Currency – We keep up-to-date as open source quickly iterates Data Connectivity – Connect to data sources Scalability – Makes tools designed for desktops scalable to enterprise workloads We provide:
- 14. IBM Data Science Experience capabilities overview Predictive Power 100% Capacity Model Builder (CADS) Build model1 Deploy model2 Refresh model3 Import Sources: DSx Notebooks DSx Flow UI External tools Auto-generate model from input data, testing various algorithms for best fit (e.g. CADS) Detect loss of predictive power and refresh model, subject to preferences Deploy model into production - scale, manage and monitor Model Automation Model Deployment Model
- 15. The full range of Watson Cognitive services through APIs you can access through DSX Alchemy Language Conversa- tion Dialog Document Conversion Language Translator Natural Language Classifier Natural Language Under- standing Personality Insights Retrieve and Rank Tone Analyzer Speech to Text Visual Recognition Text to Speech Alchemy- Data News Discovery Discovery News Tradeoff Analytics Speech Vision Data Insights Language
- 16. We’ve been recognized for our vision Source: https://www.gartner.com/doc/reprints?id=1-3TKD8OH&ct=170215&st=sb http://www.developerweek.com/awards/2017-devies-award-winners/ Gartner Magic Quadrant 2017 Data Science Platforms DeveloperWeek 2017 Devie Forrester Wave 2017 Predictive Analytics & Machine Learning
- 17. •Eliminate inefficient islands of data repositories with IBM Common SQL Engine Capabilities. •Simplify management and reduce costs with open standard toolsBig Data •Enable better information sharing with first ever “Shop for Data” Capabilities. •.Massively scalable, shared-nothing, in-memory data integration and cleansing engine running natively in a Hadoop cluster to help bring enterprise robust capabilities to the "data lake Governance •Accelerate data analytics to gain new insight with Industry leader Data Science and Machine learning tool from ML Model development to deployment. •Leverage built-in cognitive capabilities powered by machine learning models, natural language processing and next-generation APIs to unlock hidden value in all your data Machine Learning and Data Science IBM Governed Data Lake Advantages
- 18. Explore Several Education Case Studies- www.ibmbigdatahub.com University of South Carolina (USC) SchoolCity, a California-based education start up Michigan State University
- 19. Next Step – IBM Governed Data Lake and Data Science Quick Start Solution 1. Receive best practices for your organizations to get started with governed data lake 2. Achieve faster time- to-value with pre-built accelerators 3. Leverage world-class data scientists and engineers with proven results at most mature big data and Data Science customers 12 Nodes of best in class Open Source Hadoop- IBM Hortonworks Data Platform 5 Users for IBM Data Science Experience 1 weeks of partner services engagement _________ = 75K USD* *Commercials for services from third party Partner Services *Commercials focused on Data Science environment for Governed Data Lake *Commercials may vary from local conditions and countries to countries *Offer valid till 31st March 2018 + +
- 20. Reach me at – karan@sg.ibm.com Read more at- www.ibmbigdatahub.com
Editor's Notes
- 1
- Nelson Mandela, former president of South Africa, 1993 Nobel Peace Prize laureate. Occasion: Launch of Mindset Network Place: Planetarium, University of the Witwatersrand Johannesburg South Africa Date: Wednesday, July 16, 2003 Source: http://db.nelsonmandela.org/speeches/pub_view.asp?pg=item&ItemID=NMS909
- Source: Cave, R., Foden, N., King, M., and Stent, M. Personalised education: From curriculum to career with cognitive systems. IBM Corporation. April 2016. http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=EDW03008GBEN
- … perspectives of respondents varied widely across these four major roles surveyed. A correlation analysis was run to evaluate the variance between responses from executives from the four roles surveyed in the study. The analysis revealed a significant variance between the responses from Labor / Workforce Policy executives and Industry executives. Workforce Development and Education executives were closely aligned to one another and more greatly aligned with Industry than their Labor / Workforce Policy peers.
- Sources: Martec’s Law - http://chiefmartec.com/2013/06/martecs-law-technology-changes-exponentially-organizations-change-logarithmically/ IBV Millennial survey: How do you prefer to obtain network-related knowledge and skills? (Millennials n=1,153, Gen X n=353, Baby Boomers n=278)
- Technology is disrupting and driving new requirements and opportunities for higher education… Increased competition through greater options Rapid emergence of blended learning models and online platforms Universities are no longer just offering the traditional brick and mortar lectures, many courses can be accessed online as well as the classroom New entrants into the higher education industry such as massive online open courses (MOOCs) deliver a greater variety in learning content to core customers online e.g.: edX, Coursera, Udacity Forcing rapid changes to curriculum to remain relevant Technological advances (e.g., 3D printing, advanced robotics, cloud, mobile, etc.) are disrupting current business needs and skills demanded Curricula must keep pace with technology changes to meet the changing demands of industry and the skills required Rapidly changing expectations from students Students have new expectations on how they interact with content and they expect educational content to be more easily accessible and engaging Students hold higher expectations of higher education today than ever before. Curricula being taught is expected to be relevant, applicable, and help build relationships Variety in curricula is imperative for students today Creating new opportunities for enhancing learning experiences Increasing number of innovative alternatives to traditional higher education models are emerging with greater emphasis on skill development Delivery methods of lesson plans are to be more engaging and interactive with the use of new technology Ability of technology to improve practice simulations making them much more realistic than before (see Center for Advanced Medical Learning and Simulation [CAMLS] case study)
- … perspectives of respondents varied widely across these four major roles surveyed. A correlation analysis was run to evaluate the variance between responses from executives from the four roles surveyed in the study. The analysis revealed a significant variance between the responses from Labor / Workforce Policy executives and Industry executives. Workforce Development and Education executives were closely aligned to one another and more greatly aligned with Industry than their Labor / Workforce Policy peers.
- Source: Q16 “Where do you see the greatest opportunity for technology to impact higher education?” n = 883 Higher education should embrace technology to improve education access, experience, and variety Industry and academic leaders identify the four most beneficial uses of new technology in higher education as: Promoting education access: provide greater access to classes, curricula, and other educational content Deepening education experience: integrate physical and digital worlds for a more compelling and engaging educational experience Expanding education variety: enable pursuit of educational experiences beyond traditional higher education institutions Improving outcomes: enable improved decision making by focusing on patters that demonstrably improve student success 62% of academics believe the benefits of technology outweigh the adoption costs of technology when improving the technology resources available to students and faculty. Q18 “For each of the following areas, how would you evaluate the relevant cost and benefit of each?” n = 603
- Moving your company along the journey to becoming a digital business takes a combination of elements. Some of them are beyond this presentation – because they take true commitment from everyone; the executives down to the employees. However, once the decision has been made to begin the journey, the elements needed include; ideas, data, analytics, skills, and the tools to get you there. At IBM we can help with both the infrastructure, skills and processes to help guide you on this journey. In Summary – these are the areas to consider and a high level summary of the steps most organizations follow to get through their first analytics project. START WITH A REAL PROBLEM TO SOLVE - Focus on solving a real problem. This helps to ensure your strategy aligns with your business and data, provides real ROI to justify your expenses, and gives your company a win. DETERMINE DATA YOU NEED TO ANSWER YOUR QUESTIONS – Don’t limit yourself to just using internal data or structured data – think about how you could incorporate behavioral data from external sources. Think about sensor data, video, and audio and if they would help with your analytics. For example, many companies are using mobile phone as sensors to determine location and preferences of their consumers. As you start working with the data during the analytics stage, you will most likely return to this stage to either obtain more data as you continue to refine your analysis. SELECT THE "BEST” TECHNIQUES TO GENERATE VALID, RELIABLE FINDINGS - There are many techniques used to conduct analytics. Typically one chooses the technique or techniques likely to yield results for the kinds of predictions one wants to make, and then looks for the best tools to achieve the intended goals and outcomes. Selecting the most appropriate techniques for analytics has a lot to do with knowing the questions that the analysis will help answer, or the performance problems to be solved. Don’t force your research design to fit the platform; select the platform you need for achieving the goals you want to achieve. PLAN TO SPEND TIME CLEANING YOUR DATA – As mentioned during the Gather all the Data stage, you will most likely find the data contains quality issues or outliers you’d prefer to exclude from your analysis. Additionally you might find working with aggregates or transforming the data may work well with what you’re trying to accomplish. BE PREPARED TO ACT ON YOUR FINDINGS - Simply knowing the outcome isn’t enough. Your predictions must be actionable. You need to find students at risk, mitigate risks and discover how different students can be better served with targeted interventions and supports. Analytics without action don't really matter very much to anyone. Depending on the type of analytics you’re performing, deploy the outcome into either an application, report, or process.
- Hortonworks Hadoop customers need large scale data ingest, transformation, cleansing, monitoring, and business level cataloging.
- Now let’s dig into the IBM Data Science Experience (DSX). DSX is a single place to Learn, Create, and Collaborate – the 3 pillars of a successful data science organization. With DSX we are making it easier for data scientists and data science teams to learn new skills and technologies from the fast moving data science profession. Users can join the 400,000+ registered users on Big Data University to take courses and administer custom curriculum on Data Science. DSX also provides freely available datasets, code samples, tutorials and articles from the data science community, thereby making it easier for data scientists to learn in-context. We also know that a data science team isn’t going to standardize on a single technology or tool – data scientists need an open and integrated platform that brings all of their tools in one place. DSX brings together the best of open source such as RStudio, Spark, and Python in an integrated environment along with IBM value-adds, such as a managed Spark service and data shaping capabilities all in a secure and governed way. Users can collaborate in DSX with ease -- you can manage project assets and user collaboration through built-in social features for sharing, forking, and reusing assets. These built-in collaboration features can really help boost team productivity and managing projects across their lifecycle.