Big Data Business Intelligence for Govt. Agencies Training...

Big Data Business Intelligence for Govt. Agencies Training Course

Description

Overview

Advances in technologies and the increasing amount of information are transforming how business is conducted in many industries, including government. Government data generation and digital archiving rates are on the rise due to the rapid growth of mobile devices and applications, smart sensors and devices, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, information management, processing, storage, security, and disposition become more complex as well. New capture, search, discovery, and analysis tools are helping organizations gain insights from their unstructured data. The government market is at a tipping point, realizing that information is a strategic asset, and government needs to protect, leverage, and analyze both structured and unstructured information to better serve and meet mission requirements. As government leaders strive to evolve data-driven organizations to successfully accomplish mission, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will be created from a mashup of the most disruptive technologies:

  • Mobile devices and applications
  • Cloud services
  • Social business technologies and networking
  • Big Data and analytics

Price per participant:   599347193 VND

Workday courses take place between 09:30 and 16:30

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
  • Trainer 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 Trainers 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)

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