Course Outline

Each session is 2 hours

Day-1: Session -1: Business Overview of Why Big Data Business Intelligence in Govt.

  • Case Studies from NIH, DoE
  • Big Data adaptation rate in Govt. Agencies & and how they are aligning their future operation around Big Data Predictive Analytics
  • Broad Scale Application Area in DoD, NSA, IRS, USDA etc.
  • Interfacing Big Data with Legacy data
  • Basic understanding of enabling technologies in predictive analytics
  • Data Integration & Dashboard visualization
  • Fraud management
  • Business Rule/ Fraud detection generation
  • Threat detection and profiling
  • Cost benefit analysis for Big Data implementation

Day-1: Session-2 : Introduction of Big Data-1

  • Main characteristics of Big Data-volume, variety, velocity and veracity. MPP architecture for volume.
  • Data Warehouses – static schema, slowly evolving dataset
  • MPP Databases like Greenplum, Exadata, Teradata, Netezza, Vertica etc.
  • Hadoop Based Solutions – no conditions on structure of dataset.
  • Typical pattern : HDFS, MapReduce (crunch), retrieve from HDFS
  • Batch- suited for analytical/non-interactive
  • Volume : CEP streaming data
  • Typical choices – CEP products (e.g. Infostreams, Apama, MarkLogic etc)
  • Less production ready – Storm/S4
  • NoSQL Databases – (columnar and key-value): Best suited as analytical adjunct to data warehouse/database

Day-1 : Session -3 : Introduction to Big Data-2

NoSQL solutions

  • KV Store - Keyspace, Flare, SchemaFree, RAMCloud, Oracle NoSQL Database (OnDB)
  • KV Store - Dynamo, Voldemort, Dynomite, SubRecord, Mo8onDb, DovetailDB
  • KV Store (Hierarchical) - GT.m, Cache
  • KV Store (Ordered) - TokyoTyrant, Lightcloud, NMDB, Luxio, MemcacheDB, Actord
  • KV Cache - Memcached, Repcached, Coherence, Infinispan, EXtremeScale, JBossCache, Velocity, Terracoqua
  • Tuple Store - Gigaspaces, Coord, Apache River
  • Object Database - ZopeDB, DB40, Shoal
  • Document Store - CouchDB, Cloudant, Couchbase, MongoDB, Jackrabbit, XML-Databases, ThruDB, CloudKit, Prsevere, Riak-Basho, Scalaris
  • Wide Columnar Store - BigTable, HBase, Apache Cassandra, Hypertable, KAI, OpenNeptune, Qbase, KDI

Varieties of Data: Introduction to Data Cleaning issue in Big Data

  • RDBMS – static structure/schema, doesn’t promote agile, exploratory environment.
  • NoSQL – semi structured, enough structure to store data without exact schema before storing data
  • Data cleaning issues

Day-1 : Session-4 : Big Data Introduction-3 : Hadoop

  • When to select Hadoop?
  • STRUCTURED - Enterprise data warehouses/databases can store massive data (at a cost) but impose structure (not good for active exploration)
  • SEMI STRUCTURED data – tough to do with traditional solutions (DW/DB)
  • Warehousing data = HUGE effort and static even after implementation
  • For variety & volume of data, crunched on commodity hardware – HADOOP
  • Commodity H/W needed to create a Hadoop Cluster

Introduction to Map Reduce /HDFS

  • MapReduce – distribute computing over multiple servers
  • HDFS – make data available locally for the computing process (with redundancy)
  • Data – can be unstructured/schema-less (unlike RDBMS)
  • Developer responsibility to make sense of data
  • Programming MapReduce = working with Java (pros/cons), manually loading data into HDFS

Day-2: Session-1: Big Data Ecosystem-Building Big Data ETL: universe of Big Data Tools-which one to use and when?

  • Hadoop vs. Other NoSQL solutions
  • For interactive, random access to data
  • Hbase (column oriented database) on top of Hadoop
  • Random access to data but restrictions imposed (max 1 PB)
  • Not good for ad-hoc analytics, good for logging, counting, time-series
  • Sqoop - Import from databases to Hive or HDFS (JDBC/ODBC access)
  • Flume – Stream data (e.g. log data) into HDFS

Day-2: Session-2: Big Data Management System

  • Moving parts, compute nodes start/fail :ZooKeeper - For configuration/coordination/naming services
  • Complex pipeline/workflow: Oozie – manage workflow, dependencies, daisy chain
  • Deploy, configure, cluster management, upgrade etc (sys admin) :Ambari
  • In Cloud : Whirr

Day-2: Session-3: Predictive analytics in Business Intelligence -1: Fundamental Techniques & Machine learning based BI :

  • Introduction to Machine learning
  • Learning classification techniques
  • Bayesian Prediction-preparing training file
  • Support Vector Machine
  • KNN p-Tree Algebra & vertical mining
  • Neural Network
  • Big Data large variable problem -Random forest (RF)
  • Big Data Automation problem – Multi-model ensemble RF
  • Automation through Soft10-M
  • Text analytic tool-Treeminer
  • Agile learning
  • Agent based learning
  • Distributed learning
  • Introduction to Open source Tools for predictive analytics : R, Rapidminer, Mahut

Day-2: Session-4 Predictive analytics eco-system-2: Common predictive analytic problems in Govt.

  • Insight analytic
  • Visualization analytic
  • Structured predictive analytic
  • Unstructured predictive analytic
  • Threat/fraudstar/vendor profiling
  • Recommendation Engine
  • Pattern detection
  • Rule/Scenario discovery –failure, fraud, optimization
  • Root cause discovery
  • Sentiment analysis
  • CRM analytic
  • Network analytic
  • Text Analytics
  • Technology assisted review
  • Fraud analytic
  • Real Time Analytic

Day-3 : Sesion-1 : Real Time and Scalable Analytic Over Hadoop

  • Why common analytic algorithms fail in Hadoop/HDFS
  • Apache Hama- for Bulk Synchronous distributed computing
  • Apache SPARK- for cluster computing for real time analytic
  • CMU Graphics Lab2- Graph based asynchronous approach to distributed computing
  • KNN p-Algebra based approach from Treeminer for reduced hardware cost of operation

Day-3: Session-2: Tools for eDiscovery and Forensics

  • eDiscovery over Big Data vs. Legacy data – a comparison of cost and performance
  • Predictive coding and technology assisted review (TAR)
  • Live demo of a Tar product ( vMiner) to understand how TAR works for faster discovery
  • Faster indexing through HDFS –velocity of data
  • NLP or Natural Language processing –various techniques and open source products
  • eDiscovery in foreign languages-technology for foreign language processing

Day-3 : Session 3: Big Data BI for Cyber Security –Understanding whole 360 degree views of speedy data collection to threat identification

  • Understanding basics of security analytics-attack surface, security misconfiguration, host defenses
  • Network infrastructure/ Large datapipe / Response ETL for real time analytic
  • Prescriptive vs predictive – Fixed rule based vs auto-discovery of threat rules from Meta data

Day-3: Session 4: Big Data in USDA : Application in Agriculture

  • Introduction to IoT ( Internet of Things) for agriculture-sensor based Big Data and control
  • Introduction to Satellite imaging and its application in agriculture
  • Integrating sensor and image data for fertility of soil, cultivation recommendation and forecasting
  • Agriculture insurance and Big Data
  • Crop Loss forecasting

Day-4 : Session-1: Fraud prevention BI from Big Data in Govt-Fraud analytic:

  • Basic classification of Fraud analytics- rule based vs predictive analytics
  • Supervised vs unsupervised Machine learning for Fraud pattern detection
  • Vendor fraud/over charging for projects
  • Medicare and Medicaid fraud- fraud detection techniques for claim processing
  • Travel reimbursement frauds
  • IRS refund frauds
  • Case studies and live demo will be given wherever data is available.

Day-4 : Session-2: Social Media Analytic- Intelligence gathering and analysis

  • Big Data ETL API for extracting social media data
  • Text, image, meta data and video
  • Sentiment analysis from social media feed
  • Contextual and non-contextual filtering of social media feed
  • Social Media Dashboard to integrate diverse social media
  • Automated profiling of social media profile
  • Live demo of each analytic will be given through Treeminer Tool.

Day-4 : Session-3: Big Data Analytic in image processing and video feeds

  • Image Storage techniques in Big Data- Storage solution for data exceeding petabytes
  • LTFS and LTO
  • GPFS-LTFS ( Layered storage solution for Big image data)
  • Fundamental of image analytics
  • Object recognition
  • Image segmentation
  • Motion tracking
  • 3-D image reconstruction

Day-4: Session-4: Big Data applications in NIH:

  • Emerging areas of Bio-informatics
  • Meta-genomics and Big Data mining issues
  • Big Data Predictive analytic for Pharmacogenomics, Metabolomics and Proteomics
  • Big Data in downstream Genomics process
  • Application of Big data predictive analytics in Public health

Big Data Dashboard for quick accessibility of diverse data and display :

  • Integration of existing application platform with Big Data Dashboard
  • Big Data management
  • Case Study of Big Data Dashboard: Tableau and Pentaho
  • Use Big Data app to push location based services in Govt.
  • Tracking system and management

Day-5 : Session-1: How to justify Big Data BI implementation within an organization:

  • Defining ROI for Big Data implementation
  • Case studies for saving Analyst Time for collection and preparation of Data –increase in productivity gain
  • Case studies of revenue gain from saving the licensed database cost
  • Revenue gain from location based services
  • Saving from fraud prevention
  • An integrated spreadsheet approach to calculate approx. expense vs. Revenue gain/savings from Big Data implementation.

Day-5 : Session-2: Step by Step procedure to replace legacy data system to Big Data System:

  • Understanding practical Big Data Migration Roadmap
  • What are the important information needed before architecting a Big Data implementation
  • What are the different ways of calculating volume, velocity, variety and veracity of data
  • How to estimate data growth
  • Case studies

Day-5: Session 4: Review of Big Data Vendors and review of their products. Q/A session:

  • Accenture
  • APTEAN (Formerly CDC Software)
  • Cisco Systems
  • Cloudera
  • Dell
  • EMC
  • GoodData Corporation
  • Guavus
  • Hitachi Data Systems
  • Hortonworks
  • HP
  • IBM
  • Informatica
  • Intel
  • Jaspersoft
  • Microsoft
  • MongoDB (Formerly 10Gen)
  • MU Sigma
  • Netapp
  • Opera Solutions
  • Oracle
  • Pentaho
  • Platfora
  • Qliktech
  • Quantum
  • Rackspace
  • Revolution Analytics
  • Salesforce
  • SAP
  • SAS Institute
  • Sisense
  • Software AG/Terracotta
  • Soft10 Automation
  • Splunk
  • Sqrrl
  • Supermicro
  • Tableau Software
  • Teradata
  • Think Big Analytics
  • Tidemark Systems
  • Treeminer
  • VMware (Part of EMC)

Requirements

  • Basic knowledge of business operation and data systems in Govt. in their domain
  • Basic understanding of SQL/Oracle or relational database
  • Basic understanding of Statistics (at Spreadsheet level) 
 35 Hours

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