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Course Outline
Each session is 2 hours
Day-1: Session -1: Business Overview of Why Big Data Business Intelligence for Government
- Case Studies from NIH, DoE
- Big Data adaptation rate in Government Agencies and how they are aligning their future operations 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 to 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 the structure of the 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 an analytical adjunct to data warehouses/databases
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 Issues in Big Data
- RDBMS – static structure/schema, doesn’t promote an agile, exploratory environment.
- NoSQL – semi-structured, enough structure to store data without an 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 MapReduce /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 Ecosystem -2: Common Predictive Analytic Problems in Government
- 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: Session-1: Real-Time and Scalable Analytic Over Hadoop
- Why common analytic algorithms fail in Hadoop/HDFS
- Apache Hama—Bulk Synchronous distributed computing
- Apache SPARK—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 metadata
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 soil fertility, cultivation recommendation, and forecasting
- Agriculture insurance and Big Data
- Crop loss forecasting
Day-4: Session-1: Fraud Prevention BI from Big Data in Government—Fraud Analytics:
- Basic classification of fraud analytics—rule-based vs. predictive analytics
- Supervised vs. unsupervised machine learning for fraud pattern detection
- Vendor fraud/overcharging for projects
- Medicare and Medicaid fraud—fraud detection techniques for claim processing
- Travel reimbursement frauds
- IRS refund frauds
- Case studies and live demos 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, metadata, 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 profiles
- 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 solutions for data exceeding petabytes
- LTFS and LTO
- GPFS-LTFS (Layered storage solution for big image data)
- Fundamentals 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 Bioinformatics
- Meta-genomics and Big Data mining issues
- Big Data Predictive analytics for Pharmacogenomics, Metabolomics, and Proteomics
- Big Data in downstream Genomics processes
- Application of Big Data predictive analytics in Public health
Big Data Dashboard for Quick Accessibility of Diverse Data and Display:
- Integration of existing application platforms with Big Data Dashboard
- Big Data management
- Case study of Big Data Dashboard: Tableau and Pentaho
- Use Big Data apps to push location-based services in government
- 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 licensed database costs
- Revenue gain from location-based services
- Savings from fraud prevention
- An integrated spreadsheet approach to calculate approximate expense vs. revenue gain/savings from Big Data implementation.
Day-5: Session-2: Step-by-Step Procedure to Replace Legacy Data System with a Big Data System:
- Understanding practical Big Data Migration Roadmap
- Important information needed before architecting a Big Data implementation
- 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 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
- Fundamental knowledge of business operations and data systems for government within their domain
- Basic understanding of SQL/Oracle or relational databases
- Basic understanding of statistics (at the spreadsheet level)
35 Hours
Testimonials (1)
The ability of the trainer to align the course with the requirements of the organization other than just providing the course for the sake of delivering it.
